Relevant bibliographies by topics / Ontology based 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 based information retrieval'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Ontology based 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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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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Dissertations / Theses on the topic "Ontology based information retrieval"

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Addy, Nicholas G. "Ontology driven geographic information retrieval." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/2526.

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The theory of modern information retrieval processes must be improved to meet parallel growth and efficiency in its dependent hardware architectures. The growth in data sources facilitated by hardware improvements must be conversant with parallel growth at the user end of the information retrieval paradigm, encompassing both an increasing demand for data services and a widening user base. Contemporary sources refer to such growth as three dimensional, in reference to the expected and parallel growth in the key areas of hardware processing power, demand from current users of information services and an increase in demand via an extended user base consisting of institutions and organizations who are not characteristically defined by their use of geographic information. This extended user base is expected to grow due to the demand to utilise and incorporate geographic information as part of competitive business processes, to fill the need for advertising and spatial marketing demographics. The vision of the semantic web as such is the challenge of managing integration between diverse and increasing data sources and diverse and increasing end users of information. Whilst data standardisation is one means of achieving this vision at the source end of the information flow, it is not a solution in a free market of ideas. Information in its elemental form should be accessible regardless of the domain of its creation.In an environment where the users and sources are continually growing in scope and depth, the management of data via precise and relevant information retrieval requires techniques which can integrate information seamlessly between machines and users regardless of the domain of application or data storage methods. This research is the study of a theory of geographic information structure which can be applied to all aspects of information systems development, governing at a conceptual level the representation of information to meet the requirements of inter machine operability as well as inter user operability. This research entails a thorough study of the use of ontology from theoretical definition to modern use in information systems development and retrieval, in the geographic domain. This is a study examining how the use of words to describe geographic features are elements which can form a geographic ontology and evaluates WordNet, an English language ontology in the form of a lexical database as a structure for improving geographic information recall on Gazetteers. The results of this research conclude that WordNet can be utilised to as a methodology for improving search results in geographic information retrieval processes as a source for additional query terms, but only on a narrow user domain.
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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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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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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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Deniz, Onur. "Ontology Based Text Mining In Turkish Radiology Reports." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614145/index.pdf.

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Vast amount of radiology reports are produced in hospitals. Being in free text format and having errors due to rapid production, it continuously gets more complicated for radiologists and physicians to reach meaningful information. Though application of ontologies into bio-medical text mining has gained increasing interest in recent years, less work has been offered for ontology based retrieval tasks in Turkish language. In this work, an information extraction and retrieval system based on SNOMED-CT ontology has been proposed for Turkish radiology reports. Main purpose of this work is to utilize semantic relations in ontology to improve precision and recall rates of search results in domain. Practical problems encountered such as spelling errors, segmentation and tokenization of unstructured medical reports has also been addressed during the work.
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Skovronski, John. "An ontology-based publish-subscribe framework." Diss., Online access via UMI:, 2006.

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Ismail, Muhammad, and Attuallah Jan. "Context-based supply of documents in a healthcare process." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH. Forskningsmiljö Informationsteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-18513.

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The more enhanced and reliable healthcare facilities, depend partly on accumulated organizational knowledge. Ontology and semantic web are the key factors in long-term sustainability towards the improvement of patient treatment process. Generally, researchers have the common consensus that knowledge is hard to capture due to its implicit nature, making it hard to manage. Medical professionals spend more time on getting the right information at the right moment, which is already available on intranet/internet. Evaluating the literature is controversial but interesting debates on ontology and semantic web encouraged us to propose a method and 4-Tier Architecture for retrieving context-based document according to user’s information in healthcare organization. Medical professionals are facing problems to access relevant information and documents for performing different tasks in the patient-treatment process. We have focused to provide context-based retrieval of documents for medical professionals by developing a semantic web solution. We also developed different OWL ontology models, which are mainly used for semantic tagging in web pages and generating context to retrieve the relevant web page documents. In addition, we developed a prototype to testify our findings in health care sector with the goal of retrieving relevant documents in a practical manner.
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Kubilay, Mustafa. "Special Index And Retrieval Mechanism For Ontology Based Medical Domain Search Engines." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/2/12606470/index.pdf.

