Articles de revues : « Formal ontologies »

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Sanfilippo, Emilio M., Yoshinobu Kitamura, and Robert I. M. Young. "Formal ontologies in manufacturing." Applied Ontology 14, no. 2 (2019): 119–25. http://dx.doi.org/10.3233/ao-190209.

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Lumb, L. I., J. R. Freemantle, J. I. Lederman, and K. D. Aldridge. "Annotation modeling with formal ontologies: Implications for informal ontologies." Computers & Geosciences 35, no. 4 (2009): 855–61. http://dx.doi.org/10.1016/j.cageo.2008.03.009.

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Ferrario, Roberta, and Laurent Prévot. "Formal ontologies for communicating agents." Applied Ontology: An Interdisciplinary Journal of Ontological Analysis and Conceptual Modeling 2, no. 3-4 (2007): 209–16. https://doi.org/10.3233/apo-2007-042.

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Sanfilippo, Emilio, and Walter Terkaj. "Editorial: Formal Ontologies meet Industry." Procedia Manufacturing 28 (2019): 174–76. http://dx.doi.org/10.1016/j.promfg.2018.12.028.

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Abrusci, V. Michele, Christophe Fouqueré, and Marco Romano. "Formal Ontologies and Coherent Spaces." Journal of Applied Logic 12, no. 1 (2014): 67–74. http://dx.doi.org/10.1016/j.jal.2013.07.003.

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Müller, R., O. Mailahn, and R. Peifer. "Tool: Eine Sprachdomäne für die Montageplanung*/A domain specific language for assembly planning – Software-supported planning of human-robot cooperation based on ontologies." wt Werkstattstechnik online 108, no. 09 (2018): 606–10. http://dx.doi.org/10.37544/1436-4980-2018-09-42.

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Die Planung von Montagesystemen wird durch die Einführung von cyber-physischen Modulen und neuen Formen der Zusammenarbeit von Mensch und Roboter zunehmend komplexer. Ontologien können Planungswissen bezüglich Beziehungen und Restriktionen formal abbilden. Mit der hier beschriebenen Sprachdomäne werden Ontologien für Montageplaner zugänglich und anwendbar. Die Planung kann auf diese Weise beschleunigt und flexibilisiert werden. &nbsp; The planning of assembly systems is becoming increasingly complex with the introduction of cyber-physical modules and new forms of human-robot cooperation. Ontologies can formally capture planning knowledge in terms of relationships and restrictions. The domain specific language described here makes ontologies accessible and usable for assembly planners. Thus, planning may be accelerated and designed more flexibly.

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Lukashevich, N. V. "Concepts in formal and linguistic ontologies." Automatic Documentation and Mathematical Linguistics 45, no. 4 (2011): 155–62. http://dx.doi.org/10.3103/s0005105511040030.

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Shaked, Avi, and Oded Margalit. "Sustainable Risk Identification Using Formal Ontologies." Algorithms 15, no. 9 (2022): 316. http://dx.doi.org/10.3390/a15090316.

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The cyber threat landscape is highly dynamic, posing a significant risk to the operations of systems and organisations. An organisation should, therefore, continuously monitor for new threats and properly contextualise them to identify and manage the resulting risks. Risk identification is typically performed manually, relying on the integration of information from various systems as well as subject matter expert knowledge. This manual risk identification hinders the systematic consideration of new, emerging threats. This paper describes a novel method to promote automated cyber risk identification: OnToRisk. This artificial intelligence method integrates information from various sources using formal ontology definitions, and then relies on these definitions to robustly frame cybersecurity threats and provide risk-related insights. We describe a successful case study implementation of the method to frame the threat from a newly disclosed vulnerability and identify its induced organisational risk. The case study is representative of common and widespread real-life challenges, and, therefore, showcases the feasibility of using OnToRisk to sustainably identify new risks. Further applications may contribute to establishing OnToRisk as a comprehensive, disciplined mechanism for risk identification.

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Jongeling, T. B., and P. P. Kirschenmann. "FORMAL AND HYPOTHETICAL OR HEURISTIC ONTOLOGIES." Grazer Philosophische studien 29, no. 1 (1987): 217–23. http://dx.doi.org/10.1163/18756735-90000322.

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Brucker, Achim D., Idir Ait-Sadoune, Nicolas Méric, and Burkhart Wolff. "Parametric ontologies in formal software engineering." Science of Computer Programming 241 (April 2025): 103231. http://dx.doi.org/10.1016/j.scico.2024.103231.

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Jansen, L., and S. Schulz. "Formal Ontologies in Biomedical Knowledge Representation." Yearbook of Medical Informatics 22, no. 01 (2013): 132–46. http://dx.doi.org/10.1055/s-0038-1638845.

