Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources.
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Superior document: | Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series ; v.44 |
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Place / Publishing House: | Frankfurt a.M. : : Peter Lang GmbH, Internationaler Verlag der Wissenschaften,, 2011. Ã2011. |
Year of Publication: | 2011 |
Edition: | 1st ed. |
Language: | English |
Series: | Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series
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Physical Description: | 1 online resource (226 pages) |
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Wohlgenannt, Gerhard. Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources. 1st ed. Frankfurt a.M. : Peter Lang GmbH, Internationaler Verlag der Wissenschaften, 2011. Ã2011. 1 online resource (226 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series ; v.44 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. 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. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Electronic books. Print version: Wohlgenannt, Gerhard Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources Frankfurt a.M. : Peter Lang GmbH, Internationaler Verlag der Wissenschaften,c2011 9783631606513 ProQuest (Firm) Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30686223 Click to View |
language |
English |
format |
eBook |
author |
Wohlgenannt, Gerhard. |
spellingShingle |
Wohlgenannt, Gerhard. Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources. Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series ; 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. 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. |
author_facet |
Wohlgenannt, Gerhard. |
author_variant |
g w gw |
author_sort |
Wohlgenannt, Gerhard. |
title |
Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources. |
title_full |
Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources. |
title_fullStr |
Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources. |
title_full_unstemmed |
Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources. |
title_auth |
Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources. |
title_new |
Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources. |
title_sort |
learning ontology relations by combining corpus-based techniques and reasoning on data from semantic web sources. |
series |
Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series ; |
series2 |
Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series ; |
publisher |
Peter Lang GmbH, Internationaler Verlag der Wissenschaften, |
publishDate |
2011 |
physical |
1 online resource (226 pages) |
edition |
1st ed. |
contents |
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. 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. |
isbn |
9783631753842 9783631606513 |
genre |
Electronic books. |
genre_facet |
Electronic books. |
url |
https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30686223 |
illustrated |
Not Illustrated |
oclc_num |
1399170997 |
work_keys_str_mv |
AT wohlgenanntgerhard learningontologyrelationsbycombiningcorpusbasedtechniquesandreasoningondatafromsemanticwebsources |
status_str |
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ids_txt_mv |
(MiAaPQ)50030686223 (Au-PeEL)EBL30686223 (OCoLC)1399170997 |
carrierType_str_mv |
cr |
hierarchy_parent_title |
Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series ; v.44 |
is_hierarchy_title |
Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources. |
container_title |
Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series ; v.44 |
marc_error |
Info : Unimarc and ISO-8859-1 translations identical, choosing ISO-8859-1. --- [ 856 : z ] |
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