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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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Content

Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

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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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Date

22. 1.2017 16:54:03
22. 1.2017 17:10:56

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Date

22. 7.2010 19:41:16

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Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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Content

Vgl. unter: http://www.hlt.utdallas.edu/~eduardo/papers/csr_model_and_applications.pdf. Vgl. für den Proceedingsband: http://www.aclweb.org/anthology/C/C10/C10-1000.pdf.

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Date

26.12.2011 13:40:22

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Date

23. 7.2017 13:49:22

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Source

Bulletin of the Association for Information Science and Technology. 41(2015) no.4, S.18-22

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Abstract

Learning non-taxonomic relations becomes an important research topic in ontology extension. Most of the existing learning approaches are mainly based on expert crafted corpora. These approaches are normally domain-specific and the corpora acquisition is laborious and costly. On the other hand, based on the static corpora, it is not able to meet personalized needs of semantic relations discovery for various taxonomies. In this paper, we propose a novel approach for learning non-taxonomic relations on demand. For any supplied taxonomy, it can focus on the segment of the taxonomy and collect information dynamically about the taxonomic concepts by using Wikipedia as a learning source. Based on the newly generated corpus, non-taxonomic relations are acquired through three steps: a) semantic relatedness detection; b) relations extraction between concepts; and c) relations generalization within a hierarchy. The proposed approach is evaluated on three different predefined taxonomies and the experimental results show that it is effective in capturing non-taxonomic relations as needed and has good potential for the ontology extension on demand.

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Content

Article Outline 1. Introduction 2. SKOS-based multilingual thesaurus of geological time scale 2.1. Addressing the insufficiency of SKOS in the context of the Semantic Web 2.2. Addressing semantics and syntax/lexicon in multilingual GTS terms 2.3. Extending SKOS model to capture GTS structure 2.4. Summary of building the SKOS-based MLTGTS 3. Recognizing and translating GTS terms retrieved from WMS 4. Pilot system, results, and evaluation 5. Discussion 6. Conclusions Vgl. unter: http://www.sciencedirect.com/science?_ob=MiamiImageURL&_cid=271720&_user=3865853&_pii=S0098300411000744&_check=y&_origin=&_coverDate=31-Oct-2011&view=c&wchp=dGLbVlt-zSkzS&_valck=1&md5=e2c1daf53df72d034d22278212578f42&ie=/sdarticle.pdf.

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Date

1. 6.2010 9:55:22

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Date

22. 2.2013 11:39:23

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22. 2.2013 12:03:53