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  1. Järvelin, K.; Kristensen, J.; Niemi, T.; Sormunen, E.; Keskustalo, H.: ¬A deductive data model for query expansion (1996) 0.14
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    Source
    Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR '96), Zürich, Switzerland, August 18-22, 1996. Eds.: H.P. Frei et al
  2. Öttl, S.; Streiff, D.; Stettler, N.; Studer, M.: Aufbau einer Testumgebung zur Ermittlung signifikanter Parameter bei der Ontologieabfrage (2010) 0.11
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    Abstract
    Der Einsatz von semantischen Technologien ist mittlerweile ein etabliertes Mittel zur Optimierung von Information-Retrieval-Systemen. Obwohl der Einsatz von Ontologien für verschiedene Anwendungsbereiche wie beispielsweise zur Query-Expansion (Bhogal et al. 2007), zur Strukturierung von Benutzeroberflächen bzw. zur Dialoggestaltung (z. B. Garcia & Sicilia 2003; Liu et al. 2005; Lopez et al. 2006; Paulheim 2009; Paulheim & Probst 2010), in Recommendersystemen (z. B. Taehee et al. 2006; Cantador et al. 2008; Middleton et al. 2001; Middleton et al. 2009) usw. rege erforscht wird, gibt es noch kaum Bestrebungen, die einzelnen Abfragemethodiken für Ontologien systematisch zu untersuchen. Bei der Abfrage von Ontologien geht es in erster Linie darum, Zusammenhänge zwischen Begriffen zu ermitteln, indem hierarchische (Classes und Individuals), semantische (Object Properties) und ergänzende (Datatype Properties) Beziehungen abgefragt oder logische Verknüpfungen abgeleitet werden. Hierbei werden sogenannte Reasoner für die Ableitungen und als Abfragesprache SPARQL (seltener auch XPath) eingesetzt. Ein weiterer, weniger oft eingesetzter, vielversprechender Ansatz findet sich bei Hoser et al. (2006) und Weng & Chang (2008), die Techniken der Sozialen Netzwerkanalyse zur Auswertung von Ontologien miteinsetzen (Semantic Network Analysis). Um die Abfrage von Ontologien sowie Kombinationen der unterschiedlichen Abfragemöglichkeiten systematisch untersuchen zu können, wurde am SII eine entsprechende Testumgebung entwickelt, die in diesem Beitrag genauer vorgestellt werden soll.
  3. Reasoning Web : Semantic Interoperability on the Web, 13th International Summer School 2017, London, UK, July 7-11, 2017, Tutorial Lectures (2017) 0.10
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    Content
    Neumaier, Sebastian (et al.): Data Integration for Open Data on the Web - Stamou, Giorgos (et al.): Ontological Query Answering over Semantic Data - Calì, Andrea: Ontology Querying: Datalog Strikes Back - Sequeda, Juan F.: Integrating Relational Databases with the Semantic Web: A Reflection - Rousset, Marie-Christine (et al.): Datalog Revisited for Reasoning in Linked Data - Kaminski, Roland (et al.): A Tutorial on Hybrid Answer Set Solving with clingo - Eiter, Thomas (et al.): Answer Set Programming with External Source Access - Lukasiewicz, Thomas: Uncertainty Reasoning for the Semantic Web - Calvanese, Diego (et al.): OBDA for Log Extraction in Process Mining
    Editor
    Ianni, G. et al.
