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The Resource Exploiting semantic web knowledge graphs in data mining, Petar Ristoski

Exploiting semantic web knowledge graphs in data mining, Petar Ristoski

Label
Exploiting semantic web knowledge graphs in data mining
Title
Exploiting semantic web knowledge graphs in data mining
Statement of responsibility
Petar Ristoski
Creator
Subject
Genre
Language
eng
Member of
http://library.link/vocab/creatorName
Ristoski, Petar
Dewey number
925.04
Index
no index present
Literary form
non fiction
Series statement
Studies on the Semantic Web
Series volume
volume 038
http://library.link/vocab/subjectName
  • Data mining
  • Semantic Web
Label
Exploiting semantic web knowledge graphs in data mining, Petar Ristoski
Instantiates
Publication
Carrier category
volume
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Intro; Title Page; Abstract; Table of Contents; 1 Introduction; 1.1 Research Questions; 1.2 Contributions; 1.3 Structure; 2 Fundamentals; 2.1 Semantic Web Knowledge Graphs; 2.1.1 Linked Open Data; 2.2 Data Mining and The Knowledge Discovery Process; 2.3 Semantic Web Knowledge Graphs in Data Mining; 3 Related Work; 3.1 Selection; 3.1.1 Using LOD to interpret relational databases; 3.1.2 Using LOD to interpret semi-structured data; 3.1.3 Using LOD to interpret unstructured data; 3.2 Preprocessing; 3.2.1 Domain-independent Approaches; 3.2.2 Domain-specific Approaches; 3.3 Transformation
  • 3.3.1 Feature Generation3.3.2 Feature Selection; 3.3.3 Other; 3.4 Data Mining; 3.4.1 Domain-independent Approaches; 3.4.2 Domain-specific Approaches; 3.5 Interpretation; 3.6 Discussion; 3.7 Conclusion and Outlook; I Mining Semantic Web Knowledge Graphs; 4 A Collection of Benchmark Datasets for Systematic Evaluations of Machine Learning on the Semantic Web; 4.1 Datasets; 4.2 Experiments; 4.2.1 Feature Generation Strategies; 4.2.2 Experiment Setup; 4.2.3 Results; 4.2.4 Number of Generated Features; 4.2.5 Features Increase Rate; 4.3 Conclusion and Outlook
  • 5 Propositionalization Strategies for Creating Features from Linked Open Data5.1 Strategies; 5.1.1 Strategies for Features Derived from Specific Relations; 5.1.2 Strategies for Features Derived from Relations as Such; 5.2 Evaluation; 5.2.1 Tasks and Datasets; 5.2.2 Results; 5.3 Conclusion and Outlook; 6 Feature Selection in Hierarchical Feature Spaces; 6.1 Problem Statement; 6.2 Approach; 6.3 Evaluation; 6.3.1 Datasets; 6.3.2 Experiment Setup; 6.3.3 Results; 6.4 Conclusion and Outlook; 7 Mining the Web of Linked Data with RapidMiner; 7.1 Description; 7.1.1 Data Import; 7.1.2 Data Linking
  • 7.1.3 Feature Generation7.1.4 Feature Subset Selection; 7.1.5 Exploring Links; 7.1.6 Data Integration; 7.2 Example Use Case; 7.3 Evaluation; 7.3.1 Feature Generation; 7.3.2 Propositionalization Strategies; 7.3.3 Feature Selection; 7.3.4 Data Integration; 7.3.5 Time Performances; 7.4 Related Work; 7.5 Conclusion and Outlook; II Semantic Web Knowledge Graphs Embeddings; 8 RDF2Vec: RDF Graph Embeddings for Data Mining; 8.1 Approach; 8.1.1 RDF Graph Sub-Structures Extraction; 8.1.2 Neural Language Models -- word2vec; 8.2 Evaluation; 8.3 Experimental Setup; 8.4 Results
  • 8.5 Semantics of Vector Representations8.6 Features Increase Rate; 8.7 Conclusion and Outlook; 9 Biased Graph Walks for RDF Graph Embeddings; 9.1 Approach; 9.2 Evaluation; 9.2.1 Datasets; 9.2.2 Experimental Setup; 9.2.3 Results; 9.3 Conclusion and Outlook; III Applications of Semantic Web Knowledge Graphs; 10 Analyzing Statistics with Background Knowledge from Semantic Web Knowledge Graphs; 10.1 The ViCoMap Tool; 10.1.1 Data Import; 10.1.2 Correlation Analysis; 10.2 Use Case: Number of Universities per State in Germany; 10.3 Conclusion and Outlook; 11 Semantic Web enabled Recommender Systems