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Summary Objectives: Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are often misinterpreted to encode all kinds of statements, including those which are not ontological. Method: We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. Results: We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems.

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Smaili, Fatima Zohra, Xin Gao, and Robert Hoehndorf. "Formal axioms in biomedical ontologies improve analysis and interpretation of associated data." Bioinformatics 36, no. 7 (2019): 2229–36. http://dx.doi.org/10.1093/bioinformatics/btz920.

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Abstract Motivation Over the past years, significant resources have been invested into formalizing biomedical ontologies. Formal axioms in ontologies have been developed and used to detect and ensure ontology consistency, find unsatisfiable classes, improve interoperability, guide ontology extension through the application of axiom-based design patterns and encode domain background knowledge. The domain knowledge of biomedical ontologies may have also the potential to provide background knowledge for machine learning and predictive modelling. Results We use ontology-based machine learning methods to evaluate the contribution of formal axioms and ontology meta-data to the prediction of protein–protein interactions and gene–disease associations. We find that the background knowledge provided by the Gene Ontology and other ontologies significantly improves the performance of ontology-based prediction models through provision of domain-specific background knowledge. Furthermore, we find that the labels, synonyms and definitions in ontologies can also provide background knowledge that may be exploited for prediction. The axioms and meta-data of different ontologies contribute to improving data analysis in a context-specific manner. Our results have implications on the further development of formal knowledge bases and ontologies in the life sciences, in particular as machine learning methods are more frequently being applied. Our findings motivate the need for further development, and the systematic, application-driven evaluation and improvement, of formal axioms in ontologies. Availability and implementation https://github.com/bio-ontology-research-group/tsoe. Supplementary information Supplementary data are available at Bioinformatics online.

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Husáková, Martina, and Vladimír Bureš. "Formal Ontologies in Information Systems Development: A Systematic Review." Information 11, no. 2 (2020): 66. http://dx.doi.org/10.3390/info11020066.

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Computational ontologies are machine-processable structures which represent particular domains of interest. They integrate knowledge which can be used by humans or machines for decision making and problem solving. The main aim of this systematic review is to investigate the role of formal ontologies in information systems development, i.e., how these graphs-based structures can be beneficial during the analysis and design of the information systems. Specific online databases were used to identify studies focused on the interconnections between ontologies and systems engineering. One-hundred eighty-seven studies were found during the first phase of the investigation. Twenty-seven studies were examined after the elimination of duplicate and irrelevant documents. Mind mapping was substantially helpful in organising the basic ideas and in identifying five thematic groups that show the main roles of formal ontologies in information systems development. Formal ontologies are mainly used in the interoperability of information systems, human resource management, domain knowledge representation, the involvement of semantics in unified modelling language (UML)-based modelling, and the management of programming code and documentation. We explain the main ideas in the reviewed studies and suggest possible extensions to this research.

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Hnatkowska, Bogumila, Adrianna Kozierkiewicz, and Marcin Pietranik. "Formal transformation of OWL ontology to a FOKI generic meta-model." Computer Science and Information Systems, no. 00 (2025): 2. https://doi.org/10.2298/csis240227002h.

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Ontology integration is merging a set of ontologies to provide a single, unified ontology, which contains all of the knowledge from input ontologies. Most solutions described in the literature are based on the OWL format and in corporate its strengths and weaknesses. In our previous research, we developed the ontology integration framework FOKI, which does not use the OWL. Collected experimental data using prepared ontologies proved its usefulness. However, the lack of OWL support makes it challenging to use the FOKI framework in practical applications. This paper presents a meta-model and a set of transformation rules for bi-directional transformation between ontologies expressed in our framework and the OWL standard. The meta-model serves as a bridge in the transformation process. Transformation rules are built by referencing an abstract syntax element of OWL2 and an appropriate mathematical formalism from FOKI. Their correctness was verified on widely available ontologies expressed in OWL provided, e.g., by the Ontology Alignment Evaluation Initiative.

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Musen, M. A. "Domain Ontologies in Software Engineering: Use of Protégé with the EON Architecture." Methods of Information in Medicine 37, no. 04/05 (1998): 540–50. http://dx.doi.org/10.1055/s-0038-1634543.

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AbstractDomain ontologies are formal descriptions of the classes of concepts and the relationships among those concepts that describe an application area. The Protege software-engineering methodology provides a clear division between domain ontologies and domain-independent problemsolvers that, when mapped to domain ontologies, can solve application tasks. The Protege approach allows domain ontologies to inform the total software-engineering process, and for ontologies to be shared among a variety of problem-solving components. We illustrate the approach by describing the development of EON, a set of middleware components that automate various aspects of protocol-directed therapy. Our work illustrates the organizing effect that domain ontologies can have on the software-development process. Ontologies, like all formal representations, have limitations in their ability to capture the semantics of application areas. Nevertheless, the capability of ontologies to encode clinical distinctions not usually captured by controlled medical terminologies provides significant advantages for developers and maintainers of clinical software applications.