  4. Bloehdorn, S.; Cimiano, P.; Duke, A.; Haase, P.; Heizmann, J.; Thurlow, I.; Völker, J.: Ontology-based question answering for digital libraries (2007) 0.10
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    Source
    Research and advanced technology for digital libraries : 11th European conference, ECDL 2007 / Budapest, Hungary, September 16-21, 2007, proceedings. Eds.: L. Kovacs et al
  5. Amarger, F.; Chanet, J.-P.; Haemmerlé, O.; Hernandez, N.; Roussey, C.: SKOS sources transformations for ontology engineering : agronomical taxonomy use case (2014) 0.09
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    Source
    Metadata and semantics research: 8th Research Conference, MTSR 2014, Karlsruhe, Germany, November 27-29, 2014, Proceedings. Eds.: S. Closs et al
  6. Muljarto, A.-R.; Salmon, J.-M.; Neveu, P.; Charnomordic, B.; Buche, P.: Ontology-based model for food transformation processes : application to winemaking (2014) 0.09
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    Source
    Metadata and semantics research: 8th Research Conference, MTSR 2014, Karlsruhe, Germany, November 27-29, 2014, Proceedings. Eds.: S. Closs et al
  7. Boer, V. de; Wielemaker, J.; Gent, J. van; Hildebrand, M.; Isaac, A.; Ossenbruggen, J. van; Schreiber, G.: Supporting linked data production for cultural heritage institutes : the Amsterdam Museum case study (2012) 0.09
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    Source
    9th Extended Semantic Web Conference (ESWC), 2012-05-27/2012-05-31 in Hersonissos, Crete, Greece. Eds.: Elena Simperl et al
  8. Kleineberg, M.: Classifying perspectives : expressing levels of knowing in the Integrative Levels Classification (2020) 0.08
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    Source
    Knowledge Organization at the Interface. Proceedings of the Sixteenth International ISKO Conference, 2020 Aalborg, Denmark. Ed.: M. Lykke et al
  9. Balakrishnan, U,; Soergel, D.; Helfer, O.: Representing concepts through description logic expressions for knowledge organization system (KOS) mapping (2020) 0.08
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    Source
    Knowledge Organization at the Interface. Proceedings of the Sixteenth International ISKO Conference, 2020 Aalborg, Denmark. Ed.: M. Lykke et al
  10. Assem, M. van; Rijgersberg, H.; Wigham, M.; Top, J.: Converting and annotating quantitative data tables (2010) 0.08
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    Source
    The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I. Eds.: Peter F. Patel-Schneider et al
  11. Djioua, B.; Desclés, J.-P.; Alrahabi, M.: Searching and mining with semantic categories (2012) 0.08
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    Abstract
    A new model is proposed to retrieve information by building automatically a semantic metatext structure for texts that allow searching and extracting discourse and semantic information according to certain linguistic categorizations. This paper presents approaches for searching and mining full text with semantic categories. The model is built up from two engines: The first one, called EXCOM (Djioua et al., 2006; Alrahabi, 2010), is an automatic system for text annotation, related to discourse and semantic maps, which are specification of general linguistic ontologies founded on the Applicative and Cognitive Grammar. The annotation layer uses a linguistic method called Contextual Exploration, which handles the polysemic values of a term in texts. Several 'semantic maps' underlying 'point of views' for text mining guide this automatic annotation process. The second engine uses semantic annotated texts, produced previously in order to create a semantic inverted index, which is able to retrieve relevant documents for queries associated with discourse and semantic categories such as definition, quotation, causality, relations between concepts, etc. (Djioua & Desclés, 2007). This semantic indexation process builds a metatext layer for textual contents. Some data and linguistic rules sets as well as the general architecture that extend third-party software are expressed as supplementary information.
  12. Zhang, L.; Liu, Q.L.; Zhang, J.; Wang, H.F.; Pan, Y.; Yu, Y.: Semplore: an IR approach to scalable hybrid query of Semantic Web data (2007) 0.08
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    Source
    Proceeding ISWC'07/ASWC'07 : Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference. Ed.: K. Aberer et al
  13. Rousset, M.-C.; Atencia, M.; David, J.; Jouanot, F.; Ulliana, F.; Palombi, O.: Datalog revisited for reasoning in linked data (2017) 0.08
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    Source
    Reasoning Web: Semantic Interoperability on the Web, 13th International Summer School 2017, London, UK, July 7-11, 2017, Tutorial Lectures. Eds.: Ianni, G. et al
  14. El idrissi esserhrouchni, O. et al.; Frikh, B.; Ouhbi, B.: OntologyLine : a new framework for learning non-taxonomic relations of domain ontology (2016) 0.07
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    Source
    Knowledge discovery, knowledge engineering and knowledge management: 7th International Joint Conference, IC3K 2015, Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers. Eds.: A. Fred et al
  15. Thenmalar, S.; Geetha, T.V.: Enhanced ontology-based indexing and searching (2014) 0.06
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    Abstract
