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Taye, Mohammad Mustafa. "Enhanced Agile Methodology for Ontology Development in E-Learning Environments." International Journal of Interactive Mobile Technologies (iJIM) 18, no. 16 (2024): 4–24. http://dx.doi.org/10.3991/ijim.v18i16.49225.
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This study explores the use of agile approaches to the creation of ontologies for e-learning, evaluating the benefits and drawbacks as well as the impact on information display. Traditional strategies conflict with the need to fulfill the ever-evolving expectations of users and adapt to the ever-changing features of e-learning environments. The challenge aims to encourage cooperation and versatility in the creation of ontologies for e-learning through the use of Agile standards. Because they make it simpler to organize relationships and statistics, ontologies are vital elements in e-mastering domain names due to the fact that they permit adaptive knowledge of structures and individualized learning experiences. Agile ontology engineering approaches are proposed as a choice for one’s problems, emphasizing flexibility and response. This study highlights the need to work together with customers and incorporate their input into the advent of ontologies. It notably emphasizes using established feedback loops and cooperation with e-learning platform companies. The sensible usefulness and effectiveness of agile methodology for ontology development (AMOD) in e-learning settings are shown through validation efforts in real-global conditions.
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Gil, Richard, and Maria J. Martin-Bautista. "SMOL: a systemic methodology for ontology learning from heterogeneous sources." Journal of Intelligent Information Systems 42, no. 3 (2014): 415–55. http://dx.doi.org/10.1007/s10844-013-0296-x.
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El Idrissi Esserhrouchni, Omar, Bouchra Frikh, Brahim Ouhbi, and Ismail Khalil Ibrahim. "Learning domain taxonomies: the TaxoLine approach." International Journal of Web Information Systems 13, no. 3 (2017): 281–301. http://dx.doi.org/10.1108/ijwis-04-2017-0024.
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Purpose The aim of this paper is to present an online framework for building a domain taxonomy, called TaxoLine, from Web documents automatically. Design/methodology/approach TaxoLine proposes an innovative methodology that combines frequency and conditional mutual information to improve the quality of the domain taxonomy. The system also includes a set of mechanisms that improve the execution time needed to build the ontology. Findings The performance of the TaxoLine framework was applied to nine different financial corpora. The generated taxonomies are evaluated against a gold-standard ontology and are compared to state-of-the-art ontology learning methods. Originality/value The experimental results show that TaxoLine produces high precision and recall for both concept and relation extraction than well-known ontology learning algorithms. Furthermore, it also shows promising results in terms of execution time needed to build the domain taxonomy.
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Valaski, Joselaine, Sheila Reinehr, and Andreia Malucelli. "An ontology to support the classification of learning material in an organizational learning environment." Interactive Technology and Smart Education 14, no. 1 (2017): 67–87. http://dx.doi.org/10.1108/itse-11-2016-0044.
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Purpose The purpose of this research was to evaluate whether ontology integrated in an organizational learning environment may support the automatic learning material classification in a specific knowledge area. Design/methodology/approach An ontology for recommending learning material was integrated in the organizational learning environment based on ontology. An experiment was performed with 15 experts and 84 learners. Experts and learners were asked to classify 30 learning material related to Software Engineering area. The results obtained from experts and learners were compared with the ontology results. Findings Among 30 learning materials evaluated, 24 learning materials got closer to the expert classification using the ontology than using the learners’ manual classification. The learners had difficulties in correctly classifying the learning materials according to the knowledge area applied. Originality/value In an autonomous collaborative environment without a tutor responsible for organizing the learning materials shared by collaborators, an ontology may be an auxiliary mechanism to support automatic learning material classification. The proposed ontology uses recommendations given by the collaborators to get the correct knowledge area classification. The correct classification may support retrieval of appropriate learning materials according to the learners’ needs.
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Sathiya, B., and T. V. Geetha. "Automatic Ontology Learning from Multiple Knowledge Sources of Text." International Journal of Intelligent Information Technologies 14, no. 2 (2018): 1–21. http://dx.doi.org/10.4018/ijiit.2018040101.
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The prime textual sources used for ontology learning are a domain corpus and dynamic large text from web pages. The first source is limited and possibly outdated, while the second is uncertain. To overcome these shortcomings, a novel ontology learning methodology is proposed to utilize the different sources of text such as a corpus, web pages and the massive probabilistic knowledge base, Probase, for an effective automated construction of ontology. Specifically, to discover taxonomical relations among the concept of the ontology, a new web page based two-level semantic query formation methodology using the lexical syntactic patterns (LSP) and a novel scoring measure: Fitness built on Probase are proposed. Also, a syntactic and statistical measure called COS (Co-occurrence Strength) scoring, and Domain and Range-NTRD (Non-Taxonomical Relation Discovery) algorithms are proposed to accurately identify non-taxonomical relations(NTR) among concepts, using evidence from the corpus and web pages.
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Rahmani, Mahdi, and Beheshti Moluksadat Hosseini. "Designing an E-Learning System Based on Ontology." Journal of Information Processing and Management 36, no. 1 (2020): 271–94. https://doi.org/10.5281/zenodo.14039589.
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The aim of the present research is to design an e-learning system based on ontology. This study is applied in nature and is classified as descriptive research. In this research, the main concepts in the field of e-learning were extracted by reviewing published articles and information resources related to e-learning. Ultimately, the main categories in the field of e-learning were identified, including human resources, technological infrastructure, and educational content. The methodology used for extracting concepts and semantic relationships was based on knowledge engineering, while the method employed for creating the ontology was a top-down approach. Additionally, the software "Protégé" version 5.3 was used to construct the ontology. The research followed seven stages for ontology construction, which included determining the scope and coverage of the ontology, establishing the hierarchy of the ontology, identifying conceptual pairs, defining categories, describing features, defining instances, and creating examples. Based on the research findings, the proposed method can be utilized to complete and develop the suggested ontology in the field of e-learning.
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Ekwealor, Oluchukwu, Chidi Betrand, Chiemeka Chukwudum, Charles Uchefuna, and Obinna Agbata. "Development of a Semantic Web-Ontology E-Learning Platform." American Journal of Computer Science and Technology 7, no. 4 (2024): 176–82. http://dx.doi.org/10.11648/j.ajcst.20240704.15.
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This paper is focused on developing A Semantic Web-Ontology E-Learning Platform, which is a system that combines semantic web and ontology technology to guarantees a sophisticated learning environment that provides the learners with adaptable and customized learning resources based on learners’ knowledge requirement. With this system, learners can log in from their comfort zone anytime, to receive their online lesson as provided by their tutor. The system has an added advantage of providing a personalized learning to students through creation of intelligent search engine and ontology backbone consisting of learning data and their meta data. The learner, through this search engine, searches the ontology semantically for the learning materials that suits his/her profile. The system also has the capability of filtering the search results by matching them with the profile of a particular learner using inference engine, such that the result best suited for the user’s academic need is presented. This work will not only promote self-directed learning but will also facilitate quick search of learning materials, by narrowing the search based on specified learner’s interest. The methodology adopted for this work is Object-Oriented Analysis and Design Methodology (OOADM) and programing languages used are Php-Mysql and Java Script. The system will be of great benefit to schools, other learning institutions and organization seeking to educate their manpower.
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Zhou, Yang, Zhao Yang Zeng, Bin Tian, Zhi Yu Jia, and Xiao Guo. "Ontology Modeling of Aircraft Fault Knowledge." Applied Mechanics and Materials 236-237 (November 2012): 350–55. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.350.
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With consideration of the actual requirement of fault knowledge management in current aviation maintenance, ontology-based knowledge representation method of aircraft fault is studied. Knowledge sources of aircraft fault ontology are analyzed based on ontology modeling primitives. Ontology construction method of aircraft fault knowledge is proposed learning from software engineering and the essence of the existing ontology construction methodology. On the basis of the above study, aircraft fault ontology which is consist of Fault Core sub-ontology, Product domain sub-ontology, Case domain sub-ontology, and Diagnosis domain sub-ontology is build, and it represents aircraft fault knowledge completely and consistently, and also lays the foundation for knowledge management of aircraft fault.
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Jacksi, Karwan. "Design and Implementation of E-Campus Ontology with a Hybrid Software Engineering Methodology." Science Journal of University of Zakho 7, no. 3 (2019): 95–100. http://dx.doi.org/10.25271/sjuoz.2019.7.3.613.