Control code
on1111087409
Dimensions
23 cm.
Extent
xviii, 226 pages
Form of item
online
Isbn
9781614999805
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations :
System control number
(OCoLC)1111087409
Label
Exploiting semantic web knowledge graphs in data mining, Petar Ristoski
Publication
Carrier category
volume
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Intro; Title Page; Abstract; Table of Contents; 1 Introduction; 1.1 Research Questions; 1.2 Contributions; 1.3 Structure; 2 Fundamentals; 2.1 Semantic Web Knowledge Graphs; 2.1.1 Linked Open Data; 2.2 Data Mining and The Knowledge Discovery Process; 2.3 Semantic Web Knowledge Graphs in Data Mining; 3 Related Work; 3.1 Selection; 3.1.1 Using LOD to interpret relational databases; 3.1.2 Using LOD to interpret semi-structured data; 3.1.3 Using LOD to interpret unstructured data; 3.2 Preprocessing; 3.2.1 Domain-independent Approaches; 3.2.2 Domain-specific Approaches; 3.3 Transformation
  • 3.3.1 Feature Generation3.3.2 Feature Selection; 3.3.3 Other; 3.4 Data Mining; 3.4.1 Domain-independent Approaches; 3.4.2 Domain-specific Approaches; 3.5 Interpretation; 3.6 Discussion; 3.7 Conclusion and Outlook; I Mining Semantic Web Knowledge Graphs; 4 A Collection of Benchmark Datasets for Systematic Evaluations of Machine Learning on the Semantic Web; 4.1 Datasets; 4.2 Experiments; 4.2.1 Feature Generation Strategies; 4.2.2 Experiment Setup; 4.2.3 Results; 4.2.4 Number of Generated Features; 4.2.5 Features Increase Rate; 4.3 Conclusion and Outlook
  • 5 Propositionalization Strategies for Creating Features from Linked Open Data5.1 Strategies; 5.1.1 Strategies for Features Derived from Specific Relations; 5.1.2 Strategies for Features Derived from Relations as Such; 5.2 Evaluation; 5.2.1 Tasks and Datasets; 5.2.2 Results; 5.3 Conclusion and Outlook; 6 Feature Selection in Hierarchical Feature Spaces; 6.1 Problem Statement; 6.2 Approach; 6.3 Evaluation; 6.3.1 Datasets; 6.3.2 Experiment Setup; 6.3.3 Results; 6.4 Conclusion and Outlook; 7 Mining the Web of Linked Data with RapidMiner; 7.1 Description; 7.1.1 Data Import; 7.1.2 Data Linking
  • 7.1.3 Feature Generation7.1.4 Feature Subset Selection; 7.1.5 Exploring Links; 7.1.6 Data Integration; 7.2 Example Use Case; 7.3 Evaluation; 7.3.1 Feature Generation; 7.3.2 Propositionalization Strategies; 7.3.3 Feature Selection; 7.3.4 Data Integration; 7.3.5 Time Performances; 7.4 Related Work; 7.5 Conclusion and Outlook; II Semantic Web Knowledge Graphs Embeddings; 8 RDF2Vec: RDF Graph Embeddings for Data Mining; 8.1 Approach; 8.1.1 RDF Graph Sub-Structures Extraction; 8.1.2 Neural Language Models -- word2vec; 8.2 Evaluation; 8.3 Experimental Setup; 8.4 Results
  • 8.5 Semantics of Vector Representations8.6 Features Increase Rate; 8.7 Conclusion and Outlook; 9 Biased Graph Walks for RDF Graph Embeddings; 9.1 Approach; 9.2 Evaluation; 9.2.1 Datasets; 9.2.2 Experimental Setup; 9.2.3 Results; 9.3 Conclusion and Outlook; III Applications of Semantic Web Knowledge Graphs; 10 Analyzing Statistics with Background Knowledge from Semantic Web Knowledge Graphs; 10.1 The ViCoMap Tool; 10.1.1 Data Import; 10.1.2 Correlation Analysis; 10.2 Use Case: Number of Universities per State in Germany; 10.3 Conclusion and Outlook; 11 Semantic Web enabled Recommender Systems
Control code
on1111087409
Dimensions
23 cm.
Extent
xviii, 226 pages
Form of item
online
Isbn
9781614999805
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations :
System control number
(OCoLC)1111087409

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