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Samoilov, D. E., V. A. Semenova, and S. V. Smirnov. "incomplete data analysis for building formal ontologies." Ontology of designing 6, no. 3 (2016): 317–39. http://dx.doi.org/10.18287/2223-9537-2016-6-3-317-339.

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An, Yoo Jung, Kuo-Chuan Huang, Soon Ae Chun, and James Geller. "A Formal Approach to Evaluating Medical Ontology Systems using Naturalness." International Journal of Computational Models and Algorithms in Medicine 1, no. 1 (2010): 1–18. http://dx.doi.org/10.4018/jcmam.2010072001.

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Ontologies, terminologies and vocabularies are popular repositories for collecting the terms used in a domain.It may be expected that in the future more such ontologies will be created for domain experts. However, there is increasing interest in making the language of experts understandable to casual users. For example, cancer patients often research their cases on the Web. The authors consider the problem of objectively evaluating the quality of ontologies (QoO). This article formalizes the notion of naturalness as a component of QoO and quantitatively measures naturalness for well-known ontologies (UMLS, WordNet, OpenCyc) based on their concepts, IS-A relationships and semantic relationships. To compute numeric values characterizing the naturalness of an ontology, this article defines appropriate metrics. As absolute numbers in such a pursuit are often meaningless, we concentrate on using relative naturalness metrics. That allows us to say that a certain ontology is relatively more natural than another one.

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Ma, Zongmin, Haitao Cheng, and Li Yan. "Automatic Construction of OWL Ontologies From Petri Nets." International Journal on Semantic Web and Information Systems 15, no. 1 (2019): 21–51. http://dx.doi.org/10.4018/ijswis.2019010102.

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Ontology, as a formal representation method of domain knowledge, plays a particular important key role in semantic web. How to construct ontologies has become a key technology in the semantic web, especially constructing ontologies from existing domain knowledge. Currently, Petri nets have been a mathematical modeling tool, and have been widely studied and successfully applied in modeling of software engineering, database and artificial intelligence. In particular, PNML (Petri Net Markup Language) language has been a part of ISO/IEC Petri nets standard for representing and exchanging data on Petri nets. Therefore, how to construct ontologies from PNML model of Petri nets needs to be investigated. In this article, the authors investigate a method for automatic construction of web ontology language (OWL) ontologies from PNML of Petri nets. Firstly, this paper gives a formal definition and the semantics of PNML models of Petri nets. On this basis, a formal approach for constructing OWL ontologies from PNML model of Petri nets is proposed, i.e., this paper transforms Petri nets (including PNML model and PNML document of the Petri nets) into OWL ontologies at both structure and instance levels. Furthermore, the correctness of the transformation is proved. Finally, a prototype construction tool called PN2OWL is developed to transform Petri nets models into OWL ontologies automatically.

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Nanda, Jyotirmaya, Timothy W. Simpson, Soundar R. T. Kumara, and Steven B. Shooter. "A Methodology for Product Family Ontology Development Using Formal Concept Analysis and Web Ontology Language." Journal of Computing and Information Science in Engineering 6, no. 2 (2005): 103–13. http://dx.doi.org/10.1115/1.2190237.

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The use of ontologies for information sharing is well documented in the literature, but the lack of a comprehensive and systematic methodology for constructing product ontologies has limited the process of developing ontologies for design artifacts. In this paper we introduce the Product Family Ontology Development Methodology (PFODM), a novel methodology to develop formal product ontologies using the Semantic Web paradigm. Within PFODM, Formal Concept Analysis (FCA) is used first to identify similarities among a finite set of design artifacts based on their properties and then to develop and refine a product family ontology using Web Ontology Language (OWL). A family of seven one-time-use cameras is used to demonstrate the steps of the PFODM to construct such an ontology. The benefit of PFODM lies in providing a systematic and consistent methodology for constructing ontologies to support product family design. The resulting ontologies provide a hierarchical conceptual clustering of related design artifacts, which is particularly advantageous for product family design where parts, processes, and most important, information is intentionally shared and reused to reduce complexity, lead-time, and development costs. Potential uses of the resulting ontologies and FCA representations within product family design are also discussed.

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Song, Hua Zhu, Cong Xiao, and Lu Xu. "Ontology-Based Semantic Similarity Measure with Concept Lattice." Applied Mechanics and Materials 411-414 (September 2013): 177–81. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.177.

