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|a 1614999813
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|a QA76.9.D343
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|a 006.312
|2 23
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|a UAMI
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|a Ristoski, Petar,
|e author.
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|a Exploiting semantic web knowledge graphs in data mining /
|c Petar Ristoski.
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|a Amsterdam :
|b IOS Press,
|c [2019]
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|c ©2019
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|a 1 online resource (xviii, 226 pages) :
|b illustrations
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a Studies on the Semantic Web ;
|v volume 038
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|a Includes bibliographical references.
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|a 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
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|a 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
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|a 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
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|a 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
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|a 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
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|a Print version record.
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| 590 |
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|a eBooks on EBSCOhost
|b EBSCO eBook Subscription Academic Collection - Worldwide
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| 650 |
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|a Data mining.
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| 650 |
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|a Semantic Web.
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| 650 |
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|a Data Mining
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|a Exploration de données (Informatique)
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| 650 |
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|a Web sémantique.
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| 650 |
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|a Data mining
|2 fast
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| 650 |
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|a Semantic Web
|2 fast
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| 830 |
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|a Studies on the Semantic Web ;
|v v. 038.
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| 856 |
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|u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2217268
|z Texto completo
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| 938 |
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|a ProQuest Ebook Central
|b EBLB
|n EBL5844067
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| 938 |
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|a EBSCOhost
|b EBSC
|n 2217268
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| 994 |
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|a 92
|b IZTAP
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