The Resource Exploiting semantic web knowledge graphs in data mining, Petar Ristoski
Exploiting semantic web knowledge graphs in data mining, Petar Ristoski
Resource Information
The item Exploiting semantic web knowledge graphs in data mining, Petar Ristoski represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.This item is available to borrow from 1 library branch.
Resource Information
The item Exploiting semantic web knowledge graphs in data mining, Petar Ristoski represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.
This item is available to borrow from 1 library branch.
- Language
- eng
- Extent
- xviii, 226 pages
- 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
- Isbn
- 9781614999805
- Label
- Exploiting semantic web knowledge graphs in data mining
- Title
- Exploiting semantic web knowledge graphs in data mining
- Statement of responsibility
- Petar Ristoski
- Language
- eng
- 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
- 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
- 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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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.eui.eu/portal/Exploiting-semantic-web-knowledge-graphs-in-data/WOCiGySDbxs/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.eui.eu/portal/Exploiting-semantic-web-knowledge-graphs-in-data/WOCiGySDbxs/">Exploiting semantic web knowledge graphs in data mining, Petar Ristoski</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.eui.eu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.eui.eu/">European University Institute</a></span></span></span></span></div>