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Semantic similarity measure has always been one of the important contents in artificial intelligence. This paper puts the ontology as the research object, and measure the semantic similarity between two ontologies in view of concept lattice. Firstly, concept lattice is introduced to similarity measure, and the thought of the ontology-based semantic similarity measure with concept lattice was given. Next, the solution of the measure was described, which includes generating a formal context of heterogeneous ontologies, constructing the corresponding formal context of formal context to fetch the formal context in concept lattice, and using irreducible infimum theory to calculate the similarity value of heterogeneous ontology concept. Finally, we employed a sample to verify the measure method. The results showed the method can effectively compute the semantic similarity between the ontologies, and the method proposed is valid and feasible.

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Aranda-Corral, Gonzalo A., Joaquín Borrego-Díaz, Antonia M. Chávez-González, and Nataliya M. Gulayeva. "A Logical–Algebraic Approach to Revising Formal Ontologies: Application in Mereotopology." AI 5, no. 2 (2024): 746–89. http://dx.doi.org/10.3390/ai5020039.

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In ontology engineering, reusing (or extending) ontologies poses a significant challenge, requiring revising their ontological commitments and ensuring accurate representation and coherent reasoning. This study aims to address two main objectives. Firstly, it seeks to develop a methodological approach supporting ontology extension practices. Secondly, it aims to demonstrate its feasibility by applying the approach to the case of extending qualitative spatial reasoning (QSR) theories. Key questions involve effectively interpreting spatial extensions while maintaining consistency. The framework systematically analyzes extensions of formal ontologies, providing a reconstruction of a qualitative calculus. Reconstructed qualitative calculus demonstrates improved interpretative capabilities and reasoning accuracy. The research underscores the importance of methodological approaches when extending formal ontologies, with spatial interpretation serving as a valuable case study.

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Vita, Randi J., James A. Overton, Kei-Hoi Cheung, et al. "Formal representation of immunology related data with ontologies." Journal of Immunology 202, no. 1_Supplement (2019): 130.26. http://dx.doi.org/10.4049/jimmunol.202.supp.130.26.

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Abstract The Human Immunology Project Consortium (HIPC) is a multicenter collaboration between research centers performing large-scale human immunology studies that focus on profiling the human immune response to natural infection and vaccination. “Immune exposures” are events such as natural infection and vaccination whereby the immune system may or may not respond to the exposure. Many of the HIPC studies investigate the response of specific cell populations after a variety of immune exposures. In order to cross-compare results from the many different centers and projects, we established a standardized representation of immune exposures and cell descriptions that simplifies data collection. By standardizing how this data is collected and stored, the vast amount of data collected by these diverse projects is made significantly more useful and interoperable. The data collected by the HIPC projects is stored in the National Institute of Health, Division of Allergy, Immunology and Transplantation funded resource, the Immunology Database and Analysis Portal (ImmPort). ImmPort was modified to provide the necessary structured data fields to capture our standardized representation of immune exposures and studied cell populations with a set of data fields that primarily utilize formal ontology terms. We will discuss the process of modeling immune exposures and cell populations via ontology terms, including real life scenarios from HIPC projects, as well as collaborations with existing ontology projects in order to meet the specific needs of immunologists.

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Čiukšys, Donatas, and Albertas Čaplinskas. "Role of formal ontologies in modern information system engineering." Lietuvos matematikos rinkinys 43 (December 22, 2003): 199–203. http://dx.doi.org/10.15388/lmr.2003.32400.

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Paper discusses the importance of role formal ontologies play in information system engineering, analyses different conceptions of formal ontology, analyses relations of conceptualization, ontology and domain theory, gives formal definition of conceptualization as intensional semantic structure. Definition of formal ontology, acceptable in field of information system engineering, is proposed.

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de Rozario, Richard. "Matching a Trope Ontology to the Basic Formal Ontology." Philosophies 4, no. 3 (2019): 40. http://dx.doi.org/10.3390/philosophies4030040.

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Applied ontology, at the foundational level, is as much philosophy as engineering and as such provides a different aspect of contemporary natural philosophy. A prominent foundational ontology in this field is the Basic Formal Ontology (BFO). It is important for lesser known ontologies, like the trope ontology of interest here, to match to BFO because BFO acts like the glue between many disparate ontologies. Moreover, such matchings provide philosophical insight into ontologies. As such, the core research question here is how we can match a trope ontology to BFO (which is based on universals) and what insights such a matching provides for foundational ontology. This article provides a logical matching, starting with BFO’s top entities (continuants and occurrences) and identifies key ontological issues that arise, such as whether universals and mereological sums are equivalent. This article concludes with general observations about the matching, including that matching to universals is generally straightforward, but not so much the matching between relations. In particular, the treatment of occurrences as causal chains is different in the trope ontology, compared to BFO’s use of time arguments.

