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Journal articles on the topic 'Ontology and information retrieval'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Shen, Jin Xing. "Ontology-Based Semantic Retrieval for Management Information System." Applied Mechanics and Materials 278-280 (January 2013): 2069–72. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.2069.

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In order to achieve semantic retrieval for scientific research information in WWW, this paper applies an ontology-based framework to information retrieval system for management information system. After analyze the limitations of traditional method, bring a semantic search forward, and mainly introduce the thought of the semantic retrieval as well as the way to constitute ontology entity and the language that describes it. Moreover, semantic retrieval system based on ontology is also given. The application to retrieve project information shows that the framework can overcome the localization of other ontology’s models, and this research facilitates the semantic retrieval of management information through semantic retrieval concepts on the Semantic Web.

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J, Naren, Raja Rajeswari D, Nikhith Sannidhi, and Vithya G. "An Investigation on Ontology Based Fuzzy Semantic Information Retrieval." International Journal of Psychosocial Rehabilitation 23, no. 1 (February 20, 2019): 377–84. http://dx.doi.org/10.37200/ijpr/v23i1/pr190248.

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T, Padmavathi. "Comparative Analysis of Information Retrieval using Ontology Based vs Traditional Information Systems in Food Science Domain." DESIDOC Journal of Library & Information Technology 40, no. 02 (March 28, 2020): 437–44. http://dx.doi.org/10.14429/djlit.40.02.15213.

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The current methods of searching and information retrieval are imprecise, often yielding results in tens of thousands of web pages. Extraction of the actual information needed often requires extensive manual browsing of retrieved documents. In order to address these drawbacks, this paper introduces an implementation in the field of food science of the ontology-based information retrieval system, and comparison is made with conventional information systems. The ontology of Food Semantic Web Knowledge Base (FSWKB) was built using the Protégé framework which supports two main models of ontology through the editors Protégé-Frames and Protégé-OWL. The FSWKB is composed of two heterogeneous ontologies, and these are merged and processed on a separate server application making use of the Apache Jena Fuseki an SPARQL server offering SPARQL endpoint. The experimental results indicated that ontology-based information systems are more effective in terms of their retrieval capability compared to the more conventional information retrieval systems. The retrieval effectiveness was measured in terms of precision and recall. The results of the work showed that traditional search results in average precision and recall levels of 0.92 and 0.18. The ontology-based test for precision and recall has average rates of 0.96 and 0.97.

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Jain, Sumit, and C. S. Bhatia C.S.Bhatia. "An Approach For Information Retrieval For Bookstores Using Formal Ontology." Indian Journal of Applied Research 1, no. 5 (October 1, 2011): 185–87. http://dx.doi.org/10.15373/2249555x/feb2012/69.

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Ji, Zhi Gang. "A Novel Information Organization Method Based on Ontology for Unstructured Information." Key Engineering Materials 439-440 (June 2010): 1042–47. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.1042.

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The paper analyzes the mode and method for unstructured information, compares the similarities and differences between ontology and vocabulary and taxonomy, and discusses the position and the function of ontology for unstructured information from the two aspects of standard system of semantic net and the concept characteristic of ontology. Based on the ontologies DB, the annotation of unstructured information is obtained. Information retrieval which consists of concept-matched retrieval and rule-based reasoning retrieval is proposed with the help of information annotation. The experiment results show that rule-based retrieval has better recall and shorter retrieval time than fact-based retrieval and relationship-based retrieval.

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S. Laddha, Shilpa, and Dr Pradip M. Jawandhiya. "Onto Semantic Tourism Information Retrieval." International Journal of Engineering & Technology 7, no. 4.7 (September 27, 2018): 148. http://dx.doi.org/10.14419/ijet.v7i4.7.20532.

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Semantic Search is an area of research which focuses on meaning of terms used in user query. Ontology plays significant role to define the concept and the relationship of terms in domain. Since the understanding of concepts is domain specific, Ontology creation is also domain specific. According to this argument, query interpreted in Tourism domain can have different meaning in some other domain. This paper presents a prototype of information retrieval interface using ontology which can save users time by rendering relevant, precise and efficient search results as compared to traditional search interfaces.

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Lutz, M., and E. Klien. "Ontology‐based retrieval of geographic information." International Journal of Geographical Information Science 20, no. 3 (March 2006): 233–60. http://dx.doi.org/10.1080/13658810500287107.

