Relevant bibliographies by topics / Ontology and information retrieval

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Ontology and information retrieval'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Ontology and information retrieval"

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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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Dissertations / Theses on the topic "Ontology and information retrieval"

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Tomassen, Stein L. "Conceptual Ontology Enrichment for Web Information Retrieval." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14270.

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Searching for information on the Web can be frustrating. One of the reasons is the ambiguity of words. The work presented in this thesis concentrates on how the effectiveness of standard information retrieval systems can be enhanced with semantic technologies like ontologies. Ontologies are knowledge models that can represent knowledge of any universe of discourse by describing how concepts of a domain are related. Creating and maintaining ontologies can be tedious and costly. However, we focus on reusing ontologies, rather than engineering, and on their applicability to improve the retrieval effectiveness of existing search systems. The aim of this work is to find an effective approach for applying ontologies to existing search systems. The basic idea is that these ontologies can be used to tackle the problem of ambiguous words and hence improve the retrieval effectiveness. Our approach to semantic search builds on feature vectors (FV). The basic idea is to connect the (standardised) domain terminology encoded in an ontology to the actual terminology used in a text corpus. Therefore, we propose to associate every ontology entity (classes and individuals are called entities in this work) with a FV that is tailored to the actual terminology used in a text corpus like the Web. These FVs are created off-line and later used on-line to filter (i.e. to disambiguate search) and re-rank the search results from an underlying search system. This pragmatic approach is applicable to existing search systems since it only depends on extending the query and presentation components, in other words there is no need to alter either the indexing or the ranking components of the existing systems. A set of experiments have been carried out and the results report on improvement by more than 10%. Furthermore, we have shown that the approach is neither dependent on highly specific queries nor on a collection comprised only of relevant documents. In addition, we have shown that the FVs are relatively persistent, i.e. little maintenance of the FVs is required. In this work, we focus on the creation and evaluation of these feature vectors. As a result, a part of the contribution of this work is a framework for the construction of FVs. Furthermore, we have proposed a set of metrics to measure the quality of the created FVs. We have also provided a set of guidelines for optimal construction of feature vectors for different categories of ontologies.
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Jimeno, Yepes Antonio José. "Ontology refinement for improved information retrieval in the biomedical domain." Doctoral thesis, Universitat Jaume I, 2009. http://hdl.handle.net/10803/384552.

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Este trabajo de tesis doctoral se centra en el uso de ontologías de dominio y su refinamiento enfocado a la recuperación de la información. El dominio seleccionado ha sido el de la Biomedicina, que dispone de una extensa colección de resúmenes en la base de datos Medline y recursos que facilitan la creación de ontologías muy extensas, tales como MeSH o UMLS. En este trabajo se ha desarrollado también un modelo de formulación de consulta que permite relacionar un modelo de documento con una ontología dentro de los modelos de lenguaje. Además hemos desarrollado un algoritmo que permite mejorar la ontología para la tarea de recuperación de la información a partir de recursos no estructurados. Los resultados muestran que el refinamiento de las ontologías aplicado a la recuperación de la información mejora el rendimiento, identificando automáticamente información no presente en la ontología. Además hemos comprobado que el tipo de contenido relevante para las consultas depende de propiedades relacionadas con el tipo de consulta y la colección de documentos. Los resultados están acordes con resultados existentes en el campo de la recuperación de la información.
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Mehalingam, Senthilkumar. "Ontology based code generation for datalogger." Diss., Online access via UMI:, 2006.

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Fischer, Wolf [Verfasser], and Bernhard [Akademischer Betreuer] Bauer. "Linguistically Motivated Ontology-Based Information Retrieval / Wolf Fischer. Betreuer: Bernhard Bauer." Augsburg : Universität Augsburg, 2013. http://d-nb.info/1077702795/34.

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Bhogal, Jagdev. "Investigating ontology based query expansion using a probabilistic retrieval model." Thesis, City University London, 2011. http://openaccess.city.ac.uk/2946/.

