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20231204023231.0 |
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231204s2011 xx o ||||0 eng d |
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|a 9783631753842
|q (electronic bk.)
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|z 9783631606513
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|a (MiAaPQ)EBC30686223
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|a (Au-PeEL)EBL30686223
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|a (OCoLC)1399170997
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|a MiAaPQ
|b eng
|e rda
|e pn
|c MiAaPQ
|d MiAaPQ
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|a Wohlgenannt, Gerhard.
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|a Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources.
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|a 1st ed.
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|a Frankfurt a.M. :
|b Peter Lang GmbH, Internationaler Verlag der Wissenschaften,
|c 2011.
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|c Ã2011.
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|a 1 online resource (226 pages)
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|a text
|b txt
|2 rdacontent
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|a computer
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|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series ;
|v v.44
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|a Cover -- 1 Introduction -- 2 The Semantic Web -- 2.1 Overview -- 2.1.1 Background and Vision -- 2.1.2 Features -- 2.1.3 Misconceptions and Criticism -- 2.2 Applications -- 3 Ontologies -- 3.1 Fundamentals -- 3.1.1 Purpose -- 3.1.2 Structure and Entities -- 3.1.3 Ontology Research Fields -- 3.2 Representation -- 3.2.1 Resource Description Framework -- 3.2.2 RDF Schema -- 3.2.3 Web Ontology Language -- 3.3 Querying and Reasoning -- 3.3.1 SPARQL and RDQL -- 3.3.2 Reasoning with Jena -- 3.3.3 Redland -- 3.4 Public Datasets and Ontologies -- 3.4.1 DBpedia -- 3.4.2 Freebase -- 3.4.3 OpenCyc -- 4 Methodology -- 4.1 Ontology Learning -- 4.2 Methods for Learning Semantic Associations -- 4.2.1 Natural Language Processing Techniques -- 4.2.2 Lexico-syntactic Patterns -- 4.2.3 Relevant Statistical and Information Retrieval Measures and Methods -- 4.2.4 Machine Learning Paradigms -- 4.3 Literature Review -- 4.3.1 Domain Text and Semantic Associations -- 4.3.2 The Web and Semantic Associations -- 4.3.3 Domain Text and Linguistic Patterns -- 4.3.4 The Web and Linguistic Patterns -- 4.3.5 Semantic Web Data and Reasoning -- 4.3.6 Selected Work from SemEval2007 -- 4.3.7 Learning of Qualia Structures -- 4.4 webLyzard Ontology Learning System -- 4.4.1 System Overview -- 4.4.2 Major Components of the Framework -- 4.4.3 Identification of the Most Relevant Concepts -- 4.4.4 Concept Positioning and Taxonomy Discovery -- 4.5 A Novel Method to Detect Relations -- 4.5.1 Relation Labeling Based on Vector Space Similarity -- 4.5.2 Ontological Restrictions and Integration of External Knowledge -- 4.5.3 The Knowledge Base -- 4.5.4 A Hybrid Method for Relation Labeling -- 4.5.5 Integration of User Feedback -- 4.6 Implementation of the Method -- 4.6.1 Training -- 4.6.2 Compute Vector Space Similarities -- 4.6.3 Ontological Restrictions and Concept Grounding -- 4.6.4 Scarlet.
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|a 4.6.5 Evaluation -- 5 Results and Evaluation -- 5.1 Domain Relations and Domain Corpus -- 5.2 Evaluation of the Vector Space Model -- 5.2.1 Evaluation Baselines -- 5.2.2 Configuration Parameters -- 5.2.3 Average Ranking Precision -- 5.2.4 First Guess Correct -- 5.2.5 Second Guess Correct -- 5.3 Concept Grounding -- 5.4 Scarlet -- 5.5 Evaluation of Integrated Data Sources -- 5.5.1 Average Ranking Precision -- 5.5.2 First Guess Correct -- 5.5.3 Second Guess Correct -- 5.5.4 Individual Predicates -- 5.5.5 Summary and Interpretation -- 6 Conclusions and Outlook -- Bibliography.
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|a Description based on publisher supplied metadata and other sources.
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|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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| 655 |
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|a Electronic books.
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|i Print version:
|a Wohlgenannt, Gerhard
|t Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources
|d Frankfurt a.M. : Peter Lang GmbH, Internationaler Verlag der Wissenschaften,c2011
|z 9783631606513
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| 797 |
2 |
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|a ProQuest (Firm)
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| 830 |
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|a Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series
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| 856 |
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|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=30686223
|z Click to View
|