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Stenzhorn, Holger, Stefan Schulz, Martin Boeker, and Barry Smith. "Adapting Clinical Ontologies in Real-World Environments." JUCS - Journal of Universal Computer Science 14, no. (22) (2008): 3767–80. https://doi.org/10.3217/jucs-014-22-3767.

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The desideratum of semantic interoperability has been intensively discussed in medical informatics circles in recent years. Originally, experts assumed that this issue could be sufficiently addressed by insisting simply on the application of shared clinical terminologies or clinical information models. However, the use of the term 'ontology' has been steadily increasing more recently. We discuss criteria for distinguishing clinical ontologies from clinical terminologies and information models. Then, we briefly present the role clinical ontologies play in two multicentric research projects. Finally, we discuss the interactions between these different kinds of knowledge representation artifacts and the stakeholders involved in developing interoperational real-world clinical applications. We provide ontology engineering examples from two EU-funded projects.

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Ozaki, Ana. "Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?" KI - Künstliche Intelligenz 34, no. 3 (2020): 317–27. http://dx.doi.org/10.1007/s13218-020-00656-9.

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Abstract The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies.

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Lord, Phillip, and Robert Stevens. "ISMB 2003 Bio-ontologies SIG and Sixth Annual Bio-ontologies Meeting Report." Comparative and Functional Genomics 4, no. 6 (2003): 663–66. http://dx.doi.org/10.1002/cfg.339.

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The Annual Bio-Ontologies meeting (http://www.cs.man.ac.uk/˜stevens/meeting03/) has now been running for 6 consecutive years, as a special interest group (SIG) of the much larger ISMB conference. It met in Brisbane, Australia, this summer, the first time it was held outside North America or Europe. The bio-ontologies meeting is 1 day long and normally has around 100 attendees. This year there were many fewer, no doubt a result of the distance, global politics and SARS. The meeting consisted of a series of 30 min talks with no formal peer review or publication. Talks ranged in style from fairly formal and complete pieces of work, through works in progress, to the very informal and discursive. Each year's meeting has a theme and this year it was ‘ontologies, and text processing’. There is a tendency for those submitting talks to ignore the theme completely, but this year's theme obviously struck a chord, as half the programme was about ontologies and text analysis (http://www.cs.man.ac.uk/˜stevensr/meeting03/programme.html). Despite the smaller size of the meeting, the programme was particularly strong this year, meaning that the tension between allowing time for the many excellent talks, discussion and questions from the floor was particular keenly felt. A happy problem to have!

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Szostak, Rick. "Advances in Classification Research Online 2013 Classification, Ontology, and the Semantic Web." Advances in Classification Research Online 24, no. 1 (2014): 30. http://dx.doi.org/10.7152/acro.v24i1.14674.

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The Semantic Web is developing slowly, but arguably surely. Two inter-related sources of delay are network effects and ontologies. The Semantic Web has come over time to rely onformal ontologies but there are many of these and they are each hard to master. The ability to link databases is compromised by the use of incompatible ontologies. But the RDF triplet format at the centre of the Semantic Web insists only on triplets of the form (object) (predicate orproperty) (subject). This paper explores the potential for a classification system that contains these three types of hierarchies (things, predicates, properties), plus a minimal set of rules on how they can be combined, to serve the needsof the Semantic Web. To this end, it surveys theroles (both the intended roles and side-effects) that formal ontologies play within the Semantic Web. The paper also briefly reviews the challenges faced in applying existing classification systems or thesauri to the Semantic Web.

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Daradkeh, Yousef Ibrahim, and Iryna Tvoroshenko. "Application of an Improved Formal Model of the Hybrid Development of Ontologies in Complex Information Systems." Applied Sciences 10, no. 19 (2020): 6777. http://dx.doi.org/10.3390/app10196777.

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Ontologies in artificial intelligence systems are an effective way to represent and integrate knowledge and data. The property of such structures is that any subject area is accurately described in formal language. There is a problem in the research and determination of the adequacy of ontologies under development. The perspective directions are model construction for the development of fuzzy ontologies and also the creation of methods for evaluating adequacy. The achieved results allow one to implement the processes of supporting the development and integration of ontologies of complex systems on the basis of intelligent approaches. The method is proposed to solve the problem of alternative representation and the integration of knowledge and data in artificial intelligence systems. The methodology of improving the model of the hybrid development of fuzzy ontologies is described here; it provides the preliminary modification of models of extensive and intensive progress of ontologies in space and time. The identified features of fuzzy ontology processing allow us to create a procedure for finding and eliminating inadequacies. The software implementation of the application for the integration and presentation of heterogeneous data is carried out. The consumption of Random Access Memory (RAM) for the proposed models is analyzed. The further perspectives of the proposed research are determined in accordance with the principles of classification.