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Pruski, Cédric, Nicolas Guelfi, and Chantal Reynaud. "Adaptive Ontology-Based Web Information Retrieval." International Journal of Web Portals 3, no. 3 (July 2011): 41–58. http://dx.doi.org/10.4018/ijwp.2011070104.

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Finding relevant information on the Web is difficult for most users. Although Web search applications are improving, they must be more “intelligent” to adapt to the search domains targeted by queries, the evolution of these domains, and users’ characteristics. In this paper, the authors present the TARGET framework for Web Information Retrieval. The proposed approach relies on the use of ontologies of a particular nature, called adaptive ontologies, for representing both the search domain and a user’s profile. Unlike existing approaches on ontologies, the authors make adaptive ontologies adapt semi-automatically to the evolution of the modeled domain. The ontologies and their properties are exploited for domain specific Web search purposes. The authors propose graph-based data structures for enriching Web data in semantics, as well as define an automatic query expansion technique to adapt a query to users’ real needs. The enriched query is evaluated on the previously defined graph-based data structures representing a set of Web pages returned by a usual search engine in order to extract the most relevant information according to user needs. The overall TARGET framework is formalized using first-order logic and fully tool supported.

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Jimeno-Yepes, Antonio, Rafael Berlanga-Llavori, and Dietrich Rebholz-Schuhmann. "Ontology refinement for improved information retrieval." Information Processing & Management 46, no. 4 (July 2010): 426–35. http://dx.doi.org/10.1016/j.ipm.2009.05.008.

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Kang, Jin Cui, and Jing Long Gao. "Application of Ontology Technology in Agricultural Information Retrieval." Advanced Materials Research 756-759 (September 2013): 1249–53. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1249.

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The agricultural information on the internet become more and more, it is very difficult to search accurate related information from such different information, in order to improve the efficiency of information retrieval on the internet, the intelligent searching technology of agricultural information based on ontology is proposed. The paper firstly introduces research on the agricultural ontology and information retrieval, and takes agriculture domain knowledge as research object, analyzes the characters of agricultural domain knowledge and semantics retrieval, then uses the agricultural ontology to make the structure of agriculture ontology knowledge, and constructs the related agricultural knowledge ontology and knowledge base, implementing the intelligent searching of the agricultural information. The results indicate that the application of agricultural ontology technology in the agricultural information retrieval not only achieves the intelligent retrieval of agricultural information, but also greatly improves the accuracy and reliability of agricultural information retrieval.

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Zhu, Hong Mei, Yong Quan Liang, Qi Jia Tian, and Shu Juan Ji. "Agricultural Policy-Oriented Ontology-Based Semantic Information Retrieval." Key Engineering Materials 439-440 (June 2010): 572–76. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.572.

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Research on architecture of ontology-based information semantic representation and Retrieval is done. As a case study, a prototype for agricultural policy-oriented ontology-based semantic information retrieval system (APOSIRS) is established. Ontology plays a role that providing a shared terminology and supporting for the retrieval process. The architecture allows APOSIRS-based applications to perform automatic semantic information Retrieval of agricultural policy text at more length: automatic and dynamic semantic annotation of unstructured and semi-structured content, semantically-enabled information extraction, indexing, retrieval, as well as ontology management, such as querying and modifying the underlying ontology and knowledge bases. Main components of this architecture have been implemented and their results are reported.

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Mamatha, Ch, Dr V. Anandam, Priyadarshini Chatterjee, and Hepshiba Vijaya Kumari. "Attribute Based Image Retrieval and Segmentation using On-tological Approaches." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 2018): 103. http://dx.doi.org/10.14419/ijet.v7i4.6.20440.

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Content based image retrieval is gaining more and more importance as it is an apt approach to retrieve an image. The image is retrieved based on certain texture. Ontology is a branch of Meta Physics that helps in analyzing an input image based on certain textures. Ontology helps to retrieve an image based on its properties. Ontology describes a domain. With that domain, we can proceed further to understand the relation between the features present in the domain. There are biological-ontologies to analyze biological outcomes. The field of information technology can be combined with biological ontology to study the results of different biological effects. With the systematic concept of ontology that includes rules, classes, relations etc we can understand an image better that eventually helps in accurate image retrieval. Ontology can be generic or domain specific. In this paper we will be using domain specific ontology used to analyze the features of digital images along with image segmentation to retrieve an image. We will be testing our proposed system using the colored images of mammals. In case of image segmentation we will using the general techniques already existing.