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Semantic Web according to the vision of the W3C is the future of WWW (or Web). It is an extension of the current Web through standards by the W3C. Data of the Semantic Web has well-defined meanings, can be understood by devices and allows machines and people to work in collaboration. Ontologies are vital components of the Semantic Web infrastructure and are more often recognized as the backbone of the Semantic Web. Although numerous developments occur in the field of developing ontologies along the lines with the Semantic Web implementation, but standardizing the process models, tools and methodologies need to be improved in the future. In literature, experts in ontology engineering have stated that setting a methodology for developing ontology applications with support of integrated tools is an essential task for ontology engineering to be succeeded. In this paper, an e-campus ontology for educational purposes is designed and implemented, and mainly focused on the learning hierarchy of C-sharp programming language. A hybrid methodology based on software engineering approaches for developing ontologies is presented. Finally, the developed methodology is applied on the implemented ontology.
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Kero, ADMAS, Dawit Demissie, and Kula Kekeba. "Leveraging Ontology-Driven Machine Learning for Public Policy Analysis: A Systematic Review of Social Media Applications." IJID (International Journal on Informatics for Development) 13, no. 2 (2024): 485–503. https://doi.org/10.14421/ijid.2024.4176.
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As social media platforms increasingly serve, machine learning techniques are formulated with particular ontologies, which furnish invaluable resources. This qualitative literature review investigates the incorporation of ontology-driven machine learning methodologies for analysing public policy utilizing social media data. This review encompasses findings from scholarly research published between 2019 and 2024 that apply ontologies to enhance models' interpretation, precision, and flexibility across diverse sectors, including health, environment, economy, and culture. An integrated methodology is adopted to identify, select, and evaluate pertinent studies by scrutinizing elements such as genre ontology, machine learning, existing literature, and evaluation metrics. The findings indicate that the ontology-centric framework facilitates the extraction process and semantic analysis, ultimately contributing to a more nuanced comprehension of unstructured data. Nonetheless, obstacles persist in ontology development concerning capacity enhancement, data integrity, and ethical considerations. The review concludes with a discourse on the ramifications for policymakers and researchers who may leverage these insights to guide decision-making, and scholars are now urged to confront limitations and investigate novel platforms, metrics, and ethical frameworks. The review underscores the potential of ontology-driven machine learning as a formidable strategy in the advancement of policy research and social analysis.
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Mohd Zailani, Sara Afiqah, Nurul Aswa Omar, Aida Mustapha, and Mohd Hisyam Abdul Rahim. "Fasting Ontology in Pillars of Islam." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 562. http://dx.doi.org/10.11591/ijeecs.v12.i2.pp562-569.
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The development of Fasting Ontology in the Pillars of Islam is presented in this paper and has been built based on reliable sources of Islamic Knowledge. The METHONTOLOGY methodology is used for the ontology development, which include identifying motivation scenarios, creating the competency questions, implementation and evaluation. From the beginning of the development of life cycle, the ontology was appraised from the competency questions and the outcome were clear. Therefore, this ontology can link each concept specifically to the individual verse together with the Tafsir that is related to the topics. The ontology proposed will be part of a larger ontology on Five Pillars of Islam. This development of the ontology is intended to refer to the field of learning for other purpose. For instance, search engine, chatbot, expert system or knowledge-based system.
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Sara, Afiqah Mohd Zailani, Aswa Omar Nurul, Mustapha Aida, and Hisyam Abdul Rahim Mohd. "Fasting Ontology in Pillars of Islam." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 562–69. https://doi.org/10.11591/ijeecs.v12.i2.pp562-569.
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The development of Fasting Ontology in the Pillars of Islam is presented in this paper and has been built based on reliable sources of Islamic Knowledge. The METHONTOLOGY methodology is used for the ontology development, which include identifying motivation scenarios, creating the competency questions, implementation and evaluation. From the beginning of the development of life cycle, the ontology was appraised from the competency questions and the outcome were clear. Therefore, this ontology can link each concept specifically to the individual verse together with the Tafsir that is related to the topics. The ontology proposed will be part of a larger ontology on Five Pillars of Islam. This development of the ontology is intended to refer to the field of learning for other purpose. For instance, search engine, chatbot, expert system or knowledge-based system.
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Zulkipli, Zayanah Zafirah, Ruhaila Maskat, and Noor Hasimah Ibrahim Teo. "A systematic literature review of automatic ontology construction." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 2 (2022): 878–89. https://doi.org/10.11591/ijeecs.v28.i2.pp878-889.
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Ontologies have gotten a lot of interest as a knowledge representation approach in recent years. However, constructing an ontology manually can be a difficult task. The alternative way is to automate the ontology construction, either by performing a semi or fully-automatic approach. In this paper, we will conduct a systematic literature review that will focus on a comparative analysis of different techniques relating to both semi and fullyautomatic ontology construction using several techniques and an automated approach applied. The goal is to identify the distribution, methodology, automated part, evaluation method, main tools, and technologies used to construct the automatic ontology. This paper will review academic documents published in peer-reviewed venues from 2017 to 2021, based on a four-step selection process of identification, screening, eligibility, and inclusion for the selection process. To examine these documents, a systematic review was conducted and five main research questions were answered. The results indicate that automatic ontology construction could give higher complexity, shorter time, and reduce the role of the expert knowledge to evaluate ontology than manual ontology construction. Finally, we summarize the most commonly used methods in automatic ontology construction, which we believe will serve as a foundation for future multidisciplinary research.
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Syamili, C., and R. V. Rekha. "Developing an ontology for Greek mythology." Electronic Library 36, no. 1 (2018): 119–32. http://dx.doi.org/10.1108/el-02-2017-0030.
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Purpose The purpose of this study is to illustrate the development of ontology for the heroes of the ancient Greek mythology and religion. At present, a number of ontologies exist in different domains. However, ontologies of epics and myths are comparatively very few. To be more specific, nobody has developed such ontology for Greek mythology. This paper describes the attempts at developing ontology for Greek mythology to fill this gap. Design/methodology/approach This paper follows a combination of different methodologies, which is assumed to be a more effective way of developing ontology for mythology. It has adopted motivating scenario concept from Gruninger and Fox, developing cycle from Methontology and the analytico–synthetic approach from yet another methodology for ontology, and hence, it is a combination of three existing approaches. Findings A merged methodology has been adopted for this paper. The developed ontology was evaluated and made to meet with the information needs of its users. On the basis of the study, it was found that Greek mythology ontology could answer 62 per cent of the questions after first evaluation, i.e. 76 out of the 123 questions. The unanswered questions were analyzed in detail for further development of the ontology. The missing concepts were fed into the ontology; the ontology obtained after this stage was an exhaustive one. Practical implications This ontology will grow with time and can be used in semantic applications or e-learning modules related to the domain of Greek mythology. Originality/value This work is the first attempt to build ontology for Greek mythology. The approach is unique in that it has attempted to trace out the individual characteristics as well as the relationship between the characters described in the work.
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Форміка, Анна, and Алессія Барбагалло. "INTEGRATING SEMANTIC SEARCH IN E-LEARNING TECHNOLOGIES: THE ELSE SYSTEM." Information Technologies and Learning Tools 78, no. 4 (2020): 237–48. http://dx.doi.org/10.33407/itlt.v78i4.3527.
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The integration of semantic web methodologies and e-learning technologies is a challenge that has attracted a lot of attention for a decade. Given this, the purpose of this paper is the definition of a new e-learning semantic web methodology for the development of courses for health professionals in both distance and residential learning modes. ELSE is an ontology-based system which allows the construction of customized e-learning courses according to the needs and learning preferences of the user. It integrates semantic search methodologies and e-learning technologies. The underlying methodology relies on a reference domain ontology and teaching multimedial interactive modules, referred to as Reusable Learning Objects (RLOs), which are annotated according to the concepts of the ontology. The user can specify his/her training needs by selecting a set of concepts from the ontology, and the SemSim semantic search engine allows the identification of the set of RLOs that satisfy the user request at best, in efficient way. SemSim is a semantic similarity method which has been extensively experimented with and shows a higher correlation with human judgment with respect to the most relevant similarity methods defined in the literature. The set of RLOs is successively reorganized according to the learning preferences of the user. ELSE has been developed within a project of the CME (Continuing Medical Education) program - ECM for Italian participants - whose goal is the introduction of new methodologies and tools to keep updated health professionals and, in particular, medical specialists, in order to ensure effectiveness, safety, and efficiency of the national health service. ELSE has been tested and validated in the domain of osteoporosis, and the overall judgment about the system is very positive, both in terms of usability and effectiveness of customization. The system has been developed in cooperation with the ECM provider SPES S.c.p.A., accredited by the Italian Ministry of Health.