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Charnine, M. M., and S. S. Kalinin. "Natural Language Processing Tools for Predictive Modeling of Advanced Trends in Formal Ontologies in Biomedical Sciences." SibScript 26, no. 4 (2024): 567–75. http://dx.doi.org/10.21603/sibscript-2024-26-4-567-575.

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Natural language processing methods can be used to predict advanced application trends in formal ontologies. Formal ontologies help to formalize the characteristics of objects in various domains. As a result, machine learning programs identify patterns and relationships between these characteristics. The article describes an experiment based on machine learning methods in combination with text search methods. It involves the CatBoost algorithm for predictive modeling and clustering of lexical items. The vector models of the corresponding items reflect a trend in a particular domain of knowledge; proximity between them was calculated based on the idea of semantic distance. The experiment revealed four advanced areas for formal ontologies, i.e., genotype – phenotype; personalization; clustering algorithms, and collaborative task management. Each area that represented the predictable trends of development in this particular domain was provided with keywords. The article also contains a review of most popular scientific articles on these trends.

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Uschold, Mike, and Michael Gruninger. "Ontologies: principles, methods and applications." Knowledge Engineering Review 11, no. 2 (1996): 93–136. http://dx.doi.org/10.1017/s0269888900007797.

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AbstractThis paper is intended to serve as a comprehensive introduction to the emerging field concerned with the design and use of ontologies. We observe that disparate backgrounds, languages, tools and techniques are a major barrier to effective communication among people, organisations and/or software understanding (i.e. an “ontology”) in a given subject area, can improve such communication, which in turn, can give rise to greater reuse and sharing, inter-operability, and more reliable software. After motivating their need, we clarify just what ontologies are and what purpose they serve. We outline a methodology for developing and evaluating ontologies, first discussing informal techniques, concerning such issues as scoping, handling ambiguity, reaching agreement and producing definitions. We then consider the benefits and describe, a more formal approach. We re-visit the scoping phase, and discuss the role of formal languages and techniques in the specification, implementation and evalution of ontologies. Finally, we review the state of the art and practice in this emerging field, considering various case studies, software tools for ontology development, key research issues and future prospects.

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Nickles, Matthias. "Social acquisition of ontologies from communication processes." Applied Ontology: An Interdisciplinary Journal of Ontological Analysis and Conceptual Modeling 2, no. 3-4 (2007): 373–97. https://doi.org/10.3233/apo-2007-040.

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This work introduces a formal framework for the social acquisition of ontologies which are constructed dynamically from overhearing the possibly conflicting symbolic interaction of autonomous information sources, and an approach to the pragmatics of communicated ontological axioms. Technically, the framework is based on distributed variants of description logic for the formal contextualization of statements w.r.t. their respective provenance, speaker's attitude, addressees, and subjective degree of confidence. Doing so, our approach demarcates from the dominating more or less informal approaches to context and provenance representation on the semantic web, and carefully distinguishes between communication attitudes such as public assertion and intention exhibited on the (semantic) web on the one hand, and mental attitudes such as private belief on the other. Furthermore, our framework provides formal means for the probabilistic fusion of controversial opinions, and presents a semantics of information publishing acts. Our approach provides an incomplex and genuinely social approach to knowledge acquisition and representation, and is thus expected to be widely applicable in fields such as the semantic web and social software, and possibly also in other distributed environments such as P2P systems.

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DRAGALINA-CHERNAYA, ELENA. "THE VARIETY OF INVARIANCE IN FORMAL AND REGIONAL ONTOLOGIES." HORIZON / Fenomenologicheskie issledovanija/ STUDIEN ZUR PHÄNOMENOLOGIE / STUDIES IN PHENOMENOLOGY / ÉTUDES PHÉNOMÉNOLOGIQUES 13, no. 1 (2024): 15–32. http://dx.doi.org/10.21638/2226-5260-2024-13-1-15-32.

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The paper examines the invariance principles proposed by the analytical and phenomenological traditions for demarcating the boundaries of formal and regional ontologies. The principle of invariance with respect to isomorphic transformations, generalizing Alfred Tarski’s criterion for logical concepts, is extended to formal ontology as the theory of manifolds in its phenomenological interpretation. Isomorphism types, which are abstract individuals of the highest order, hypostases of forms of all possible ontologies, are considered as model-theoretical analogs of manifolds. The correlativity of the phenomenological principles of demarcation of ontological regions and the criterion of invariance with respect to isomorphic transformations is demonstrated, leading to the convergence of logic and formal mathematics in both traditions and, at the same time, to the exclusion of geometry from formal ontology. Particular attention is paid to the discussion of the analytical and phenomenological traditions of the synthetic (material) a priori and the contribution that Wittgenstein’s doctrine on internal relations at different stages of its evolution makes to this discussion. The paper reveals the basis for later Wittgenstein’s criticism of his earlier project of creating a phenomenological language for expressing internal regional relations (such as the colours exclusion). The paper shows how Wittgenstein’s doubts about the possibility of an ideal notation based on the dichotomy of the logical and phenomenological led him to the study of invariants arising in language games. These invariants are not determined by the special properties of the categorical objects of formal ontology or the structures of regional subject matter, but are stable equilibria generated by the “consensus of actions.” New perspectives are outlined that are opened for logic and phenomenology by switching focus from the invariants of ontological structures to the invariants of structured interactions of various agents.