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This research briefly outlines the problems of traditional information retrieval systems and discusses the different approaches to inferring context in document retrieval. By context we mean word disambiguation which is achieved by exploring the generalisation-specialisation hierarchies within a given ontology. Specifically, we examine the use of ontology based query expansion for defining query context. Query expansion can be done in many ways and in this work we consider the use of relevance feedback and pseudo-relevance feedback for query expansion. We examine relevance feedback and pseudo-relevance to ascertain the existence of performance differences between relevance feedback and pseudo-relevance feedback. The information retrieval system used is based on the probabilistic retrieval model and the query expansion method is extended using information from a news domain ontology. The aim of this project is to assess the impact of the use of the ontology on the query expansion results. Our results show that ontology based query expansion has resulted in a higher number of relevant documents being retrieved compared to the standard relevance feedback process. Overall, ontology based query expansion improves recall but does not produce any significant improvements for the precision results. Pseudo-relevance feedback has achieved better results than relevance feedback. We also found that reducing or increasing the relevance feedback parameters (number of terms or number of documents) does not correlate with the results. When comparing the effect of varying the number of terms parameter with the number of documents parameter, the former benefits the pseudo-relevance feedback results but the latter has an additional effect on the relevance feedback results. There are many factors which influence the success of ontology based query expansion. The thesis discusses these factors and gives some guidelines on using ontologies for the purpose of query expansion.
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Chang, Jia Kang. "Investigation on applying modular ontology to statistical language model for information retrieval." Thesis, University of Central Lancashire, 2015. http://clok.uclan.ac.uk/11803/.

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The objective of this research is to provide a novel approach to improving retrieval performance by exploiting Ontology with the statistical language model (SLM). The proposed methods consist of two major processes, namely ontology-based query expansion (OQE) and ontology-based document classification (ODC). Research experiments have required development of an independent search tool that can combine the OQE and ODC in a traditional SLM-based information retrieval (IR) process using a Web document collection. This research considers the ongoing challenges of modular ontology enhanced SLM-based search and addresses three contribution aspects. The first concerns how to apply modular ontology to query expansion, in a bespoke language model search tool (LMST). The second considers how to incorporate OQE with the language model to improve the search performance. The third examines how to manipulate such semantic-based document classification to improve the smoothing accuracy. The role of ontology in the research is to provide formally described domains of interest that serve as context, to enhance system query effectiveness.
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Wang, Xinkai. "Chinese-English cross-lingual information retrieval in biomedicine using ontology-based query expansion." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/chineseenglish-crosslingual-information-retrieval-in-biomedicine-using-ontologybased-query-expansion(1b7443d3-3baf-402b-83bb-f45e78876404).html.

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In this thesis, we propose a new approach to Chinese-English Biomedical cross-lingual information retrieval (CLIR) using query expansion based on the eCMeSH Tree, a Chinese-English ontology extended from the Chinese Medical Subject Headings (CMeSH) Tree. The CMeSH Tree is not designed for information retrieval (IR), since it only includes heading terms and has no term weighting scheme for these terms. Therefore, we design an algorithm, which employs a rule-based parsing technique combined with the C-value term extraction algorithm and a filtering technique based on mutual information, to extract Chinese synonyms for the corresponding heading terms. We also develop a term-weighting mechanism. Following the hierarchical structure of CMeSH, we extend the CMeSH Tree to the eCMeSH Tree with synonymous terms and their weights. We propose an algorithm to implement CLIR using the eCMeSH Tree terms to expand queries. In order to evaluate the retrieval improvements obtained from our approach, the results of the query expansion based on the eCMeSH Tree are individually compared with the results of the experiments of query expansion using the CMeSH Tree terms, query expansion using pseudo-relevance feedback, and document translation. We also evaluate the combinations of these three approaches. This study also investigates the factors which affect the CLIR performance, including a stemming algorithm, retrieval models, and word segmentation.
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Chartrand, Tim. "Ontology-based extraction of RDF data from the World Wide Web /." Diss., CLICK HERE for online access, 2003. http://contentdm.lib.byu.edu/ETD/image/etd168.pdf.

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Modica, Giovanni. "A framework for automatic ontology generation from autonomous web applications." Master's thesis, Mississippi State : Mississippi State University, 2002. http://library.msstate.edu/etd/show.asp?etd=etd-09032002-165210.

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Ngo, Duy Hoa. "Enhancing Ontology Matching by Using Machine Learning, Graph Matching and Information Retrieval Techniques." Thesis, Montpellier 2, 2012. http://www.theses.fr/2012MON20096/document.