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Deng, Lawrence Y., Huan-Chao Keh, and Yi-Jen Liu. "Ontology-Based Multimedia Authoring Tool for Adaptive E-Learning." International Journal of Distance Education Technologies 8, no. 4 (2010): 42–65. http://dx.doi.org/10.4018/jdet.2010100104.
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More video streaming technologies supporting distance learning systems are becoming popular among distributed network environments. In this paper, the authors develop a multimedia authoring tool for adaptive e-learning by using characterization of extended media streaming technologies. The distributed approach is based on an ontology-based model. Suppose a well-known teacher is giving a lecture/presentation to his student. Because of time constraints and other commitments, many students cannot attend. The main goal of the authors’ system is to provide a feasible method to record and represent a lecture/presentation using a browser with the windows media services. This system requires flexible support for the modeling of multimedia content models and supports possible interactivity, transfer of streams multimedia data such as audio, video, text, and annotations using network facilities. The authors propose a new approach for the modeling of reusable and adaptable multimedia content. A comprehensive system for advanced multimedia content production is also developed. This approach significantly impacts and supports the multimedia presentation authoring processes in terms of methodology and commercial aspects.
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Rezgui, Kalthoum, Hédia Mhiri, and Khaled Ghédira. "Towards a common and semantic representation of e-portfolios." Data Technologies and Applications 52, no. 4 (2018): 520–38. http://dx.doi.org/10.1108/dta-01-2018-0008.
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Purpose Since the early 1980s, a paradigm shift, caused by the work undertaken in the field of cognitive psychology, has occurred. This shift is known as the move from teacher-centered instruction to learner-centered or learning-centered instruction, and emphasizes the importance of building new knowledge on previous ones, interacting with peers, making meaningful and reflective learning and being engaged in his own path to foster learning. This new vision of teaching has created a need for new learning and assessment instruments that are better adapted to these pedagogical realities. In this context, the electronic portfolio or e-portfolio is one of the most versatile and effective tools that have been proposed for this purpose. More specifically, the interest in e-portfolios has grown considerably with the emergence of the competency-based approach and portfolio-based competency assessments. The purpose of this paper is to describe a semantic-based representation of e-portfolios, defined on the basis of official e-portfolio standards and specifications. Moreover, a comparative study of several well-known e-portfolio solutions has been carried out based on different facets, such as functional features, technical and organizational features. The objective is to identify those features that are mostly supported by e-portfolio solution providers and accordingly to gain a fairly accurate idea of the common structure of e-portfolios. In addition, the authors take advantage of an already implemented ontological model describing competency-related characteristics of learners and learning objects and combine it with the e-portfolio ontology, with a view to support a more reliable and authentic competency assessment. Design/methodology/approach The proposed e-portfolio ontology was built following the ontology development methodology Methontology (Fernandez et al., 1997). In addition, it was constructed using the Protégé ontology environment (Protégé, 2007) and was implemented in OWL (Web Ontology Language) (Antoniou and Harmelen, 2004). Findings The proposed e-portfolio ontology provides humans with a shared vocabulary that enables capturing the most important elements in e-portfolios and serves as the basis for the semantic interoperability for machines. Originality/value The main advantage of the e-portfolio ontology lies in its ability to provide a common and semantically enriched representation of e-portfolio artifacts, thus facilitating the interoperability and exchange of competency evidences between different learning systems and platforms. In addition, capturing the semantics of e-portfolios helps to make them utilizable by intelligent applications.
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Vogler, Adrian, Binh Vu, Matthias Then, and Matthias Hemmje. "Towards a QBLM-Based Qualification-Management Methodology Supporting Human-Resource Management and Development." Information 15, no. 10 (2024): 600. http://dx.doi.org/10.3390/info15100600.
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Abstract: This position paper presents a novel perspective on addressing the challenges of digital transformation in higher education through the development of a qualification-based learning model (QBLM) qualification management methodology. It argues that the rapid pace of technological advancement and the resulting need for continuous upskilling and reskilling necessitate a more dynamic and adaptive approach to human-resource management and development. The paper posits that by extending QBLM through the integration of artificial intelligence (AI) and machine learning (ML), a more effective system for analyzing competence requirements and designing personalized learning pathways can be created. The paper proposes a three-fold approach: (1) developing the FPHR ontology to support semantic annotation of HR qualifications in higher-education institutions (HEIs), (2) integrating this ontology into QBLM to ensure the machine-readability of qualifications, and (3) modeling a knowledge-based production process for HRs in skills-based learning. This paper outlines the current state of the art, presents conceptual models, and describes planned proof-of-concept implementations and evaluations. It contends that this approach will significantly enhance the effectiveness of human-resource development in the rapidly evolving digital knowledge society. By presenting this position, the paper aims to stimulate discussion and collaboration within the academic community on innovative approaches to qualification management in higher education. The work addresses critical issues arising from technological development and offers a forward-thinking solution to bridge the gap between current and future skill requirements in industry and academia.
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Qiu, Qinjun, Miao Tian, Kai Ma, Yong Jian Tan, Liufeng Tao, and Zhong Xie. "A question answering system based on mineral exploration ontology generation: A deep learning methodology." Ore Geology Reviews 153 (February 2023): 105294. http://dx.doi.org/10.1016/j.oregeorev.2023.105294.
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Oluborode, Kayode, Gregory M. Wajiga, and Yusuf M. Malgwi. "Facial Detection and Recognition Analysis using Ontology-Driven Machine Learning Model." International Journal of Development Mathematics (IJDM) 1, no. 4 (2024): 214–26. https://doi.org/10.62054/ijdm/0104.17.
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Facial detection and recognition technologies have achieved remarkable advancements through the integration of ontology-driven machine learning (ML). This study examines how ontology structured framework for representing domain-specific knowledge to enhance the robustness, accuracy, and interpretability of ML models, particularly Convolutional Neural Networks (CNNs), in the field of facial detection and recognition. Acknowledging the limitations of traditional ML methods, such as data inefficiency, inadequate generalization, and insufficient interpretability, this research addresses critical challenges, including semantic discrepancies, variations in environmental conditions, and ethical considerations. By leveraging ontology, the study aims to provide semantic enrichment, contextual adaptation, and data augmentation for ML models. A hybrid methodology is introduced that effectively integrates ontology with CNNs, in conjunction with the Viola-Jones and Eigenfaces algorithms, to improve performance in facial recognition tasks. The study utilizes comprehensive datasets such as CelebA and MegaFace, employing rigorous preprocessing and training processes that incorporate ontology-based feedback mechanisms. The results demonstrated significant improvements in accuracy, robustness, and explainability. Ontology-CNN models outperform traditional approaches in managing variations in facial attributes and environmental conditions. This study concludes that ontology-based frameworks present a promising avenue for developing efficient and ethically responsible facial recognition systems. Future research should focus on exploring the scalability of these models across diverse demographic contexts and further addressing ethical considerations related to privacy and fairness.
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Kotis, Konstantinos, Andreas Papasalouros, George Vouros, Nikolaos Pappas, and Konstantinos Zoumpatianos. "Enhancing the Collective Knowledge for the Engineering of Ontologies in Open and Socially Constructed Learning Spaces." JUCS - Journal of Universal Computer Science 17, no. (12) (2011): 1710–42. https://doi.org/10.3217/jucs-017-12-1710.
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The aim of this paper is to present a novel technological approach for enhancing the collective knowledge of communities of learners on the engineering of ontologies within a collaborative, open and socially constructed environment. The proposed technology aims at shaping information spaces into ontologies in a collaborative, communicative and learner-centered way during the ontology development life-cycle. The paper conjectures that such a collaborative environment can yield educational benefits, thus there is need to follow principles that apply in the Computer Supported Collaborative Learning (CSCL) paradigm. This work is mainly based on a collaborative and human-centered ontology engineering methodology and on a meta-ontology framework for developing ontologies, namely HCOME and HCOME-3O respectively. The integration of key technologies such as Semantic Wiki and Argumentation models with Ontology Engineering methodologies and tools serve as an enabler of learning spaces construction for different domain-specific information spaces in open settings. Inside these learning spaces innovative conceptualizations (both domain and development) are conceived, described by intertwined ontological meta-models following the HCOME-3O specifications for future reference and tutoring support. Such learning spaces support two types of ontology engineering courses: a) courses related to the know-how of shaping information spaces into ontologies (namely, the development knowledge) and b) courses related to the analysis of the domain itself (namely, the domain knowledge). The paper reports on the evaluation of the approach within a CSCL setting in Ontology Engineering, using the integrated set of tools and the framework that have been developed for the collaborative engineering of ontologies.