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Liang, Jun Feng, Chun Jin, Lei Zhang, and Xu Ning Liu. "Research of Agricultural Information Management and Retrieval." Advanced Materials Research 989-994 (July 2014): 5630–33. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.5630.

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In order to improve the efficiency of agricultural information retrieval and provide the effect methods for the information retrieval of agricultural, the intelligent searching technology of agricultural information based on ontology is proposed. The paper firstly introduces the concept of ontology, analyzes the characters of agricultural knowledge, and constructs the related agricultural knowledge ontology and knowledge base, implementing the intelligent searching of the agricultural information. The results indicate that the research on agricultural ontology can contribute to organization and searching of agricultural scientific knowledge and provide methods for information organization and searching of agricultural knowledge.

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Lam, S. S., and Samuel P. M. Choi. "Multidimensional Ontology-Based Information Retrieval for Academic Counseling." International Journal of Systems and Service-Oriented Engineering 4, no. 3 (July 2014): 66–82. http://dx.doi.org/10.4018/ijssoe.2014070104.

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Conventional information retrieval can only locate documents containing user specified keywords. Integrating domain ontology with information retrieval extends the keyword-based search to semantic search and thus potentially improves the precision and recall of the document retrieval. In this paper, a set of new multidimensional ontology-based information retrieval algorithms is proposed for searching both specific and related terms. In particular, the relevant data properties of an instance, the relevant concepts, the relevant related concepts, and the related instances of a given user query can be identified from the domain ontology via the multidimensional search. Using the proposed algorithms, an intelligent counselling system which provides 24x7 online academic counselling services is developed. Through an interactive user-interface and domain ontology, the system facilitates students to find desired information by reviewing and refining their query. The article also outlines how to enable ontology-based searching for a conventional website.

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Lou, Wen, and Junping Qiu. "Semantic information retrieval research based on co-occurrence analysis." Online Information Review 38, no. 1 (January 8, 2014): 4–23. http://dx.doi.org/10.1108/oir-11-2012-0203.

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Purpose – The paper aims to develop a new method for potential relations retrieval. It aims to find common aspects between co-occurrence analysis and ontology to build a model of semantic information retrieval based on co-occurrence analysis. Design/methodology/approach – This paper used a literature review, co-occurrence analysis, ontology build and other methods to design a model and process of semantic information retrieval based on co-occurrence analysis. Archaeological data from Wuhan University Library's bibliographic retrieval systems was used for experimental analysis. Findings – The literature review found that semantic information retrieval research mainly concentrates on ontology-based query techniques, semantic annotation and semantic relation retrieval. Moreover most recent systems can only achieve obvious relations retrieval. Ontology and co-occurrence analysis have strong similarities in theoretical ideas, data types, expressions, and applications. Research limitations/implications – The experiment data came from a Chinese university which perhaps limits its usefulness elsewhere. Practical implications – This paper constructed a model to understand potential relations retrieval. An experiment proved the feasibility of co-occurrence analysis used in semantic information retrieval. Compared with traditional retrieval, semantic information retrieval based on co-occurrence analysis is more user-friendly. Originality/value – This study is one of the first to combine co-occurrence analysis with semantic information retrieval to find detailed relationships.

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E.Attia, Zeinab, Ahmed M. Gadallah, and Hesham A. Hefny. "Semantic Information Retrieval Model: Fuzzy Ontology Approach." International Journal of Computer Applications 91, no. 13 (April 18, 2014): 9–14. http://dx.doi.org/10.5120/15940-5156.

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YU, Jing, Guo-quan WU, and Yi LU. "Government information retrieval based on domain ontology." Journal of Computer Applications 30, no. 6 (June 25, 2010): 1664–67. http://dx.doi.org/10.3724/sp.j.1087.2010.01664.

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Kumar, Naveen. "Ontology based Books Information Retrieval using SPARQL." International Journal of Computer Applications 67, no. 13 (April 18, 2013): 24–27. http://dx.doi.org/10.5120/11457-7063.

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LI, ZHANJUN, and KARTHIK RAMANI. "Ontology-based design information extraction and retrieval." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, no. 2 (March 19, 2007): 137–54. http://dx.doi.org/10.1017/s0890060407070199.