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Ces dernières années, les ontologies ont suscité de nombreux travaux dans le domaine du web sémantique. Elles sont utilisées pour fournir le vocabulaire sémantique permettant de rendre la connaissance du domaine disponible pour l'échange et l'interprétation au travers des systèmes d'information. Toutefois, en raison de la nature décentralisée du web sémantique, les ontologies sont très hétérogènes. Cette hétérogénéité provoque le problème de la variation de sens ou ambiguïté dans l'interprétation des entités et, par conséquent, elle empêche le partage des connaissances du domaine. L'alignement d'ontologies, qui a pour but la découverte des correspondances sémantiques entre des ontologies, devient une tâche cruciale pour résoudre ce problème d'hétérogénéité dans les applications du web sémantique. Les principaux défis dans le domaine de l'alignement d'ontologies ont été décrits dans des études récentes. Parmi eux, la sélection de mesures de similarité appropriées ainsi que le réglage de la configuration de leur combinaison sont connus pour être des problèmes fondamentaux que la communauté doit traiter. En outre, la vérification de la cohérence sémantique des correspondances est connue pour être une tâche importante. Par ailleurs, la difficulté du problème augmente avec la taille des ontologies. Pour faire face à ces défis, nous proposons dans cette thèse une nouvelle approche, qui combine différentes techniques issues des domaines de l'apprentissage automatique, d'appariement de graphes et de recherche d'information en vue d'améliorer la qualité de l'alignement d'ontologies. En effet, nous utilisons des techniques de recherche d'information pour concevoir de nouvelles mesures de similarité efficaces afin de comparer les étiquettes et les profils d'entités de contexte au niveau des entités. Nous appliquons également une méthode d'appariement de graphes appelée propagation de similarité au niveau de la structure qui découvre effectivement des correspondances en exploitant des informations structurelles des entités. Pour combiner les mesures de similarité au niveau des entités, nous transformons la tâche de l'alignement d'ontologie en une tâche de classification de l'apprentissage automatique. Par ailleurs, nous proposons une méthode dynamique de la somme pondérée pour combiner automatiquement les correspondances obtenues au niveau des entités et celles obtenues au niveau de la structure. Afin d'écarter les correspondances incohérentes, nous avons conçu une nouvelle méthode de filtrage sémantique. Enfin, pour traiter le problème de l'alignement d'ontologies à large échelle, nous proposons deux méthodes de sélection des candidats pour réduire l'espace de calcul.Toutes ces contributions ont été mises en œuvre dans un prototype nommé YAM++. Pour évaluer notre approche, nous avons utilisé des données du banc d'essai de la compétition OAEI : Benchmark, Conference, Multifarm, Anatomy, Library and Large Biomedical Ontologies. Les résultats expérimentaux montrent que les méthodes proposées sont très efficaces. De plus, en comparaison avec les autres participants à la compétition OAEI, YAM++ a montré sa compétitivité et a acquis une position de haut rang
In recent years, ontologies have attracted a lot of attention in the Computer Science community, especially in the Semantic Web field. They serve as explicit conceptual knowledge models and provide the semantic vocabularies that make domain knowledge available for exchange and interpretation among information systems. However, due to the decentralized nature of the semantic web, ontologies are highlyheterogeneous. This heterogeneity mainly causes the problem of variation in meaning or ambiguity in entity interpretation and, consequently, it prevents domain knowledge sharing. Therefore, ontology matching, which discovers correspondences between semantically related entities of ontologies, becomes a crucial task in semantic web applications.Several challenges to the field of ontology matching have been outlined in recent research. Among them, selection of the appropriate similarity measures as well as configuration tuning of their combination are known as fundamental issues that the community should deal with. In addition, verifying the semantic coherent of the discovered alignment is also known as a crucial task. Furthermore, the difficulty of the problem grows with the size of the ontologies. To deal with these challenges, in this thesis, we propose a novel matching approach, which combines different techniques coming from the fields of machine learning, graph matching and information retrieval in order to enhance the ontology matching quality. Indeed, we make use of information retrieval techniques to design new effective similarity measures for comparing labels and context profiles of entities at element level. We also apply a graph matching method named similarity propagation at structure level that effectively discovers mappings by exploring structural information of entities in the input ontologies. In terms of combination similarity measures at element level, we transform the ontology matching task into a classification task in machine learning. Besides, we propose a dynamic weighted sum method to automatically combine the matching results obtained from the element and structure level matchers. In order to remove inconsistent mappings, we design a new fast semantic filtering method. Finally, to deal with large scale ontology matching task, we propose two candidate selection methods to reduce computational space.All these contributions have been implemented in a prototype named YAM++. To evaluate our approach, we adopt various tracks namely Benchmark, Conference, Multifarm, Anatomy, Library and Large BiomedicalOntologies from the OAEI campaign. The experimental results show that the proposed matching methods work effectively. Moreover, in comparison to other participants in OAEI campaigns, YAM++ showed to be highly competitive and gained a high ranking position
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Books on the topic "Ontology and information retrieval"

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Perspectives on ontology learning. Amsterdam, Netherlands]: IOS Press, 2014.