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Budiantari, Ni Made Julia, and Ngurah Agus Sanjaya ER. "Pengembangan Model Ontologi Cerita Rakyat Bali." Jurnal Nasional Teknologi Informasi dan Aplikasnya 1, no. 3 (2023): 961. https://doi.org/10.24843/jnatia.2023.v01.i03.p23.
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This research aims to develop an ontology related to the domain of Balinese folklore. Balinese folklore is an important part of Bali's cultural heritage and has moral values, as well as being an inspiration in art and performance. Ontology is a method to organize and categorize information in a structured way, and has been widely applied in various fields. However, research on the ontology of Balinese folklore is still limited. This research will propose and develop a comprehensive and structured ontology model for Balinese folklore. The steps to be taken include analysis of relevant ontologies, development of an ontology schema, organization and classification of information, and testing and validation of the ontology model built. The method used in this research is methodology, which includes the stages of specification, knowledge acquisition, conceptualization, integration, evaluation, and documentation. The result of this research is a structured Balinese folklore ontology that has been evaluated using SPARQL queries. This ontology can be used as a source of information and reference in learning and utilizing Balinese folklore.
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Pastor, Danilo, Gloria Arcos-Medina, Vanessa Bonito, and Jaime Cepeda. "Design of an Adaptive Educational Application to Generate Customized Tests Based on Ontology." International Journal of Emerging Technologies in Learning (iJET) 16, no. 03 (2021): 171. http://dx.doi.org/10.3991/ijet.v16i03.17805.
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The personalization of certain teaching processes produces improved learn-ing results. In the assessment of learning, there is a need to personalize the test items according to the learning styles of the students. This paper pro-poses the design of an adaptive application that generates personalized tests according to the students' learning styles. To facilitate the design of the proposed application, an ontology for creating personalized tests was de-signed based on the use of learning styles by means of applying the Methontology methodology. This ontology has a hierarchy of 3 levels, 9 first-level classes, 12 second-level subclasses, and 10 third-level sub-classes. The application was developed using the Primefaces framework and the Jena library to manage the ontology. At the end of the development stage, the usability of the application created was measured using the heu-ristic evaluation method based on the ten principles of Jackob Nielsen. The results obtained indicate that the application complies with the aforemen-tioned principles, earning a 94% usability rating. Consequently, it can be deemed a useful application for end-users
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Adel, Ebtsam, Shaker El-Sappagh, Sherif Barakat, Jong-Wan Hu, and Mohammed Elmogy. "An Extended Semantic Interoperability Model for Distributed Electronic Health Record Based on Fuzzy Ontology Semantics." Electronics 10, no. 14 (2021): 1733. http://dx.doi.org/10.3390/electronics10141733.
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Semantic interoperability of distributed electronic health record (EHR) systems is a crucial problem for querying EHR and machine learning projects. The main contribution of this paper is to propose and implement a fuzzy ontology-based semantic interoperability framework for distributed EHR systems. First, a separate standard ontology is created for each input source. Second, a unified ontology is created that merges the previously created ontologies. However, this crisp ontology is not able to answer vague or uncertain queries. We thirdly extend the integrated crisp ontology into a fuzzy ontology by using a standard methodology and fuzzy logic to handle this limitation. The used dataset includes identified data of 100 patients. The resulting fuzzy ontology includes 27 class, 58 properties, 43 fuzzy data types, 451 instances, 8376 axioms, 5232 logical axioms, 1216 declarative axioms, 113 annotation axioms, and 3204 data property assertions. The resulting ontology is tested using real data from the MIMIC-III intensive care unit dataset and real archetypes from openEHR. This fuzzy ontology-based system helps physicians accurately query any required data about patients from distributed locations using near-natural language queries. Domain specialists validated the accuracy and correctness of the obtained results.
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Makris, Christos, and Michael Angelos Simos. "OTNEL: A Distributed Online Deep Learning Semantic Annotation Methodology." Big Data and Cognitive Computing 4, no. 4 (2020): 31. http://dx.doi.org/10.3390/bdcc4040031.
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Semantic representation of unstructured text is crucial in modern artificial intelligence and information retrieval applications. The semantic information extraction process from an unstructured text fragment to a corresponding representation from a concept ontology is known as named entity disambiguation. In this work, we introduce a distributed, supervised deep learning methodology employing a long short-term memory-based deep learning architecture model for entity linking with Wikipedia. In the context of a frequently changing online world, we introduce and study the domain of online training named entity disambiguation, featuring on-the-fly adaptation to underlying knowledge changes. Our novel methodology evaluates polysemous anchor mentions with sense compatibility based on thematic segmentation of the Wikipedia knowledge graph representation. We aim at both robust performance and high entity-linking accuracy results. The introduced modeling process efficiently addresses conceptualization, formalization, and computational challenges for the online training entity-linking task. The novel online training concept can be exploited for wider adoption, as it is considerably beneficial for targeted topic, online global context consensus for entity disambiguation.
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Lee, Kwangyon, Haemin Jung, June Seok Hong, and Wooju Kim. "Learning Knowledge Using Frequent Subgraph Mining from Ontology Graph Data." Applied Sciences 11, no. 3 (2021): 932. http://dx.doi.org/10.3390/app11030932.
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In many areas, vast amounts of information are rapidly accumulating in the form of ontology-based knowledge graphs, and the use of information in these forms of knowledge graphs is becoming increasingly important. This study proposes a novel method for efficiently learning frequent subgraphs (i.e., knowledge) from ontology-based graph data. An ontology-based large-scale graph is decomposed into small unit subgraphs, which are used as the unit to calculate the frequency of the subgraph. The frequent subgraphs are extracted through candidate generation and chunking processes. To verify the usefulness of the extracted frequent subgraphs, the methodology was applied to movie rating prediction. Using the frequent subgraphs as user profiles, the graph similarity between the rating graph and new item graph was calculated to predict the rating. The MovieLens dataset was used for the experiment, and a comparison showed that the proposed method outperformed other widely used recommendation methods. This study is meaningful in that it proposed an efficient method for extracting frequent subgraphs while maintaining semantic information and considering scalability in large-scale graphs. Furthermore, the proposed method can provide results that include semantic information to serve as a logical basis for rating prediction or recommendation, which existing methods are unable to provide.
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Massaro, Alessandro, Gabriele Cosoli, Nicola Magaletti, and Alberto Costantiello. "A Search Methodology Based on Industrial Ontology and Machine Learning to Analyze Georeferenced Italian Districts." Knowledge 2, no. 2 (2022): 243–65. http://dx.doi.org/10.3390/knowledge2020015.
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The subject of the proposed study is a method implementable for a search engine able to provide supply chain information, gaining the company’s knowledge base. The method is based on the construction of specific supply chain ontologies to enrich Machine Learning (ML) algorithm results able to filter and refine the searching process. The search engine is structured into two main search levels. The first one provides a preliminary filter of supply chain attributes based on the hierarchical clustering approach. The second one improves and refines the research by means of an ML classification and web scraping. The goal of the searching method is to identify a georeferenced supply chain district, finalized to optimize production and planning production strategies. Different technologies are proposed as candidates for the implementation of each part of the search engine. A preliminary prototype with limited functions is realized by means of Graphical User Interfaces (GUIs). Finally, a case study of the ice cream supply chain is discussed to explain how the proposed method can be applied to construct a basic ontology model. The results are performed within the framework of the project “Smart District 4.0”.
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Utama, Agung Bella Putra, Syaad Patmanthara, Aji Prasetya Wibawa, and Gülsün Kurubacak. "Forecasting learning in electrical engineering and informatics: An ontological approach." International Journal of Education and Learning 5, no. 3 (2023): 185–96. http://dx.doi.org/10.31763/ijele.v5i3.1227.