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Because of the increasing complexity of products and the design process, as well as the popularity of computer-aided documentation tools, the number of electronic and textual design documents being generated has exploded. The availability of such extensive document resources has created new challenges and opportunities for research. These include improving design information retrieval to achieve a more coherent environment for design exploration, learning, and reuse. One critical issue is related to the construction of a structured representation for indexing design documents that record engineers' ideas and reasoning processes for a specific design. This representation should explicitly and accurately capture the important design concepts as well as the relationships between these concepts so that engineers can locate their documents of interest with less effort. For design information retrieval, we propose to use shallow natural language processing and domain-specific design ontology to automatically construct a structured and semantics-based representation from unstructured design documents. The design concepts and relationships of the representation are recognized from the document based on the identified linguistic patterns. The recognized concepts and relationships are joined to form a concept graph. The integration of these concept graphs builds an application-specific design ontology, which can be seen as the structured representation of the content of the corporate document repository, as well as an automatically populated knowledge base from previous designs. To improve the performance of design information retrieval, we have developed ontology-based query processing, where users' requests are interpreted based on their domain-specific meanings. Our approach contrasts with the traditionally used keyword-based search. An experiment to test the retrieval performance is conducted by using the design documents from a product design scenario. The results demonstrate that our method outperforms the keyword-based search techniques. This research contributes to the development and use of engineering ontology for design information retrieval.

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Remi, S., and S. C. Varghese. "Domain Ontology Driven Fuzzy Semantic Information Retrieval." Procedia Computer Science 46 (2015): 676–81. http://dx.doi.org/10.1016/j.procs.2015.02.122.

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Sudha Ramkumar, A., and B. Poorna. "Semantic Information Retrieval Based on Domain Ontology." International Journal of Web Technology 004, no. 002 (December 14, 2015): 33–35. http://dx.doi.org/10.20894/ijwt.104.004.002.001.

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Su, Xiaomeng, and Jon Atle Gulla. "An information retrieval approach to ontology mapping." Data & Knowledge Engineering 58, no. 1 (July 2006): 47–69. http://dx.doi.org/10.1016/j.datak.2005.05.012.

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Akmal, Suriati, Li-Hsing Shih, and Rafael Batres. "Ontology-based similarity for product information retrieval." Computers in Industry 65, no. 1 (January 2014): 91–107. http://dx.doi.org/10.1016/j.compind.2013.07.011.

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Manouchehri, Sanaz, Mahdieh Mirzabeigi, and Tahere Jowkar. "Evaluating the effectiveness of Farsi-English query production using ontology: a case of scientometric ontology." Aslib Journal of Information Management 73, no. 3 (April 29, 2021): 386–405. http://dx.doi.org/10.1108/ajim-08-2020-0268.

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PurposeThis paper aims to discover the effectiveness of Farsi-English query using ontology.Design/methodology/approachThe present study is quasi-experimental. The sample consisted of 60 students and graduate and doctoral staff from Shiraz University and the Regional Center for Science and Technology. A researcher-made questionnaire was used to assess the level of English language proficiency of users, background knowledge and their level of satisfaction with search results before and after using ontology. Each user also evaluated the relevance of the top ten results on the Google search engine results page before and after using ontology.FindingsThe findings showed that the level of complexity of the task, the use of ontology, the interactive effect of the level of complexity of the task with the domain knowledge of the users, and the interactive effect of the level of complexity of the task with ontology, influence the effectiveness of retrieval results from the users' point of view. The results of the present study also showed that the level of complexity of the task, the use of ontology, and the interactive effect of the level of complexity of the task and the use of ontology, affect the level of user satisfaction.Originality/valueThe results of this research are significant in both theoretical and practical aspects. Theoretically, given the lack of research in which the interactive effect of the use of ontology has examined the level of complexity of tasks and domain knowledge of users, the present study can be considered as an attempt to improve information retrieval systems. From a practical point of view, the results of this research will help researchers and designers of information retrieval systems to understand that the use of ontologies can be used to retrieve information and improve the query and assess the needs of users and their satisfaction in this field, and ultimately, making the information retrieval process more effective.