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Schade, Sven. Ontology-driven translation of geospatial data. Heidelberg: AKA, 2010.

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Ontology engineering in a networked world. Berlin: Springer, 2012.

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Suárez-Figueroa, Mari Carmen. Ontology engineering in a networked world. Berlin: Springer, 2012.

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Casellas, Núria. Legal ontology engineering: Methodologies, modelling trends, and the ontology of professional judicial knowledge. Dordrecht: Springer, 2011.

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Anupama, Mallik, and Ghosh Hiranmay, eds. Multimedia ontology: Representation and applications. Boca Raton, Florida: CRC Press, 2016.

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Applied ontology: An introduction. Frankfurt: Ontos Verlag, 2008.

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Gargouri, Faiez. Ontology theory, management and design: Advanced tools and models. Hershey, PA: Information Science Reference, 2010.

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Lim, Edward H. Y. Knowledge Seeker - Ontology Modelling for Information Search and Management: A Compendium. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Ontology learning and knowledge discovery using the Web: Challenges and recent advances. Hershey, PA: Information Science Reference, 2011.

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Book chapters on the topic "Ontology and information retrieval"

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Noah, Shahrul Azman, Nor Afni Raziah Alias, Nurul Aida Osman, Zuraidah Abdullah, Nazlia Omar, Yazrina Yahya, and Maryati Mohd Yusof. "Ontology-Driven Semantic Digital Library." In Information Retrieval Technology, 141–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17187-1_13.

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Lipani, Aldo, Florina Piroi, Linda Andersson, and Allan Hanbury. "An Information Retrieval Ontology for Information Retrieval Nanopublications." In Lecture Notes in Computer Science, 44–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11382-1_5.

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Taheri, Aynaz, and Mehrnoush Shamsfard. "Mapping FarsNet to Suggested Upper Merged Ontology." In Information Retrieval Technology, 604–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25631-8_55.

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Park, Dae-Won, Hyoung-Sam Heo, Hyuk-Chul Kwon, and Hea-Young Chung. "Protein Function Classification Based on Gene Ontology." In Information Retrieval Technology, 691–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11562382_69.

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Lim, Edward H. Y., James N. K. Liu, and Raymond S. T. Lee. "Text Information Retrieval." In Knowledge Seeker - Ontology Modelling for Information Search and Management, 27–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17916-7_3.

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Chan, Ki, and Wai Lam. "Gene Ontology Classification of Biomedical Literatures Using Context Association." In Information Retrieval Technology, 552–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11562382_49.

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Lee, Seungkeun, Sehoon Lee, Kiwook Lim, and Junghyun Lee. "The Design of Webservices Framework Support Ontology Based Dynamic Service Composition." In Information Retrieval Technology, 721–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11562382_74.

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Vallet, David, Miriam Fernández, and Pablo Castells. "An Ontology-Based Information Retrieval Model." In Lecture Notes in Computer Science, 455–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11431053_31.

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Tomassen, Stein L. "Research on Ontology-Driven Information Retrieval." In On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops, 1460–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11915072_50.

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Mousselly-Sergieh, Hatem, and Rainer Unland. "IROM: Information Retrieval-Based Ontology Matching." In Semantic Multimedia, 127–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23017-2_9.

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Conference papers on the topic "Ontology and information retrieval"

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Kindle, C. "Ontology-supported Information Retrieval." In EUROCON 2005 - The International Conference on "Computer as a Tool". IEEE, 2005. http://dx.doi.org/10.1109/eurcon.2005.1630148.

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Yim, Sungshik, and David Rosen. "Case-Based Retrieval Approach of Supporting Process Planning in Layer-Based Additive Manufacturing." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35309.