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This research explores the vital role of ontology in learning forecasting in electrical engineering and informatics. As formally defined models of knowledge, ontologies are critical in organizing concepts for predictive learning. More than just an inquiry, our research reveals complex interconnections centered on Internet of Things (IoT) design, the semantic web, and knowledge modeling. Applications demonstrate the practical significance of ontologies in fostering intelligent connections, advancing information production, and improving interactions between computers, devices, and humans. This research introduces a comprehensive forecasting learning ontology to highlight the importance of ontologies in education, scientific inquiry, and developing systems for predictive analysis. Ontologies provide a structured framework for understanding the essence of predictive learning, encompassing key elements such as ideas, terminology, methodology, algorithms, data preprocessing, assessment, validation, data sources, application environments, interactions with technology, and learning processes. Emphasizing ontologies as indispensable instruments that drive technological development, our work underscores structured representation, semantic interoperability, and knowledge integration. In summary, this research improves the understanding of ontologies in forecasting by explaining practical applications and revealing new perspectives. Its unique contribution lies in its specific applications and natural consequences, laying the foundation for the future progress of ontology and learning forecasting, especially in educational contexts.
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Anggraini, Dhea Ayu, Oktaviani Adhi Suciptaningsih, Ade Eka Anggraini, Dedi Kuswandi, and M. Ramli. "Philosophical Ontology, Axiology, and Epistemology Approaches in Developing Literacy through Differentiated Instruction Strategies in Elementary Schools." Eduvest - Journal of Universal Studies 5, no. 5 (2025): 5786–98. https://doi.org/10.59188/eduvest.v5i5.50078.
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In the contemporary educational landscape, integrating philosophical frameworks—ontology, axiology, and epistemology—into differentiated instruction strategies is essential for enhancing literacy in elementary schools. This study employs a literature review methodology, analyzing 14 reviewed articles to explore how these philosophical approaches inform effective teaching practices. The findings reveal that educators' perceptions of knowledge (ontology) significantly influence literacy strategies, while understanding values (axiology) is crucial for creating inclusive learning environments. Additionally, students' epistemological beliefs impact their engagement with literacy instruction, highlighting the need for educators to address these beliefs in their teaching. The implications of this research suggest that a comprehensive framework incorporating these philosophical dimensions can improve literacy outcomes and foster equitable learning opportunities for diverse learners.
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Fairbanks, Jeffrey, Andres Orbe, Christine Patterson, Edoardo Serra, and Marion Scheepers. "Identifying ATT&CK Tactics in Android Malware Control Flow Graph through Graph Representation Learning and Interpretability (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12941–42. http://dx.doi.org/10.1609/aaai.v36i11.21607.
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To mitigate a malware threat it is important to understand the malware’s behavior. The MITRE ATT&ACK ontology specifies an enumeration of tactics, techniques, and procedures (TTP) that characterize malware. However, absent are automated procedures that would characterize, given the malware executable, which part of the execution flow is connected with a specific TTP. This paper provides an automation methodology to locate TTP in a sub-part of the control flow graph that describes the execution flow of a malware executable. This methodology merges graph representation learning and tools for machine learning explanation.
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Volegzhanina, Eugeniya Mikhailovna, and Irina Sergeevna Volegzhanina. "Knowledge Representation in Multilingual Education Resources." Development of education 5, no. 4 (2022): 19–26. http://dx.doi.org/10.31483/r-104718.
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Introduction. Ontologies are now recognised as the advanced standard of knowledge representation for e-learning and some industries. In particular, the development of multilingual ontological education resources is characterised as a promising area of research in the context of industry universities’ digital transformation. The article deals with the development of an academic course multilingual ontology in a Controlled Natural Language. Relevance. Although there are many ontology editors, national developers of education resources should be familiar with formal logic and have a good command of English. Therefore, it is difficult to discuss widespread use of ontology-based education solutions in Russian universities. Materials and Methods. The article offers a version of Controlled Russian Language for academic knowledge representation. A methodology to be used for compiling academic course ontologies is developed. As an example, a piece of ontology for the Introductory Course on Railways is considered. Results and Discussion. To support this way of knowledge representation, a prototype of ontology editor Onto.plus was developed to support the version of Controlled Russian Language. To implement the multilanguage function, equivalent versions for the Controlled Russian Language ontology were developed in English and Chinese. Conclusions. The solutions are a contribution to the implementation of an open project to develop an ontology resource integrating universities and industry.
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Phung, Trang, Ngoc Ly Q., and Masayuki Fukuzawa. "A Human Retrieval System based on Human Attribute Ontology and Deep Multi-task Neural Network." ICT Research 2024, no. 2 (2024): 80–93. http://dx.doi.org/10.32913/mic-ict-research.v2024.n2.1255.
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The goal of this research is to enhance the capabilityof image retrieval systems to understand images moreeffectively. We present a model designed for searching humanobjects (such as pedestrians or persons) within expansiveimage datasets. Our unique approach involves developing animage retrieval system that incorporates attribute learningand the Human Attribute Ontology (HAO). This researchoffers several key contributions: (1) The development of theHuman Attribute Ontology (HAO) which serves as a repositoryfor storing prior knowledge about images. Thanks toits hierarchical structure, this ontology facilitates the reuse ofprior knowledge, optimizing the subsequent stages of attributelearning and image retrieval; (2) The implementation of aConvolutional Neural Network (CNN) to spearhead attributelearning, leveraging the HAO to enhance accuracy; (3) Thecreation of a Human Image Retrieval system that utilizes bothattribute learning and the HAO. Our system delves deeperby understanding images at the attribute level, highlightingthe advantages of harnessing the ontology to reuse existingknowledge. The efficacy of our methodology is validatedthrough experiments on benchmark datasets like PETA andPa100k achieving state-of-the-art results.
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Kalita, Deepjyoti, and Dipen Deka. "Ontology for preserving the knowledge base of traditional dances (OTD)." Electronic Library 38, no. 4 (2020): 785–803. http://dx.doi.org/10.1108/el-11-2019-0258.
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Purpose Systematic organization of domain knowledge has many advantages in archiving, sharing and retrieval of information. Ontologies provide a cushion for such practices in the semantic Web environment. This study aims to develop an ontology that can preserve the knowledge base of traditional dance practices. Design/methodology/approach It is hypothesized that an ontology-based approach for the chosen domain might boost collaborative research prospects in the domain. A systematic methodology was developed for modeling the ontology based on the analytico-synthetic rule of library classification. Protégé 5.2 was used as an editor for the ontology using the Web ontology language combined with description logic axioms. Ontology was later implemented in a local GraphDB repository to run queries over it. Findings The developed ontology on traditional dances (OTD) was tested using the dances of the Rabha tribes of North East India. Rabha tribes are from an indigenous mongoloid community and have a robust presence in Southeast Asian countries, such as Myanmar, Thailand, Bangladesh, Bhutan and Nepal. The result from HermiT reasoner found the presence of no logical inconsistency in the ontology, while the OOPS! pitfall checker tool reported no major internal inconsistency. The induced knowledge base of traditional dances of the Rabha’s in the developed OTD was further validated based on some competency questions. Research limitations/implications In the growing trend of globalization, preservation of the cultural knowledge base of human societies is an important issue. Traditional dances reflect a strong base of the cultural heritage of human societies as they are closely related to the lifestyle, habitat, religious practices and festivals of a specific community. Originality/value The current study is exclusively designed, keeping in mind the variables of traditional dance domain based on a survey of the user- and domain-specific needs. The ontology finds probable uses in traditional knowledge information systems, lifestyle-based e-commerce sites and e-learning platforms.
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Maller, Mark. "GroundUp Ontology." Logos & Episteme 15, no. 2 (2024): 185–204. http://dx.doi.org/10.5840/logos-episteme202415215.
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The first pathway toward a new conceptualist answer to the existence of universals begins with Descartes. The article is guided by a Cartesian method of starting anew in metaphysics and our knowledge of mind-dependent universals. Relevant examples and learning experiments defend and validate the pragmatic utility of conceptualism. It is past time for analytic ontology to set aside its assumptions, reevaluate its methodology and simplify itself. I raise novel objections through metaphor and analogy against standard and Platonic realism. Independent universals of realism are speculative and are neither necessary nor sufficient. This rejection of metaphysical realism defends the validity of scientific empiricist realism. Historical arguments such as William James’ empirical conceptualism and J.S. Mill’s criticisms strengthen this position. Nominalist methods are also considered. My theory is confirmed and useful for a preliminary epistemic-ontology which evaluates concepts and universals of mineral species. This appendix is consistent with Descartes’ theory of attributes and provides a new important approach to this field of study. The article, long dormant, is made possible by the work of Rene Descartes.