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Wang, Jin Hao, Chao Ying Yang, Guang Qi Mu, and Dan Tan. "Ontology and Multi-Agent Based Intelligent Retrieval Model for Power Quality Monitoring Information." Applied Mechanics and Materials 599-601 (August 2014): 1016–20. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1016.

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A power quality monitoring information intelligent retrieval model based on ontology and multi-Agent is proposed. Combining ontology technology with multi-Agent technology, establish a four layer alliance structure of the intelligent retrieval model. At first, the function of the model for each layer has been describled in detail. Then, the concretre building method of the power quality ontology knowledge base which is the core part of the model.has been explained.Finally,esxmple analysis show the whole intelligent retrieval process.Each layer of the model uses agent alliance mode, according with the demand of intelligent retrieval; Realizing the user's retrieval request with the aid of ontology knowledge base can not only improve retrieval efficiency, but also realize deep mining of the basic retrieval information. Example analysis shows that the model can realize intelligent retrieval quickly and efficiently and have certain practical value.

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Yin, Jia Fu, Juan Suo, and Dan Tan. "Ontology and Multi-Agent Based Intelligent Retrieval Model for Power Quality Monitoring Information." Applied Mechanics and Materials 574 (July 2014): 436–44. http://dx.doi.org/10.4028/www.scientific.net/amm.574.436.

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A power quality monitoring information intelligent retrieval model based on ontology and multi-Agent is proposed. Combining ontology technology with multi-Agent technology, establish a four layer alliance structure of the intelligent retrieval model. At first, the function of the model for each layer has been described in detail. Then, the concrete building method of the power quality ontology knowledge base which is the core part of the model has been explained. Finally, example analysis shows the whole intelligent retrieval process. Each layer of the model uses agent alliance mode, according with the demand of intelligent retrieval; realizing the user's retrieval request with the aid of ontology knowledge base can not only improve retrieval efficiency, but also realize deep mining of the basic retrieval information. Example analysis shows that the model can realize intelligent retrieval quickly and efficiently and have certain practical value.

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Zhang, Shu Dong, and Yan Chen. "Research on Domain Ontology-Based Intelligent Information Retrieval System." Key Engineering Materials 460-461 (January 2011): 300–304. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.300.

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Domain ontology introduces a new theory and method for information retrieval. In this paper, we analyze the deficiencies of traditional information retrieval and explore the relationship between domain ontology and information retrieval, as well as the basic design ideas of information retrieval based on domain ontology. Finally we present a domain ontology-based intelligent information retrieval system, so that the information retrieval can be promoted from the keyword level to the semantic level. With the rapid development of the national economy and the growth of information resources, traditional methods relying on the browser, database fields, keyword matching, or even manual retrieval query has become increasingly difficult to meet people's information retrieval needs. How to quickly and accurately identify the needed information resources has become a urgent question in front of us. Information retrieval is a technology which can find out the relevant information the user needs from a collection of large amounts of information. It has experienced manual retrieval, computer retrieval stage, now it has developed to the network and intelligent stage. The objects of information retrieval extend from a relative closed, stable and consistent, centrally managed information content by an independent database to an open, dynamic, quickly update, widely distributed, and loosely managed web content; the users of information retrieval also spread from professional intelligence agent to the common including government officials, businessmen, managers, teachers, students, professionals, etc. They ask for the higher and more diverse requirements from the results to the manner of information retrieval. Adapting to the need for network, intelligence and personalization is a new trend of information retrieval technology.

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Uthayan, K. R., and G. S. Anandha Mala. "Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System." Scientific World Journal 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/414910.

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Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology.

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Yang, Huan Hai, and Ming Yu Sun. "Study on Application of Domain Ontology in Semantic Information Retrieval." Applied Mechanics and Materials 433-435 (October 2013): 1662–65. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1662.

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Considering weakness of the traditional retrieval method based on keyword matching, the paper introduced semantic into information retrieval, and proposed a semantic retrieval model based on ontology. The paper offered a construction method of domain ontology and implemented semantic reasoning using Jena and improved a semantic similarity calculation method.

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Abburu, Sunitha, and Nitant Dube. "Satellite Parametric Description to Ontology Concepts and Semantic Classification of Satellite Data." International Journal on Semantic Web and Information Systems 12, no. 2 (April 2016): 53–75. http://dx.doi.org/10.4018/ijswis.2016040103.

