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Journal articles on the topic 'Ontology based information retrieval'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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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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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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Chen, Yuye. "Intelligent information retrieval based on Ontology." Frontiers in Computing and Intelligent Systems 2, no. 1 (November 30, 2022): 123–25. http://dx.doi.org/10.54097/fcis.v2i1.3181.

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Traditional search engines can no longer meet the increasing amount of information in today's world, and often face problems such as blurred results and long search times for some queries. And the addition of ontology makes information retrieval more intelligent, so that information data between various fields can be shared, to achieve the purpose of getting the answers users want quickly and accurately. This paper firstly introduces the definition of ontology and analyzes the traditional information retrieval system, and finally summarizes some experimental results in recent years in ontology-based intelligent information retrieval and reviews them to get a more efficient and accurate information retrieval system to achieve the effect of improving the accuracy of search engines.

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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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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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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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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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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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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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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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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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14

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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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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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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17

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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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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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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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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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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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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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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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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Huang, Gang, Xiu Ying Wu, and Man Yuan. "Information Integration System Based on Ontology." Applied Mechanics and Materials 533 (February 2014): 444–47. http://dx.doi.org/10.4028/www.scientific.net/amm.533.444.

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This paper studies Ontology-based information integration system and its implementation methods, the use of XML and RDF semantic description of the content of the information, so that these data are no longer just for the line search, this simple retrieval methods do not take full advantage of the information content the potential of the machine to understand the basis of the information content, the application can be completed more intelligent reasoning queries.

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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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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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Shen, Qi, and Meng Zhang. "A Semantic Retrieval Method Based on Ontology." Advanced Materials Research 989-994 (July 2014): 2179–83. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2179.

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Semantic retrieval method stands at the crossroads between Natural Language Processing and Machine Intelligent. This paper makes analysis on the semantic search method and research on concept similarity algorithm, and discusses the factor of weight’s influence on concept similarity as well. On this basis, this paper proposed a new semantic search method based on ontology, and apply it to the tourism information retrieval, which intellectualized tourism information retrieval service.

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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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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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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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Hayashi, Victor, Mateus Carvalho, João Carlos Néto, Felipe Pinna, Rosangela Marquesone, Wilson Ruggiero, and Maisa Duarte. "Information Retrieval Based on Brazilian Portuguese Texts." Journal of Systemics, Cybernetics and Informatics 20, no. 1 (January 2022): 249–69. http://dx.doi.org/10.54808/jsci.20.01.249.

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Knowledge-based intelligent systems might be used in the banking sector to automate customer service. One of the ways to represent knowledge that is both understandable by humans and readable by machines is by using ontologies. Whenever a customer queries its bank regarding specific products or services, the existing knowledge modeled in an ontology might be used by a customer service chatbot to answer it in an automated way. The existing manual information retrieval process from banking specialists is laborious and time-consuming. Specialists use natural language, visual representations, and common sense, often overlooking details. It is a great challenge to make a specialist's knowledge explicit, formal, precise, and completely scalable, which is the format required by a customer service chatbot. We propose a semi-automatic approach to retrieving banking information in Brazilian Portuguese texts with minimal specialist support. By combining Natural Language Processing techniques (e.g., syntactic analysis to obtain the logical meaning of sentences based on rules and its structure) and an ontology constructor library, it was possible to build a tool that receives texts from the banking domain and constructs an ontology that knowledge-based intelligent systems can use. Specialist support is only needed in intermediate refinement steps, thus optimizing the banking specialist's time. The use cases for investments, opening a banking account, and the comparison of the proposed approach show how we reduced manual labor in the information retrieval process by a factor of 40%. Our approach can identify more information in each sentence compared to a similar method found in the literature. The resulting ontologies can be used in a chatbot that automates customer support for a large Brazilian bank.

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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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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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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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Visutsak, Porawat. "Ontology-Based Semantic Retrieval for Durian Pests and Diseases Control System." International Journal of Machine Learning and Computing 11, no. 1 (January 2021): 92–97. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1019.