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Priya, M., and Aswani Kumar Ch. "A novel method for merging academic social network ontologies using formal concept analysis and hybrid semantic similarity measure." Library Hi Tech 38, no. 2 (2019): 399–419. http://dx.doi.org/10.1108/lht-02-2019-0035.

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Purpose The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure. Design/methodology/approach The HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part. Findings The experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain. Practical implications In this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers. Originality/value The HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

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Ofiсerov, V. P., and S. V. Smirnov. "FUZZY FORMAL CONCEPT ANALYSIS IN THE CONSTRUCTION OF ONTOLOGIES." Ontology of Designing 26, no. 7 (2017): 487–95. http://dx.doi.org/10.18287/2223-9537-2017-7-4-487-495.

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Schwitter, Rolf. "CREATING AND QUERYING FORMAL ONTOLOGIES VIA CONTROLLED NATURAL LANGUAGE." Applied Artificial Intelligence 24, no. 1-2 (2010): 149–74. http://dx.doi.org/10.1080/08839510903448700.

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Schulz, Stefan, and Udo Hahn. "Part-whole representation and reasoning in formal biomedical ontologies." Artificial Intelligence in Medicine 34, no. 3 (2005): 179–200. http://dx.doi.org/10.1016/j.artmed.2004.11.005.

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Alruqimi, Mohammed, and Noura Aknin. "Enabling social WEB for IoT inducing ontologies from social tagging." International Journal of Informatics and Communication Technology (IJ-ICT) 8, no. 1 (2019): 19. http://dx.doi.org/10.11591/ijict.v8i1.pp19-24.

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Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.

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MARTÍNEZ-CARRERAS, M. ANTONIA, ANDRÉS MUÑOZ, JUAN BOTÍA, and ANTONIO F. GÓMEZ-SKARMETA. "CREATING CONTEXT-AWARE COLLABORATIVE WORKING ENVIRONMENTS." International Journal on Artificial Intelligence Tools 20, no. 01 (2011): 195–207. http://dx.doi.org/10.1142/s0218213011000085.

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Context-aware systems are intended for providing services adapted to the needs of people, by taking into account their state and the information related to their environment. One alternative to represent this context information resides in the use of Semantic Web ontologies. They provide a formal vocabulary which allows to easily express and share knowledge. Additionally, several types of automatic knowledge manipulation and reasoning processes become available thanks to the formal features of such ontologies. The inclusion of context information through ontologies in Collaborative Working Environments (CWEs) may bring important benefits to team work inside an organization, such as an automatic selection between different collaborative services according to the team members' preferences and their current state. This paper describes the design and implementation of a context-reasoning system which has been integrated into a CWE architecture to take advantage of context-awareness.

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Boeker, M., H. Stenzhorn, J. Niggemann, and S. Schulz. "Granularity Issues in the Alignment of Upper Ontologies." Methods of Information in Medicine 48, no. 02 (2009): 184–89. http://dx.doi.org/10.3414/me9221.

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Summary Objectives: The application of upper ontologies has been repeatedly advocated for to support the interoperability between different domain ontologies for facilitating the shared use of data within and across disciplines. BioTop is an upper domain ontology that aims at aligning more specialized biomolecular and biomedical ontologies. The integration of BioTop and the upper ontology Basic Formal Ontology (BFO) is the objective of this study. Methods: BFO was manually integrated into BioTop, observing both its free text and formal definitions. BioTop classes were attached to BFO classes as children and BFO classes were reused in the formal definitions of BioTop classes. A description logics reasoner was used to check the logical consistency of this integration. The domain adequacy was checked manually by domain experts. Results: Logical inconsistencies were found by the reasoner when applying the BFO classes for fiat and aggregated objects in some of the BioTop class definitions. We discovered that the definition of those particular classes in BFO was dependent on the notion of physical connectedness. Hence we suggest ignoring a BFO subbranch in order not to hinder cross-granularity integration. Conclusion: Without introducing a more sophisticated theory of granularity, the described problems cannot be properly dealt with. Whereas we argue that an upper ontology should be granularity-independent, we illustrate how granularity-dependent domain ontologies can still be embedded into the framework of BioTop in combination with BFO.