    Purpose - The purpose of this paper is to improve the conceptual-based search by incorporating structural ontological information such as concepts and relations. Generally, Semantic-based information retrieval aims to identify relevant information based on the meanings of the query terms or on the context of the terms and the performance of semantic information retrieval is carried out through standard measures-precision and recall. Higher precision leads to the (meaningful) relevant documents obtained and lower recall leads to the less coverage of the concepts. Design/methodology/approach - In this paper, the authors enhance the existing ontology-based indexing proposed by Kohler et al., by incorporating sibling information to the index. The index designed by Kohler et al., contains only super and sub-concepts from the ontology. In addition, in our approach, we focus on two tasks; query expansion and ranking of the expanded queries, to improve the efficiency of the ontology-based search. The aforementioned tasks make use of ontological concepts, and relations existing between those concepts so as to obtain semantically more relevant search results for a given query. Findings - The proposed ontology-based indexing technique is investigated by analysing the coverage of concepts that are being populated in the index. Here, we introduce a new measure called index enhancement measure, to estimate the coverage of ontological concepts being indexed. We have evaluated the ontology-based search for the tourism domain with the tourism documents and tourism-specific ontology. The comparison of search results based on the use of ontology "with and without query expansion" is examined to estimate the efficiency of the proposed query expansion task. The ranking is compared with the ORank system to evaluate the performance of our ontology-based search. From these analyses, the ontology-based search results shows better recall when compared to the other concept-based search systems. The mean average precision of the ontology-based search is found to be 0.79 and the recall is found to be 0.65, the ORank system has the mean average precision of 0.62 and the recall is found to be 0.51, while the concept-based search has the mean average precision of 0.56 and the recall is found to be 0.42. Practical implications - When the concept is not present in the domain-specific ontology, the concept cannot be indexed. When the given query term is not available in the ontology then the term-based results are retrieved. Originality/value - In addition to super and sub-concepts, we incorporate the concepts present in same level (siblings) to the ontological index. The structural information from the ontology is determined for the query expansion. The ranking of the documents depends on the type of the query (single concept query, multiple concept queries and concept with relation queries) and the ontological relations that exists in the query and the documents. With this ontological structural information, the search results showed us better coverage of concepts with respect to the query.
    Date
    20. 1.2015 18:30:22
  16. Blobel, B.: Ontologies, knowledge representation, artificial intelligence : hype or prerequisite for international pHealth interoperability? (2011) 0.05
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    Source
    E-Health Across Borders Without Boundaries. E-salus trans confinia sine finibus; Proceedings of the ERMI Special Topic Conference 14-15 April 2011 Lasko, Slovenia. Eds.: L. Stoicu-Tivadar, Bernd Blobel et al
  17. Gödert, W.: Facets and typed relations as tools for reasoning processes in information retrieval (2014) 0.05
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    Source
    Metadata and semantics research: 8th Research Conference, MTSR 2014, Karlsruhe, Germany, November 27-29, 2014, Proceedings. Eds.: S. Closs et al
  18. Lukasiewicz, T.: Uncertainty reasoning for the Semantic Web (2017) 0.05
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    Source
    Reasoning Web: Semantic Interoperability on the Web, 13th International Summer School 2017, London, UK, July 7-11, 2017, Tutorial Lectures. Eds.: Ianni, G. et al
  19. Atanassova, I.; Bertin, M.: Semantic facets for scientific information retrieval (2014) 0.05
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    Source
    Semantic Web Evaluation Challenge. SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers. Eds.: V. Presutti et al
  20. Frické, M.: Logic and the organization of information (2012) 0.05
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    Footnote
    Rez. in: J. Doc. 70(2014) no.4: "Books on the organization of information and knowledge, aimed at a library/information audience, tend to fall into two clear categories. Most are practical and pragmatic, explaining the "how" as much or more than the "why". Some are theoretical, in part or in whole, showing how the practice of classification, indexing, resource description and the like relates to philosophy, logic, and other foundational bases; the books by Langridge (1992) and by Svenonious (2000) are well-known examples this latter kind. To this category certainly belongs a recent book by Martin Frické (2012). The author takes the reader for an extended tour through a variety of aspects of information organization, including classification and taxonomy, alphabetical vocabularies and indexing, cataloguing and FRBR, and aspects of the semantic web. The emphasis throughout is on showing how practice is, or should be, underpinned by formal structures; there is a particular emphasis on first order predicate calculus. The advantages of a greater, and more explicit, use of symbolic logic is a recurring theme of the book. There is a particularly commendable historical dimension, often omitted in texts on this subject. It cannot be said that this book is entirely an easy read, although it is well written with a helpful index, and its arguments are generally well supported by clear and relevant examples. It is thorough and detailed, but thereby seems better geared to the needs of advanced students and researchers than to the practitioners who are suggested as a main market. For graduate students in library/information science and related disciplines, in particular, this will be a valuable resource. I would place it alongside Svenonious' book as the best insight into the theoretical "why" of information organization. It has evoked a good deal of interest, including a set of essay commentaries in Journal of Information Science (Gilchrist et al., 2013). Introducing these, Alan Gilchrist rightly says that Frické deserves a salute for making explicit the fundamental relationship between the ancient discipline of logic and modern information organization. If information science is to continue to develop, and make a contribution to the organization of the information environments of the future, then this book sets the groundwork for the kind of studies which will be needed." (D. Bawden)

Authors

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