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- 1.10.2018 14:13:22
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Mayfield, J.; Finin, T.: Information retrieval on the Semantic Web : integrating inference and retrieval
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- 12. 2.2011 17:35:22
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Bittner, T.; Donnelly, M.; Winter, S.: Ontology and semantic interoperability (2006)
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- 3.12.2016 18:39:22
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Järvelin, K.; Kristensen, J.; Niemi, T.; Sormunen, E.; Keskustalo, H.: ¬A deductive data model for query expansion (1996)
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- Source
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Müller, T.: Wissensrepräsentation mit semantischen Netzen im Bereich Luftfahrt (2006)
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- Date
- 26. 9.2006 21:00:22
-
Kiren, T.; Shoaib, M.: ¬A novel ontology matching approach using key concepts (2016)
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- Date
- 20. 1.2015 18:30:22
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Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024)
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- Abstract
- Detecting science-technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K-core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.
-
Kiren, T.: ¬A clustering based indexing technique of modularized ontologies for information retrieval (2017)
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- Date
- 20. 1.2015 18:30:22
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Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019)
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- Content
- Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
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Bandholtz, T.; Schulte-Coerne, T.; Glaser, R.; Fock, J.; Keller, T.: iQvoc - open source SKOS(XL) maintenance and publishing tool (2010)
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-
Bittner, T.: ¬An introduction to formal ontology and how it can facilitate semantic interoperability (2005)
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-
Zeh, T.: Ontologien in den Informationswissenschaften (2011)
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- Footnote
- Vgl.: http://www.ontoport.org/alfresco -> "My Home"; http://www.xing.com/t/de/ONTOLOGIEN
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Sandkühler, H.J.: Epistemologischer Realismus und die Wirklichkeit des Wissens : eine Verteidigung der Philosophie des Geistes gegen Naturalismus und Reduktionismus (1992)
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- Footnote
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Wilson, T.: ¬The strict faceted classification model (2006)
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Witte, R.; Krestel, R.; Kappler, T.; Lockemann, P.C.: Converting a historical architecture encyclopedia into a semantic knowledge base
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Reimer, U.: Einführung in die Wissensrepräsentation : netzartige und schema-basierte Repräsentationsformate (1991)
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- Classification
- Mat T 1091 / Wissensverarbeitung
- SBB
- Mat T 1091 / Wissensverarbeitung
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Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005)
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- Content
- Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.
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Xiong, C.: Knowledge based text representations for information retrieval (2016)
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- Content
- Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.
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Stuart, D.: Practical ontologies for information professionals (2016)
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- Content
- C H A P T E R 1 What is an ontology?; Introduction; The data deluge and information overload; Defining terms; Knowledge organization systems and ontologies; Ontologies, metadata and linked data; What can an ontology do?; Ontologies and information professionals; Alternatives to ontologies; The aims of this book; The structure of this book; C H A P T E R 2 Ontologies and the semantic web; Introduction; The semantic web and linked data; Resource Description Framework (RDF); Classes, subclasses and properties; The semantic web stack; Embedded RDF; Alternative semantic visionsLibraries and the semantic web; Other cultural heritage institutions and the semantic web; Other organizations and the semantic web; Conclusion; C H A P T E R 3 Existing ontologies; Introduction; Ontology documentation; Ontologies for representing ontologies; Ontologies for libraries; Upper ontologies; Cultural heritage data models; Ontologies for the web; Conclusion; C H A P T E R 4 Adopting ontologies; Introduction; Reusing ontologies: application profiles and data models; Identifying ontologies; The ideal ontology discovery tool; Selection criteria; Conclusion C H A P T E R 5 Building ontologiesIntroduction; Approaches to building an ontology; The twelve steps; Ontology development example: Bibliometric Metrics Ontology element set; Conclusion; C H A P T E R 6 Interrogating ontologies; Introduction; Interrogating ontologies for reuse; Interrogating a knowledge base; Understanding ontology use; Conclusion; C H A P T E R 7 The future of ontologies and the information professional; Introduction; The future of ontologies for knowledge discovery; The future role of library and information professionals; The practical development of ontologies
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Wang, H.; Liu, Q.; Penin, T.; Fu, L.; Zhang, L.; Tran, T.; Yu, Y.; Pan, Y.: Semplore: a scalable IR approach to search the Web of Data (2009)
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