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Niyazova, R., A. Aktayeva, A. Sharipbay, A. Kubigenova, and B. Razakhova. "Development of a smart textbook in informatics with interactive learning support and Kazakh language integration based on artificial intelligence technologies." International Journal of Innovative Research and Scientific Studies 8, no. 3 (2025): 2413–30. https://doi.org/10.53894/ijirss.v8i3.7020.
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This article presents a project for developing interactive models of an educational resource for determining the semantic proximity between the ontology of knowledge extracted from a knowledge base based on a given question and the ontology generated from a student's answer in computer science. A general methodology for developing software has been proposed. This methodology will form the basis for the automated creation of smart textbooks on computer science in the Kazakh language using ontological models and thesauri. These educational resource models are suitable for any type of educational process (Blended Learning Technology (BLT), full-time, part-time). For example, the article describes an online smart textbook that will adapt to the student's individual learning path by providing personalized text, audio, and video materials, asking questions, and evaluating answers with an indication of percentage accuracy. A pedagogical experiment has been conducted to assess the students' performance. The online smart textbook will adapt to the student's learning style by providing personalized text, audio, and video materials. It will also ask questions and evaluate answers with an indication of percentage accuracy. In addition, completed assignments were analyzed to assess students' progress using static digital materials and standard computer testing systems for a dynamic, intelligent learning environment and knowledge assessment. The results show that the proposed methodology has great potential for increasing student engagement in studying the formalization and processing of the grammar of the Kazakh language using production rules and, based on them, developing a grammar processor, creating ontologies, thesauri, and knowledge bases on the content of “Computer Science”.
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Peter Ozioma, Uzoma, Amanze Bethran Chibuike, Agbakwuru Alphonsus Onyekachi, and Agbasonu V.C. "DEVELOPMENT OF A VISUAL SEMANTIC WEB ONTOLOGY BASED LEARNING MANAGEMENT SYSTEM." International Journal of Engineering Applied Sciences and Technology 6, no. 10 (2022): 226–38. http://dx.doi.org/10.33564/ijeast.2022.v06i10.030.
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This paper specifically aim at developing a visual semantic web ontology based e-Learning management system. The existing e-learning platforms have failed to address some challenges including managing the huge e-Learning content that is continuously grows on the web, meeting the learner's requirements when searching for an electronic learning content, representing the knowledge in a format that is easy to read and making it capable of thinking and thus allowing the re-use of e-Learning objects. In addition, most search results contain some irrelevant documents that cannot serve the need of the learner. Therefore, this thesis designed a common interface for learners, instructors, and administrators of academic institutions to upload and access learning materials. The new system created an ontology repository to maintain learners’ personalization details and taxonomy of the learning resources and integrate the knowledge embedded in many remote ontologies for improved e -Learning management system. In addition, an intelligent search engine through which learners can semantically search their learning materials and filter the search results returned by the search engine based on some semantics and the user’s preferences created. These designs simulated using a web-system developed with PHP, MySQL and JavaScript. The System Design followed the Object Oriented Design and Analysis Methodology for componentization of the system modules giving room for coupling, decoupling, modification, encapsulation and reuse, as well as easy maintainability. Unified Modeling Language extensively used to simplify the explanation of the system modules. The software performance wastested; the result shows that visual semantic web ontology-based e-Learning management system achieved 95% accuracy in returning the desired web search result.
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Muhammad Rashad, Muhammad Rashad, Noreen Khalid Noreen Khalid, Ameer Hamza Ameer Hamza, Sikander Javed, and Kashif Bilal Majeed Kashif Bilal Majeed. "A SEMANTIC FAKE NEWS DETECTION SYSTEM USING MACHINE LEARNING CLASSIFIER." Kashf Journal of Multidisciplinary Research 1, no. 12 (2024): 264–79. https://doi.org/10.71146/kjmr171.
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The purpose of fake news detection system is to build ontology to find hypothesis involved in misleading social media users through automated reasoning. Ontology for classification of news content has been created after understanding the semantic notations of textual features with in fake news dataset. The dataset we have used in our approach openly available on open-source data repository with the name fake News. The proposed model will provide semantic analysis of news content of the dataset and classification of news content into fake categories. The outcome of our proposed solution can be originating by applying three different classifiers of machine learning that is Random Forest, Logistic regression and LSTM (Long Short-Term Memory) that showed results about fake news and the accuracy of our proposed methodology is almost about 97%, 98% and 99% respectively. Thus the results prove that machine learning models performed better after analyzing the semantic features from news datasets.
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Roldan-Molina, Gabriela R., Jose R. Mendez, Iryna Yevseyeva, and Vitor Basto-Fernandes. "Ontology Fixing by Using Software Engineering Technology." Applied Sciences 10, no. 18 (2020): 6328. http://dx.doi.org/10.3390/app10186328.
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This paper presents OntologyFixer, a web-based tool that supports a methodology to build, assess, and improve the quality of ontology web language (OWL) ontologies. Using our software, knowledge engineers are able to fix low-quality OWL ontologies (such as those created from natural language documents using ontology learning processes). The fixing process is guided by a set of metrics and fixing mechanisms provided by the tool, and executed primarily through automated changes (inspired by quick fix actions used in the software engineering domain). To evaluate the quality, the tool supports numerical and graphical quality assessments, focusing on ontology content and structure attributes. This tool follows principles, and provides features, typical of scientific software, including user parameter requests, logging, multithreading execution, and experiment repeatability, among others. OntologyFixer architecture takes advantage of model view controller (MVC), strategy, template, and factory design patterns; and decouples graphical user interfaces (GUI) from ontology quality metrics, ontology fixing, and REST (REpresentational State Transfer) API (Application Programming Interface) components (used for pitfall identification, and ontology evaluation). We also separate part of the OntologyFixer functionality into a new package called OntoMetrics, which focuses on the identification of symptoms and the evaluation of the quality of ontologies. Finally, OntologyFixer provides mechanisms to easily develop and integrate new quick fix methods.
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Fouzia, Nadeem, MUHAMMAD ARSHAD AWAN, Tariq Muhammad, and Khaleeq Danish. "Developing an Arabic-Urdu Ontology of Quranic Concepts: A Semantic Approach." International Journal of Innovations in Science & Technology 7, no. 1 (2025): 637–50. https://doi.org/10.5281/zenodo.15496806.
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An Arabic-Urdu ontology system dedicated to Quranic concepts represents a necessity for protecting the semantic value and making religious texts more accessible during Quranic study. Ontology-driven annotation tools show their ability to achieve precise translations and thematic searches by establishing their effects on the translation process. Researchers built this ontology using Protégé 5.6.4 which classifies Quranic concepts into twelve specific sections from Corpus.quran.com: Artifact, Astronomical Body, Event, False Deity, Holy Book, Language, Living Creation, Location, Physical Attribute, Physical Substance, Religion and Weather Phenomena. Validation of the ontology included expert evaluation and a HermiT computational assessment that led to user testingand an accuracy rate of 89.31%. The system uses SPARQL queries as a method to achieve both organized and efficient retrieval of Quranic knowledge. The analysis emphasizes the value of ontological structures as a means to connect Arabic and Urdu semantics which then improves both Quranic interpretation and computational linguistic understanding. While the methodology effectively maps Quranic concepts, challenges such as language nuances and theological precision persist, requiring further advancements in machine learning and natural language processing. Future research should focus on expanding ontology categories, integrating AI-based models, and enhancing phonetic mappings to improve the ontology’s adaptability and usability in diverse linguistic and cultural settings.
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Abu-Salih, Bilal, Pornpit Wongthongtham, and Kit Yan Chan. "Twitter mining for ontology-based domain discovery incorporating machine learning." Journal of Knowledge Management 22, no. 5 (2018): 949–81. http://dx.doi.org/10.1108/jkm-11-2016-0489.