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The process planning task for a given design problem in additive manufacturing can be greatly enhanced by referencing previously developed process plans. In this research, a case-based retrieval method, called the DFM (Design For Manufacturing) framework, that retrieves previously formulated process plans is proposed to support process planning. To support the DFM Framework, we have developed an information model (ontology) of manufacturing process knowledge in the domain of additive manufacturing processes, including design requirements, process plans, and rules that map requirements to plans. Description Logic (DL) is identified as an appropriate mathematical formalism to encode the ontology and realize the computational mapping between the design and manufacturing domains. Storage and retrieval algorithms are presented that, first, structure the repository of previous DFM problems and, second, enable DFM problems to be retrieved.
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Mustafa, Jibran, Sharifullah Khan, and Khalid Latif. "Ontology based semantic information retrieval." In 2008 4th International IEEE Conference "Intelligent Systems" (IS). IEEE, 2008. http://dx.doi.org/10.1109/is.2008.4670473.

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SaravanaPriya, M., and M. JananiSankari. "Information retrieval from conference ontology." In 2014 International Conference on Advanced Communication, Control and Computing Technologies (ICACCCT). IEEE, 2014. http://dx.doi.org/10.1109/icaccct.2014.7019166.

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Sonakneware, Pratibha S., and Shivkumar J. Karale. "Efficient information retrieval using domain ontology." In 2014 International Conference for Convergence of Technology (I2CT). IEEE, 2014. http://dx.doi.org/10.1109/i2ct.2014.7092229.

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Zhuhadar, Leyla, Olfa Nasraoui, and Robert Wyatt. "Visual Ontology-Based Information Retrieval System." In 2009 13th International Conference Information Visualisation, IV. IEEE, 2009. http://dx.doi.org/10.1109/iv.2009.47.

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Gao, Huiying, Jinghua Zhao, Qiuju Yin, and Jingxia Wang. "Ontology-based enterprise information retrieval model." In 2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009). IEEE, 2009. http://dx.doi.org/10.1109/gsis.2009.5408118.

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Yinghui Huang, Guanyu Li, and Qiangqiang Li. "Rough Ontology Based Semantic Information Retrieval." In 2013 6th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2013. http://dx.doi.org/10.1109/iscid.2013.23.

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Sharma, Nmmita, Aditya Khamparia, and Babita Pandey. "Ontology Based Product Information Retrieval Ecommtology." In 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT). IEEE, 2016. http://dx.doi.org/10.1109/cict.2016.130.

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Liu, Wei, Hehe Gu, Chunmin Peng, and Dayu Cheng. "Ontology-based retrieval of geographic information." In 2010 18th International Conference on Geoinformatics. IEEE, 2010. http://dx.doi.org/10.1109/geoinformatics.2010.5567612.

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Reports on the topic "Ontology and information retrieval"

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Hart, Lewis L. Components for Ontology Driven Information Push. Fort Belvoir, VA: Defense Technical Information Center, February 2005. http://dx.doi.org/10.21236/ada431001.

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Jha, Somesh, Vitaly Shmatikov, and Matthew Fredrikson. Private Information Retrieval. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada536856.

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Braun, Ronald. Ontology-Based Information Extraction from Free-Form Text. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada383044.

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Knoblock, Craig A., Yigal Arens, and Chu-Nan Hsu. Cooperating Agents for Information Retrieval. Fort Belvoir, VA: Defense Technical Information Center, May 1994. http://dx.doi.org/10.21236/ada285887.

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Hoeferlin, David M., and Stephen A. Thorn. Crosslingual Audio Information Retrieval Development. Fort Belvoir, VA: Defense Technical Information Center, April 2009. http://dx.doi.org/10.21236/ada539725.

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Newitt, L. R., G. V. Haines, and R. L. Coles. The magnetic information retrieval program. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/225655.

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Bader, Brett William, Peter Chew, Ahmed Abdelali, and Tamara Gibson Kolda. Cross-language information retrieval using PARAFAC2. Office of Scientific and Technical Information (OSTI), May 2007. http://dx.doi.org/10.2172/908061.

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Liu, Xiaoyong, and W. B. Croft. Statistical Language Modeling for Information Retrieval. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada440321.

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Franz, Martin, J. S. McCarley, and Wei-Jing Zhu. English-Chinese Information Retrieval at IBM. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada456312.

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Lynch, C. Using the Z39.50 Information Retrieval Protocol. RFC Editor, December 1994. http://dx.doi.org/10.17487/rfc1729.

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