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Current satellite data retrieval systems retrieves data using latitude, longitude, date, time and sensor parameters like wind, cloud etc. To achieve concept based satellite data retrieval like Storm, Hurricane, Overcast and Frost etc., requires ontological concept descriptions using satellite observation parameters and concept based classification of satellite data. The current research work has designed and implemented a two phase methodology to achieve this. The phase 1 defines ontology concepts through satellite observation parameters and phase 2 describes ontology concept based satellite data classification. The efficiency of the methodology is been tested by taking the Kalpana satellite data from MOSDAC and weather ontology. This achieves concept based retrieval of satellite data, application interoperability and strengthen the ontologies. The current methodology is implemented and results in concept based satellite data classification, storage and retrieval.

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Hourali, Maryam, and Gholam Ali Montazer. "An Intelligent Information Retrieval Approach Based on Two Degrees of Uncertainty Fuzzy Ontology." Advances in Fuzzy Systems 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/683976.

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In spite of the voluminous studies in the field of intelligent retrieval systems, effective retrieving of information has been remained an important unsolved problem. Implementations of different conceptual knowledge in the information retrieval process such as ontology have been considered as a solution to enhance the quality of results. Furthermore, the conceptual formalism supported by typical ontology may not be sufficient to represent uncertainty information due to the lack of clear-cut boundaries between concepts of the domains. To tackle this type of problems, one possible solution is to insert fuzzy logic into ontology construction process. In this article, a novel approach for fuzzy ontology generation with two uncertainty degrees is proposed. Hence, by implementing linguistic variables, uncertainty level in domain's concepts (Software Maintenance Engineering (SME) domain) has been modeled, and ontology relations have been modeled by fuzzy theory consequently. Then, we combined these uncertain models and proposed a new ontology with two degrees of uncertainty both in concept expression and relation expression. The generated fuzzy ontology was implemented for expansion of initial user's queries in SME domain. Experimental results showed that the proposed model has better overall retrieval performance comparing to keyword-based or crisp ontology-based retrieval systems.

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Boonchom, Vi-sit, and Nuanwan Soonthornphisaj. "ATOB algorithm: an automatic ontology construction for Thai legal sentences retrieval." Journal of Information Science 38, no. 1 (November 21, 2011): 37–51. http://dx.doi.org/10.1177/0165551511426249.

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Ontology plays an important role in knowledge representation, especially in the domain of information retrieval. However, building ontology remains a challenging problem because it is a time-consuming task for experts. To overcome these drawbacks, we propose a novel approach called the Automatic Thai Legal Ontology Building (ATOB) algorithm for automatic legal ontology building and to improve the court sentences retrieval process. The ATOB can automatically generate seed ontology and expand the ontology using Thai legal terminology, i.e. TLlexicon. The expansion process is terminated automatically by the threshold parameter. Moreover, the ATOB applies the concept of the ant colony algorithm to improve the court sentences retrieval process. We conclude that the effective ontology should be weight-embedded. The empirical results demonstrate that the performance of the ATOB algorithm is better than that of the traditional search method. The performance figures for the ATOB framework measured in terms of precision, recall, F-measure and diversity are 0.90, 0.91, 0.90 and 0.39, respectively.

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M. P, Navale, Rohini Kasar, Sneha Kasbe, and Priyanka Shahane. "Ontology Based Information Retrieval System using Multiple Query." IJARCCE 5, no. 12 (December 30, 2016): 298–99. http://dx.doi.org/10.17148/ijarcce.2016.51268.

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CHEN, Hua-cheng, Xue-hui DU, Xing-yuan CHEN, and Chun-tao XIA. "Document sensitive information retrieval based on interest ontology." Journal of Computer Applications 32, no. 11 (May 27, 2013): 3030–33. http://dx.doi.org/10.3724/sp.j.1087.2012.03030.

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Nagarajan, G., and R. I. Minu. "Fuzzy Ontology Based Multi-Modal Semantic Information Retrieval." Procedia Computer Science 48 (2015): 101–6. http://dx.doi.org/10.1016/j.procs.2015.04.157.

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Fernández, Miriam, Iván Cantador, Vanesa López, David Vallet, Pablo Castells, and Enrico Motta. "Semantically enhanced Information Retrieval: An ontology-based approach." Journal of Web Semantics 9, no. 4 (December 2011): 434–52. http://dx.doi.org/10.1016/j.websem.2010.11.003.