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This thesis focuses on index and retrieval mechanism of an ontology based medical domain search engine. First, indexing techniques and retrieval methods are reviewed. Then, a special indexing and retrieval mechanism are introduced. This thesis also specifies the functional requirements of these mechanisms. Finally, an evaluation is given by indicating the positive and negative aspects of mechanisms.
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Books on the topic "Ontology based information retrieval"

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Arantza, Illarramendi, ed. Ontology-based query processing for global information systems. Boston: Kluwer Academic Publishers, 2001.

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Wohlgenannt, Gerhard. Learning ontology relations by combining corpus-based techniques and reasoning on data from semantic web sources. Frankfurt am Main: P. Lang, 2011.

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Wohlgenannt, Gerhard. Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources. Bern: Peter Lang International Academic Publishers, 2018.

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Sheth, A. Semantic web: Ontology and knowledge base enabled tools, services, and applications. Hershey, PA: Information Science Reference, 2013.

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

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Kemp, D. Alasdair. Computer-based knowledge retrieval. London: Aslib, 1988.

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service), SpringerLink (Online, ed. Ontology-based Application Integration. New York, NY: Springer Science+Business Media, LLC, 2011.

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Kessler, Carsten. Context-aware semantics-based information retrieval. Amsterdam: IOS Press, 2010.

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

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

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Book chapters on the topic "Ontology based information retrieval"

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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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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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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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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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Meštrović, Ana, and Andrea Calì. "An Ontology-Based Approach to Information Retrieval." In Semantic Keyword-Based Search on Structured Data Sources, 150–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53640-8_13.

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Sharan, Aditi, Manju Lata Joshi, and Anupama Pandey. "Exploiting Ontology for Concept Based Information Retrieval." In Information Systems for Indian Languages, 157–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19403-0_25.

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García, Elena, and Miguel-Ángel Sicilia. "Designing Ontology-Based Interactive Information Retrieval Interfaces." In On The Move to Meaningful Internet Systems 2003: OTM 2003 Workshops, 152–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39962-9_27.

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Liaqat, Madiha, Sharifullah Khan, and Muhammad Majid. "Fuzzy Ontology Based Model for Image Retrieval." In Mobile Web and Intelligent Information Systems, 108–20. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44215-0_9.

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Hsieh, Shang-Hsien, Ken-Yu Lin, Nai-Wen Chi, and Hsien-Tang Lin. "Domain Knowledge–Based Information Retrieval for Engineering Technical Documents." In Ontology in the AEC Industry, 1–25. Reston, VA: American Society of Civil Engineers, 2015. http://dx.doi.org/10.1061/9780784413906.ch01.

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Mukesh, Rajeswari, Sathish Kumar Penchala, and Anupama K. Ingale. "Ontology Based Zone Indexing Using Information Retrieval Systems." In Communications in Computer and Information Science, 181–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36321-4_16.

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Conference papers on the topic "Ontology based information retrieval"

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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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Deshmukh, Deepenti H., Poonam B. Lohiya, and Shwetambari G. Pundkar. "Information retrieval system based on ontology." In 2016 International conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2016. http://dx.doi.org/10.1109/scopes.2016.7955603.

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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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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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Besbes, Ghada, and Hajer Baazaoui-Zghal. "Fuzzy ontology-based Medical Information Retrieval." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737685.

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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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Zhang, Jian. "Web information retrieval based on ontology." In 2012 International Conference on Graphic and Image Processing, edited by Zeng Zhu. SPIE, 2013. http://dx.doi.org/10.1117/12.2011070.

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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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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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Reports on the topic "Ontology based information retrieval"

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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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Oard, Douglas W. Speech-Based Information Retrieval for Digital Libraries. Fort Belvoir, VA: Defense Technical Information Center, March 1997. http://dx.doi.org/10.21236/ada458105.

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Li, Eliot, Charles Nicholas, Tim Oates, and Raman K. Mehra. Intelligent Record Linkage Techniques Based on Information Retrieval, Natural Language Processing, and Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada408937.

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Demner-Fushman, Dina, and Douglas W. Oard. The Effect of Bilingual Term List Size on Dictionary-Based Cross-Language Information Retrieval. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada447948.

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Demner-Fushman, Dina, and Douglas W. Oard. The Effect of Bilingual Term List Size on Dictionary-Based Cross-Language Information Retrieval. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada452813.

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Koopmann, Patrick. Ontology-Mediated Query Answering for Probabilistic Temporal Data with EL Ontologies (Extended Version). Technische Universität Dresden, 2018. http://dx.doi.org/10.25368/2022.242.

