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Exploiting semantic web knowledge graphs in data mining

Author: Petar Ristoski
Publisher: Amsterdam : IOS Press, 2019.
Series: Studies on the Semantic Web, v. 038.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Petar Ristoski
ISBN: 9781614999812 1614999813
OCLC Number: 1111087409
Description: 1 online resource.
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
Series Title: Studies on the Semantic Web, v. 038.
Responsibility: Petar Ristoski.

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