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Purpose This paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users’ domain (s) of interest is a significant step towards addressing their domain-based trustworthiness through an accurate understanding of their content in their OSNs. Design/methodology/approach This study uses a Twitter mining approach for domain-based classification of users and their textual content. The proposed approach incorporates machine learning modules. The approach comprises two analysis phases: the time-aware semantic analysis of users’ historical content incorporating five commonly used machine learning classifiers. This framework classifies users into two main categories: politics-related and non-politics-related categories. In the second stage, the likelihood predictions obtained in the first phase will be used to predict the domain of future users’ tweets. Findings Experiments have been conducted to validate the mechanism proposed in the study framework, further supported by the excellent performance of the harnessed evaluation metrics. The experiments conducted verify the applicability of the framework to an effective domain-based classification for Twitter users and their content, as evident in the outstanding results of several performance evaluation metrics. Research limitations/implications This study is limited to an on/off domain classification for content of OSNs. Hence, we have selected a politics domain because of Twitter’s popularity as an opulent source of political deliberations. Such data abundance facilitates data aggregation and improves the results of the data analysis. Furthermore, the currently implemented machine learning approaches assume that uncertainty and incompleteness do not affect the accuracy of the Twitter classification. In fact, data uncertainty and incompleteness may exist. In the future, the authors will formulate the data uncertainty and incompleteness into fuzzy numbers which can be used to address imprecise, uncertain and vague data. Practical implications This study proposes a practical framework comprising significant implications for a variety of business-related applications, such as the voice of customer/voice of market, recommendation systems, the discovery of domain-based influencers and opinion mining through tracking and simulation. In particular, the factual grasp of the domains of interest extracted at the user level or post level enhances the customer-to-business engagement. This contributes to an accurate analysis of customer reviews and opinions to improve brand loyalty, customer service, etc. Originality/value This paper fills a gap in the existing literature by presenting a consolidated framework for Twitter mining that aims to uncover the deficiency of the current state-of-the-art approaches to topic distillation and domain discovery. The overall approach is promising in the fortification of Twitter mining towards a better understanding of users’ domains of interest.
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Gharib, Tarek, Nagwa Badr, Shaimaa Haridy, and Ajith Abraham. "Enriching Ontology Concepts Based on Texts from WWW and Corpus." JUCS - Journal of Universal Computer Science 18, no. (16) (2012): 2234–51. https://doi.org/10.3217/jucs-018-16-2234.
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In spite of the growing of ontological engineering tools, ontology knowledge acquisition remains a highly manual, time-consuming and complex task. Automatic ontology learning is a well-established research field whose goal is to support the semi-automatic construction of ontologies starting from available digital resources (e.g., A corpus, web pages, dictionaries, semi-structured and structured sources) in order to reduce the time and effort in the ontology development process. This paper proposes an enhanced methodology for enriching Lexical Ontologies such as the popular open-domain vocabulary –WordNet. Ontologies like WordNet can be semantically enriched to obtain extensions and enhancements to its lexical database. The proliferation of senses in WordNet is considered as one of its main shortcomings for practical applications. Therefore, the presented methodology depends on the Coarse-Grained word senses. These senses are generated from applying WordNet Fine-Grained word senses to a Merging Sense algorithm. This algorithm merges only semantically similar word senses instead of applying traditional clustering techniques. A performance comparison is illustrated between two different data sources (Web, Corpus) used in the Enrichment process. The results obtained from using Coarse-Grained word senses in both cases yields better precision than Fine-Grained word senses in the Word Sense Disambiguation task.
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V., I. Shynkarenko, and I. Zhuchyi L. "Constructive-Synthesizing Modelling of Ontological Document Management Support for the Railway Train Speed Restrictions." Science and Transport Progress. Bulletin of the Dnipropetrovsk National University of Railway Transport named after Academician V. Lazaryan, no. 2(98) (June 20, 2022): 59–68. https://doi.org/10.15802/stp2022/268001.
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<strong>Purpose. </strong>During the development of railway ontologies, it is necessary to take into account both the data of information systems and regulatory support to check their consistency. To do this, data integration is performed. The purpose of the work is to formalize the methods for integrating heterogeneous sources of information and ontology formation. <strong>Methodology. </strong>Constructive-synthesizing modelling of ontology formation and its resources was developed.<strong> Findings.</strong> Ontology formation formalization has been performed, which allows expanding the possibilities of automating the integration and coordination of data using ontologies. In the future, it is planned to expand the structural system for the formation of ontologies based on textual sources of railway regulatory documentation and information systems. <strong>Originality. </strong>The authors laid the foundations of using constructive-synthesizing modelling in the railway transport ontological domain to form the structure and data of the railway train speed restriction warning tables (database and csv format), their transformation into a common tabular format, vocabulary, rules and ontology individuals, as well as ontology population. Ontology learning methods have been developed to integrate data from heterogeneous sources. <strong>Practical value.</strong> The developed methods make it possible to integrate heterogeneous data sources (the structure of the table of the railway train management rules, the form and application for issuing a warning), which are railway domain-specific. It allows forming an ontology from its data sources (database and csv formats) to schema and individuals. Integration and consistency of information system data and regulatory documentation is one of the aspects of increasing the level of train traffic safety.
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Ravindran, Aisha, Jing Li, and Steve Marshall. "Learning Ethnography Through Doing Ethnography: Two Student—Researchers’ Insights." International Journal of Qualitative Methods 19 (January 1, 2020): 160940692095129. http://dx.doi.org/10.1177/1609406920951295.
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In this article, we present the accounts of the field experiences and challenges of two graduate student-researchers practising ethnographic methodology, conducting fieldwork, and writing up “post-modern” ethnographies that are both creative and “integrative”. We describe the complexities and tensions when two student-researchers negotiated many issues in the field and “behind the desk” as they transformed the texts: epistemology and ontology, reflexivity and auto-ethnography, and writing researchers and participants in and out of accounts. We conclude with a discussion on pedagogical implications, and consider the value of learning ethnography through doing ethnography.
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Shynkarenko, V. I., and L. I. Zhuchyi. "Constructive-Synthesizing Modelling of Ontological Document Management Support for the Railway Train Speed Restrictions." Science and Transport Progress, no. 2(98) (June 20, 2022): 59–68. http://dx.doi.org/10.15802/stp2022/268001.
Full textAbstract:
Purpose. During the development of railway ontologies, it is necessary to take into account both the data of information systems and regulatory support to check their consistency. To do this, data integration is performed. The purpose of the work is to formalize the methods for integrating heterogeneous sources of information and ontology formation. Methodology. Constructive-synthesizing modelling of ontology formation and its resources was developed. Findings. Ontology formation formalization has been performed, which allows expanding the possibilities of automating the integration and coordination of data using ontologies. In the future, it is planned to expand the structural system for the formation of ontologies based on textual sources of railway regulatory documentation and information systems. Originality. The authors laid the foundations of using constructive-synthesizing modelling in the railway transport ontological domain to form the structure and data of the railway train speed restriction warning tables (database and csv format), their transformation into a common tabular format, vocabulary, rules and ontology individuals, as well as ontology population. Ontology learning methods have been developed to integrate data from heterogeneous sources. Practical value. The developed methods make it possible to integrate heterogeneous data sources (the structure of the table of the railway train management rules, the form and application for issuing a warning), which are railway domain-specific. It allows forming an ontology from its data sources (database and csv formats) to schema and individuals. Integration and consistency of information system data and regulatory documentation is one of the aspects of increasing the level of train traffic safety.
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Cujar-Rosero, Felipe, David Santiago Pinchao Ortiz, Silvio Ricardo Timarán Pereira, and Jimmy Mateo Guerrero Restrepo. "Nature: A Tool Resulting from the Union of Artificial Intelligence and Natural Language Processing for Searching Research Projects in Colombia." International Journal of Artificial Intelligence & Applications 12, no. 04 (2021): 01–21. http://dx.doi.org/10.5121/ijaia.2021.12401.
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This paper presents the final results of the research project that aimed for the construction of a tool which is aided by Artificial Intelligence through an Ontology with a model trained with Machine Learning, and is aided by Natural Language Processing to support the semantic search of research projects of the Research System of the University of Nariño. For the construction of NATURE, as this tool is called, a methodology was used that includes the following stages: appropriation of knowledge, installation and configuration of tools, libraries and technologies, collection, extraction and preparation of research projects, design and development of the tool. The main results of the work were three: a) the complete construction of the Ontology with classes, object properties (predicates), data properties (attributes) and individuals (instances) in Protegé, SPARQL queries with Apache Jena Fuseki and the respective coding with Owlready2 using Jupyter Notebook with Python within the virtual environment of anaconda; b) the successful training of the model for which Machine Learning algorithms were used and specifically Natural Language Processing algorithms such as: SpaCy, NLTK, Word2vec and Doc2vec, this was also performed in Jupyter Notebook with Python within the virtual environment of anaconda and with Elasticsearch; and c) the creation of NATURE by managing and unifying the queries for the Ontology and for the Machine Learning model. The tests showed that NATURE was successful in all the searches that were performed as its results were satisfactory.