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Rezgui, Yacine. "Ontology-Centered Knowledge Management Using Information Retrieval Techniques." Journal of Computing in Civil Engineering 20, no. 4 (July 2006): 261–70. http://dx.doi.org/10.1061/(asce)0887-3801(2006)20:4(261).

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Rajasurya, Swathi. "Semantic Information Retrieval Using Ontology in University Domain." International journal of Web & Semantic Technology 3, no. 4 (October 31, 2012): 55–68. http://dx.doi.org/10.5121/ijwest.2012.3406.

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Hung, Chihli, Chih‐Fong Tsai, Shin‐Yuan Hung, and Chang‐Jiang Ku. "OGIR: an ontology‐based grid information retrieval framework." Online Information Review 36, no. 6 (November 23, 2012): 807–27. http://dx.doi.org/10.1108/14684521211287909.

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Banu, W. Aisha, P. Sheik Abdul Khad, and R. Shriram. "Information Retrieval through Mobile Devices using Semantic Ontology." Information Technology Journal 10, no. 9 (August 15, 2011): 1747–53. http://dx.doi.org/10.3923/itj.2011.1747.1753.

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Xing, Zimeng, Lina Wang, Wenbo Xing, Yongjun Ren, Tao Li, and Jinyue Xia. "Application of Ontology in the Web Information Retrieval." Journal on Big Data 1, no. 2 (2019): 79–88. http://dx.doi.org/10.32604/jbd.2019.05806.

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Chien, Been-Chian, Chih-Hung Hu, and Ming-Yi Ju. "ONTOLOGY-BASED INFORMATION RETRIEVAL USING FUZZY CONCEPT DOCUMENTATION." Cybernetics and Systems 41, no. 1 (January 26, 2010): 4–16. http://dx.doi.org/10.1080/01969720903408565.

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Magdalenic, Ivan, Danijel Radosevic, and Zoran Skocir. "Dynamic Generation of Web Services for Data Retrieval Using Ontology." Informatica 20, no. 3 (January 1, 2009): 397–416. http://dx.doi.org/10.15388/informatica.2009.257.

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Liang, Ting Ting, and Chun Qing Li. "Intelligent Science of Knowledge Retrieval System Model Based on Ontology." Applied Mechanics and Materials 707 (December 2014): 441–44. http://dx.doi.org/10.4028/www.scientific.net/amm.707.441.

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Compared with the traditional information retrieval, knowledge retrieval based on ontology has higher retrieval efficiency. In order to adequate arena for the effectiveness of knowledge,and intelligence to meet the users on the implicit and explicit knowledge retrieval demand, the author designed a system model of intelligent science based on ontology. Detailed analysis and elaboration of the three aspects: subject knowledge ontology construction, ontology knowledge retrieval, service based on intelligent. Provide methodological guidance and technical support to enrich the research contents in the field of knowledge retrieval.

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Lu, Lei, and Feng Zhang. "Research on IETM Based on Ontology Integration Framework." Applied Mechanics and Materials 190-191 (July 2012): 395–98. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.395.

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Ontology library is one of the cores of the intelligent IETM system; it is widely used in intelligent information retrieve, e-business, and network collaboration. Information retrieval and graphical representation of follow-up operation should be based on the ontology, many applications require the integrated query from related information resources, which arises the problem of semantic interoperation in heterogeneous information resources. There are many complex factors in integrating them, because different ontology creators adopt different semantics from his individual view. This paper proposes the framework of ontology integration though analyzing the problems in the process of ontology integration. The application results show that the proposed cycle evolution of ontology construction method to solve the IETM domain ontology building and storage problems.

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Ayalew, Yirsaw, Barbara Moeng, and Gontlafetse Mosweunyane. "Experimental evaluation of ontology-based HIV/AIDS frequently asked question retrieval system." Health Informatics Journal 25, no. 4 (May 23, 2018): 1434–50. http://dx.doi.org/10.1177/1460458218775147.