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In Southeast Asia, durian is affectionately called the king of fruit. Durian is the most popular crop planted in eastern and southern of Thailand. The total crop is around 600,000 tons per year; among this, 500,000 tons of the total production were exported worldwide. In Thailand, the knowledge of durian production is based on experience from generation to generation, especially the knowledge of durian pests and diseases control. This paper presents the ontology knowledge based for durian pests and diseases retrieval system. The major contributions of the system consist of 1) the stored knowledge of durian pests and diseases and 2) the diagnosis of durian diseases and the suggestions for the treatments. The ontology knowledge consists of 8 main classes: 1) diseases, 2) pests, 3) cultivars, 4) symptoms of bunch, 5) leaf area symptoms, 6) symptoms of the branches and trunk, 7) symptoms of fruit, and 8) symptoms of root and growth. The experimental results yielded 100% of precision, 88.33% of recall, and 93.8% of overall performance.

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Mahaboob, S., Prathyusha Kanakam, D. Suryanarayana, Swathi Gunnam, and Sharmela S. "Semantic Information Retrieval: An Ontology and RDF-based Model." International Journal of Computer Applications 156, no. 9 (December 15, 2016): 34–38. http://dx.doi.org/10.5120/ijca2016912575.

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Jain, Vishal, and Mayank Singh. "Ontology Based Information Retrieval in Semantic Web: A Survey." International Journal of Information Technology and Computer Science 5, no. 10 (September 1, 2013): 62–69. http://dx.doi.org/10.5815/ijitcs.2013.10.06.

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ShivajiMule, Komal, and Arti Waghmare. "Information Retrieval Techniques based on Ontology for High Effectiveness." International Journal of Computer Applications 118, no. 6 (May 20, 2015): 8–11. http://dx.doi.org/10.5120/20748-3138.

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Shi, Lei, and Rossitza Setchi. "Enhanced semantic representation for improved ontology-based information retrieval." International Journal of Knowledge-based and Intelligent Engineering Systems 17, no. 2 (May 13, 2013): 127–36. http://dx.doi.org/10.3233/kes-130258.

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Velu, Anitha, and Menakadevi Thangavelu. "Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval." Computers, Materials & Continua 70, no. 3 (2022): 4707–24. http://dx.doi.org/10.32604/cmc.2022.020095.

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Mony, Manju, Jyothi M. Rao, and Manish M. Potey. "Semantic Search based on Ontology Alignment for Information Retrieval." International Journal of Computer Applications 107, no. 10 (December 18, 2014): 25–33. http://dx.doi.org/10.5120/18789-0125.

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LI, Qing-mao, Xing-jiang YANG, Xiang-bing ZHOU, and Hong-jiang MA. "Research on topic maps-based ontology information retrieval model." Journal of Computer Applications 30, no. 1 (March 12, 2010): 240–42. http://dx.doi.org/10.3724/sp.j.1087.2010.00240.

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Hu, Jun, Xinzhou Lu, and Chun Guan. "A Semantic Information Retrieval Approach Based on Rough Ontology." Open Cybernetics & Systemics Journal 8, no. 1 (December 31, 2014): 399–404. http://dx.doi.org/10.2174/1874110x01408010399.

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OGURE, Takuya, and Kazuo FURUTA. "SOCIAL IMPLEMENTATION OF ONTOLOGY-BASED DOMAIN-ORIENTED INFORMATION RETRIEVAL." SOCIOTECHNICA 5 (2008): 206–15. http://dx.doi.org/10.3392/sociotechnica.5.206.

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Yu, Yangxin, Liuyang Wang, and Quanyin Zhu. "Intelligent fuzzy information retrieval based on ontology knowledge-base." International Journal of Internet Protocol Technology 11, no. 3 (2018): 180. http://dx.doi.org/10.1504/ijipt.2018.094534.

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Zhu, Quanyin, Yangxin Yu, and Liuyang Wang. "Intelligent fuzzy information retrieval based on ontology knowledge-base." International Journal of Internet Protocol Technology 11, no. 3 (2018): 180. http://dx.doi.org/10.1504/ijipt.2018.10015712.

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Sim, K. M., and P. T. Wong. "Toward Agency and Ontology for Web-Based Information Retrieval." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 34, no. 3 (August 2004): 257–69. http://dx.doi.org/10.1109/tsmcc.2004.829322.

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