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Especially in the field of stream reasoning, there is an increased interest in reasoning about temporal data in order to detect situations of interest or complex events. Ontologies have been proved a useful way to infer missing information from incomplete data, or simply to allow for a higher order vocabulary to be used in the event descriptions. Motivated by this, ontology-based temporal query answering has been proposed as a means for the recognition of situations and complex events. But often, the data to be processed do not only contain temporal information, but also probabilistic information, for example because of uncertain sensor measurements. While there has been a plethora of research on ontologybased temporal query answering, only little is known so far about querying temporal probabilistic data using ontologies. This work addresses this problem by introducing a temporal query language that extends a well-investigated temporal query language with probability operators, and investigating the complexity of answering queries using this query language together with ontologies formulated in the description logic EL.
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Shapovalov, Viktor B., Yevhenii B. Shapovalov, Zhanna I. Bilyk, Artem I. Atamas, Roman A. Tarasenko, and Vitaliy V. Tron. Centralized information web-oriented educational environment of Ukraine. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3251.

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The modern development of science and technology has provided high quantity of information. This information must be systemized and classified. For taxonomization of educational materials, it was proposed to use existing graph-generators and graph-visualizers of the TODOS IT platform. A separate aspect of the TODOS IT platform is the possibility of using a centralized web-oriented learning environment. Creation of the system and transdisciplinary knowledge is a problem of modern education, which can be solved by creating a centralized web-oriented educational environment. Using this approach is an important part of the learning process. Such a centralized web-oriented environment based on the ontological approach involves filling, adaptive educational services with information resources that reflect the conceptual system of a particular discipline. One of the systems providing not only collection of information but include its systemizing is centralized web-oriented educational environment based on Ontology4 system. Ontology 4 use elements of the TODOS. The paper presents specific developments of one centralized web-oriented educational environment can be used to teach different subjects such as biology, chemistry, Ukrainian language and literature, using the STEM approach.
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Baader, Franz, Stefan Borgwardt, and Marcel Lippmann. Temporal Conjunctive Queries in Expressive DLs with Non-simple Roles. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.222.

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In Ontology-Based Data Access (OBDA), user queries are evaluated over a set of facts under the open world assumption, while taking into account background knowledge given in the form of a Description Logic (DL) ontology. Motivated by situation awareness applications, temporal conjunctive queries (TCQs) have recently been proposed as a useful extension of traditional OBDA to support the processing of temporal information. This paper extends the existing complexity analysis of TCQ entailment to very expressive DLs underlying the OWL 2 standard, and in contrast to previous work also allows for queries containing transitive roles.
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Borgwardt, Stefan, Marcel Lippmann, and Veronika Thost. Temporal Query Answering w.r.t. DL-Lite-Ontologies. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.195.

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Ontology-based data access (OBDA) generalizes query answering in relational databases. It allows to query a database by using the language of an ontology, abstracting from the actual relations of the database. For ontologies formulated in Description Logics of the DL-Lite family, OBDA can be realized by rewriting the query into a classical first-order query, e.g. an SQL query, by compiling the information of the ontology into the query. The query is then answered using classical database techniques. In this report, we consider a temporal version of OBDA. We propose a temporal query language that combines a linear temporal logic with queries over DL-Litecore-ontologies. This language is well-suited for expressing temporal properties of dynamical systems and is useful in context-aware applications that need to detect specific situations. Using a first-order rewriting approach, we transform our temporal queries into queries over a temporal database. We then present three approaches to answering the resulting queries, all having different advantages and drawbacks.
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Ma, Yue, and Felix Distel. Learning Formal Definitions for Snomed CT from Text. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.193.

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Snomed CT is a widely used medical ontology which is formally expressed in a fragment of the Description Logic EL++. The underlying logics allow for expressive querying, yet make it costly to maintain and extend the ontology. Existing approaches for ontology generation mostly focus on learning superclass or subclass relations and therefore fail to be used to generate Snomed CT definitions. In this paper, we present an approach for the extraction of Snomed CT definitions from natural language texts, based on the distance relation extraction approach. By benefiting from a relatively large amount of textual data for the medical domain and the rich content of Snomed CT, such an approach comes with the benefit that no manually labelled corpus is required. We also show that the type information for Snomed CT concept is an important feature to be examined for such a system. We test and evaluate the approach using two types of texts. Experimental results show that the proposed approach is promising to assist Snomed CT development.
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