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This study presents the results of experimental evaluations of an ontology-based frequently asked question retrieval system in the domain of HIV and AIDS. The main purpose of the system is to provide answers to questions on HIV/AIDS using ontology. To evaluate the effectiveness of the frequently asked question retrieval system, we conducted two experiments. The first experiment focused on the evaluation of the quality of the ontology we developed using the OQuaRE evaluation framework which is based on software quality metrics and metrics designed for ontology quality evaluation. The second experiment focused on evaluating the effectiveness of the ontology in retrieving relevant answers. For this we used an open-source information retrieval platform, Terrier, with retrieval models BM25 and PL2. For the measurement of performance, we used the measures mean average precision, mean reciprocal rank, and precision at 5. The results suggest that frequently asked question retrieval with ontology is more effective than frequently asked question retrieval without ontology in the domain of HIV/AIDS.

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Afuan, Lasmedi, Ahmad Ashari, and Yohanes Suyanto. "A New Approach in Query Expansion Methods for Improving Information Retrieval." JUITA: Jurnal Informatika 9, no. 1 (May 22, 2021): 93. http://dx.doi.org/10.30595/juita.v9i1.9657.

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This research develops a new approach to query expansion by integrating Association Rules (AR) and Ontology. In the proposed approach, there are several steps to expand the query, namely (1) the document retrieval step; (2) the step of query expansion using AR; (3) the step of query expansion using Ontology. In the initial step, the system retrieved the top documents via the user's initial query. Next is the initial processing step (stopword removal, POS Tagging, TF-IDF). Then do a Frequent Itemset (FI) search from the list of terms generated from the previous step using FP-Growth. The association rules search by using the results of FI. The output from the AR step expanded using Ontology. The results of the expansion with Ontology use as new queries. The dataset used is a collection of learning documents. Ten queries used for the testing, the test results are measured by three measuring devices, namely recall, precision, and f-measure. Based on testing and analysis results, integrating AR and Ontology can increase the relevance of documents with the value of recall, precision, and f-measure by 87.28, 79.07, and 82.85.

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Khurana, Dhiraj, and Cheshta Diwan. "GOVT_EXAM ONTOLOGY IN SEMANTIC WEB FOR QUERY RETRIEVAL." BSSS journal of computer 12, no. 1 (June 30, 2021): 22–29. http://dx.doi.org/10.51767/jc1203.

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The World wide web is growing day by day exponentially and it becomes difficult to find the relevant information even using efficient search engines. Search results always give lots of irrelevant data which often misled to actual needed information. One solution of this problem is semantic web which was proposed by Sir Tim Berner’s Lee. Semantic web is an extended version of World Wide Web that has opened the new doors for efficient information retrieval process. Ontology is one of the methodologies to implement the semantic web. In this paper govt exam ontology is created with the help of protégé ontology creation tool. In the given ontology various relationship between different govt exams of central and state level is provided and also the general information about these exams like exam type, subject level of exam and institutes for coaching is given. This domain specific ontology can help the potential information seeker to get the accurate information which can help to crack the exam.

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Zhai, Jun, Meng Li, and Pan Sun. "Knowledge Modeling and Semantic Retrieval for Sports Information Based on Ontology." Advanced Materials Research 187 (February 2011): 45–50. http://dx.doi.org/10.4028/www.scientific.net/amr.187.45.

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Ontology, with the excellent concept hierarchy and appropriately supporting for logic reasoning, is the basis of knowledge modeling on the Semantic Web and the effective tool for semantic retrieval. This paper pays attention to the sports information management in WWW. Firstly, based on the domain ontology model, we build the sports ontology which has the character of wide-cover and small information granularity. Then this paper present the semantic retrieval system for sports information using SPARQL query language, which realizes the intelligent retrieval at semantic level according to the relations of “synonymy of”, “kind of” and “part of” between sports concepts. For the trend of exploding increase of sports information, this research has practical significance to some extent.

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Guo, Kehua, and Shigeng Zhang. "A Semantic Medical Multimedia Retrieval Approach Using Ontology Information Hiding." Computational and Mathematical Methods in Medicine 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/407917.

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Searching useful information from unstructured medical multimedia data has been a difficult problem in information retrieval. This paper reports an effective semantic medical multimedia retrieval approach which can reflect the users’ query intent. Firstly, semantic annotations will be given to the multimedia documents in the medical multimedia database. Secondly, the ontology that represented semantic information will be hidden in the head of the multimedia documents. The main innovations of this approach are cross-type retrieval support and semantic information preservation. Experimental results indicate a good precision and efficiency of our approach for medical multimedia retrieval in comparison with some traditional approaches.

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