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Otte, J. Neil, John Beverley, and Alan Ruttenberg. "BFO: Basic Formal Ontology1." Applied Ontology 17, no. 1 (2022): 17–43. http://dx.doi.org/10.3233/ao-220262.

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Basic Formal Ontology (BFO) is a top-level ontology consisting of thirty-six classes, designed to support information integration, retrieval, and analysis across all domains of scientific investigation, presently employed in over 350 ontology projects around the world. BFO is a genuine top-level ontology, containing no terms particular to material domains, such as physics, medicine, or psychology. In this paper, we demonstrate how a series of cases illustrating common types of change may be represented by universals, defined classes, and relations employing the BFO framework. We provide discussion of these cases to provide a template for other ontologists using BFO, as well as to facilitate comparison with the strategies proposed by ontologists using different top-level ontologies.

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Guizzardi, Giancarlo. "Ontology, Ontologies and the “I” of FAIR." Data Intelligence 2, no. 1-2 (2020): 181–91. http://dx.doi.org/10.1162/dint_a_00040.

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According to the FAIR guiding principles, one of the central attributes for maximizing the added value of information artifacts is interoperability. In this paper, I discuss the importance, and propose a characterization of the notion of Semantic Interoperability. Moreover, I show that a direct consequence of this view is that Semantic Interoperability cannot be achieved without the support of, on one hand, (i) ontologies, as meaning contracts capturing the conceptualizations represented in information artifacts and, on the other hand, of (ii) Ontology, as a discipline proposing formal meth- ods and theories for clarifying these conceptualizations and articulating their representations. In particular, I discuss the fundamental role of formal ontological theories (in the latter sense) to properly ground the construction of representation languages, as well as methodological and computational tools for supporting the engineering of ontologies (in the former sense) in the context of FAIR.

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Mohammed, Alruqimi, and Aknin Noura. "Enabling social web for IoT inducing ontologies from social tagging." International Journal of Informatics and Communication Technology (IJ-ICT) 8, no. 1 (2019): 19–24. https://doi.org/10.11591/ijict.v8i1.pp19-24.

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Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.

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Rodríguez-González, Alejandro, Ángel García-Crespo, Ricardo Colomo-Palacios, Juan Miguel Gómez-Berbís, and Enrique Jiménez-Domingo. "Using Ontologies in Drug Prescription." International Journal of Knowledge-Based Organizations 1, no. 4 (2011): 1–15. http://dx.doi.org/10.4018/ijkbo.2011100101.

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Medical prescription has been touted as following an accurate approach to addressing particular health problems. However, the importance of the process might demand considering a formal knowledge-driven procedure to ensure its correctness which can be achieved through Medical Decision Support Systems (MDSS). Semantic Technologies have emerged as a potential silver bullet to become the backbone of those particular Information Systems since it provides seamless integration and an underlying logical formalism. This paper sheds light into using ontologies for drug prescription through the SemMed model, architecture and proof-of-concept implementation, being able to face challenges in these areas and solve day-to-day problems of health professionals in terms of drug prescription

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H. Alkhammash, Eman. "Graphical Transformation of OWL Ontologies to Event-B Formal Models." Computers, Materials & Continua 70, no. 2 (2022): 3733–50. http://dx.doi.org/10.32604/cmc.2022.015987.

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Sicilia, Miguel-Ángel, and Elena García Barriocanal. "On the Convergence of Formal Ontologies and Standardized E-Learning." International Journal of Distance Education Technologies 3, no. 2 (2005): 13–29. http://dx.doi.org/10.4018/jdet.2005040102.

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Hagedorn, Thomas J., Barry Smith, Sundar Krishnamurty, and Ian Grosse. "Interoperability of disparate engineering domain ontologies using basic formal ontology." Journal of Engineering Design 30, no. 10-12 (2019): 625–54. http://dx.doi.org/10.1080/09544828.2019.1630805.

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Dotsika, Fefie. "Uniting formal and informal descriptive power: Reconciling ontologies with folksonomies." International Journal of Information Management 29, no. 5 (2009): 407–15. http://dx.doi.org/10.1016/j.ijinfomgt.2009.02.002.

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Batres, Rafael, and Suriati Akmal. "A Formal Concept Analysis-Based Method for Developing Process Ontologies." JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 46, no. 6 (2013): 396–406. http://dx.doi.org/10.1252/jcej.12we278.

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Hacherouf, Mokhtaria, Safia Nait-Bahloul, and Christophe Cruz. "Transforming XML schemas into OWL ontologies using formal concept analysis." Software & Systems Modeling 18, no. 3 (2018): 2093–110. http://dx.doi.org/10.1007/s10270-017-0651-4.

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