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OSKIN, A., G. KONAPLEVA, T. TAVGEN, and V. GROPPEN. "CREATION OF ELECTRONIC EDUCATIONAL AND METHODOLOGICAL COMPLEXES WITH THE HELP OF THE ONTOLOGICAL APPROACH." HERALD OF POLOTSK STATE UNIVERSITY. Series С FUNDAMENTAL SCIENCES, no. 1 (April 18, 2023): 23–28. http://dx.doi.org/10.52928/2070-1624-2023-40-1-23-28.
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The article discusses the possibilities of ontological modeling and its application for creating knowledge
 bases of electronic educational and methodological complexes. It is shown that with the help of ontological modeling
 it is possible to define a subject area, develop a conceptual model, create and fill an ontology with data, and
 finally put the ontology into action. The toolkit of ontological modeling and its possibilities for creating knowledge
 bases of electronic teaching and learning kits are considered. It is shown that the Zettelkasten technology and the
 Obsidian application can be used to build a working model of the knowledge base. An example of the application
 of the stated methodology in the construction of the knowledge base "History of the Polotsk Cadet Corps" is given.
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Hastings, Janna, Martin Glauer, Robert West, James Thomas, Alison J. Wright, and Susan Michie. "Predicting outcomes of smoking cessation interventions in novel scenarios using ontology-informed, interpretable machine learning." Wellcome Open Research 8 (November 7, 2023): 503. http://dx.doi.org/10.12688/wellcomeopenres.20012.1.
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Background Systematic reviews of effectiveness estimate the relative average effects of interventions and comparators in a set of existing studies e.g., using rate ratios. However, policymakers, planners and practitioners require predictions about outcomes in novel scenarios where aspects of the interventions, populations or settings may differ. This study aimed to develop and evaluate an ontology-informed, interpretable machine learning algorithm to predict smoking cessation outcomes using detailed information about interventions, their contexts and evaluation study methods. This is the second of two linked papers on the use of machine learning in the Human Behaviour-Change Project. Methods The study used a corpus of 405 reports of randomised trials of smoking cessation interventions from the Cochrane Library database. These were annotated using the Behaviour Change Intervention Ontology to classify, for each of 971 study arms, 82 features representing details of intervention content and delivery, population, setting, outcome, and study methodology. The annotated data was used to train a novel machine learning algorithm based on a set of interpretable rules organised according to the ontology. The algorithm was evaluated for predictive accuracy by performance in five-fold 80:20 cross-validation, and compared with other approaches. Results The machine learning algorithm produced a mean absolute error in prediction percentage cessation rates of 9.15% in cross-validation, outperforming other approaches including an uninterpretable ‘black-box’ deep neural network (9.42%), a linear regression model (10.55%) and a decision tree-based approach (9.53%). The rules generated by the algorithm were synthesised into a consensus rule set to create a publicly available predictive tool to provide outcome predictions and explanations in the form of rules expressed in terms of predictive features and their combinations. Conclusions An ontologically-informed, interpretable machine learning algorithm, using information about intervention scenarios from reports of smoking cessation trials, can predict outcomes in new smoking cessation intervention scenarios with moderate accuracy.
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Koutsomitropoulos, Dimitrios A. "Semantic annotation and harvesting of federated scholarly data using ontologies." Digital Library Perspectives 35, no. 3/4 (2019): 157–71. http://dx.doi.org/10.1108/dlp-12-2018-0038.
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Purpose Effective synthesis of learning material is a multidimensional problem, which often relies on handpicking approaches and human expertise. Sources of educational content exist in a variety of forms, each offering proprietary metadata information and search facilities. This paper aims to show that it is possible to harvest scholarly resources from various repositories of open educational resources (OERs) in a federated manner. In addition, their subject can be automatically annotated using ontology inference and standard thematic terminologies. Design/methodology/approach Based on a semantic interpretation of their metadata, authors can align external collections and maintain them in a shared knowledge pool known as the Learning Object Ontology Repository (LOOR). The author leverages the LOOR and show that it is possible to search through various educational repositories’ metadata and amalgamate their semantics into a common learning object (LO) ontology. The author then proceeds with automatic subject classification of LOs using keyword expansion and referencing standard taxonomic vocabularies for thematic classification, expressed in SKOS. Findings The approach for automatic subject classification simply takes advantage of the implicit information in the searching and selection process and combines them with expert knowledge in the domain of reference (SKOS thesauri). This is shown to improve recall by a considerable factor, while precision remains unaffected. Originality/value To the best of the author’s knowledge, the idea of subject classification of LOs through the reuse of search query terms combined with SKOS-based matching and expansion has not been investigated before in a federated scholarly setting.
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Oliveira, Luís, Rodrigo Rocha Silva, and Jorge Bernardino. "Wine Ontology Influence in a Recommendation System." Big Data and Cognitive Computing 5, no. 2 (2021): 16. http://dx.doi.org/10.3390/bdcc5020016.
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Wine is the second most popular alcoholic drink in the world behind beer. With the rise of e-commerce, recommendation systems have become a very important factor in the success of business. Recommendation systems analyze metadata to predict if, for example, a user will recommend a product. The metadata consist mostly of former reviews or web traffic from the same user. For this reason, we investigate what would happen if the information analyzed by a recommendation system was insufficient. In this paper, we explore the effects of a new wine ontology in a recommendation system. We created our own wine ontology and then made two sets of tests for each dataset. In both sets of tests, we applied four machine learning clustering algorithms that had the objective of predicting if a user recommends a wine product. The only difference between each set of tests is the attributes contained in the dataset. In the first set of tests, the datasets were influenced by the ontology, and in the second set, the only information about a wine product is its name. We compared the two test sets’ results and observed that there was a significant increase in classification accuracy when using a dataset with the proposed ontology. We demonstrate the general applicability of the methodology to other cases, applying our proposal to an Amazon product review dataset.
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Sewchurran, Kosheek, Derek Smith, and Dewald Roode. "Toward a regional ontology for information systems project management." International Journal of Managing Projects in Business 3, no. 4 (2010): 681–92. http://dx.doi.org/10.1108/17538371011076118.
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PurposeThe paper aims to paper an overview of a completed doctoral thesis which pursued the development of underlying theory (ontology) to give coherence to research in the information systems (IS) project management space.Design/methodology/approachAs a result of the considerable concern about a lack of underlying theory in project management the author has chosen to investigate the development of underlying theory to serve as a regional ontology to give debates undertaken to improve IS project management coherence. The thesis is a critical interpretive a priori effort. In the pursuit of the goal of developing a regional ontology, the notions, concepts and theories related to existentialism and social construction were investigated. These were investigated because the research literature places considerable emphasis on the need to understand as‐lived project experiences.FindingsOne of the significant outcomes that results from this research is the development of a proposed regional ontology. This was achieved by fusing the theories of Heidegger's Dasein, Bourdieu's “Theory of practice” and Maturana and Varela's “Theory of living systems”. The regional ontology is a consolidation of the various concepts defined by these researchers. These theories complement each other to give rise to a relational model of social construction which also has related phenomenological, existential and biological perspectives.Practical implicationsThe proposed ontology was interpreted using the popular alternatives that have recently emerged alongside the established best practices such as project management body of knowledge. The perspectives of complex, responsive processes of relating, the temporary organisation, agility and organisational becoming were reviewed using the regional ontology. The interpretation process illustrated that the regional ontology is able to provide a more fundamental and coherent context to subsume and delimit these emerging new frames.Originality/valueThe thesis also discusses the researcher's view of contemporary project management practice that accords with the regional ontology principles. Through argument and the contemporary context of IS project management practice that was sketched, the principles of the regional ontology are illuminated. Through this process it was possible to claim that established best practice modes of education should not exist in isolation but should instead be situated within a wider analogical context that embraces the values of learning, becoming and innovating.