
Exploiting Semantic Web Knowledge Graphs in Data Mining
Description
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
This book, Exploiting Semantic Web Knowledge Graphs in Data Mining, aims to show that Semantic Web knowledge graphs are useful for generating valuable data mining features that can be used in various data mining tasks. In Part I, Mining Semantic Web Knowledge Graphs, the author evaluates unsupervised feature generation strategies from types and relations in knowledge graphs used in different data mining tasks such as classification, regression, and outlier detection. Part II, Semantic Web Knowledge Graphs Embeddings, proposes an approach that circumvents the shortcomings introduced with the approaches in Part I, developing an approach that is able to embed complete Semantic Web knowledge graphs in a low dimensional feature space where each entity and relation in the knowledge graph is represented as a numerical vector. Finally, Part III, Applications of Semantic Web Knowledge Graphs, describes a list of applications that exploit Semantic Web knowledge graphs like classification and regression, showing that the approaches developed in Part I and Part II can be used in applications in various domains.
The book will be of interest to all those working in the field of data mining and KDD.
More details
Other editions
Additional editions
Content
- 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 Generation
- 3.3.2 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 Data
- 5.1 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 Generation
- 7.1.4 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 Representations
- 8.6 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
- 11.1 Related Work
- 11.2 Graph-based Methods for Recommender Systems
- 11.2.1 Evaluation
- 11.2.2 Conclusion and Outlook
- 11.3 A Hybrid Multi-Strategy Recommender System Using Semantic Web Knowledge Graphs
- 11.3.1 Predicting Ratings and Top k Lists
- 11.3.2 Creating Diverse Predictions
- 11.3.3 Conclusion and Outlook
- 11.4 A Content-Based Recommender System
- 11.4.1 Approach
- 11.4.2 Experiments
- 11.4.3 Conclusion
- 12 Entity and Document Modeling using Semantic Web Graph Embeddings
- 12.1 Related Work
- 12.1.1 Entity Relatedness
- 12.1.2 Entity and Document Similarity
- 12.2 Approach
- 12.2.1 Entity Similarity
- 12.2.2 Document Similarity
- 12.2.3 Entity Relatedness
- 12.3 Evaluation
- 12.3.1 Entity Relatedness
- 12.3.2 Document Similarity
- 13 Taxonomy Induction Using Knowledge Graph Embeddings
- 13.1 Introduction
- 13.2 Related Work
- 13.3 Approach
- 13.4 Experiments
- 13.4.1 Embedding the DBpedia Ontology
- 13.4.2 Inducing Ontologies from the WebIsADb
- 13.5 Conclusion and Outlook
- 14 Thesis Conclusion
- 14.1 PART I: Mining Semantic Web Knowledge Graphs
- 14.2 PART II: Semantic Web Knowledge Graphs Embeddings
- 14.3 PART III: Applications of Semantic Web Knowledge Graphs
- 14.4 Open Issues and Limitations
- 14.5 Future Work
- Bibliography
System requirements
File format: PDF
Copy protection: Watermark-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Use the free software Adobe Reader, Adobe Digital Editions, or any other PDF viewer of your choice (see eBook Help).
- Tablet/Smartphone (Android; iOS): Install the free app Adobe Digital Editions or another reading app for eBooks, e.g., PocketBook (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (only limited: Kindle).
The file format PDF always displays a book page identically on any hardware. This makes PDF suitable for complex layouts such as those used in textbooks and reference books (images, tables, columns, footnotes). Unfortunately, on the small screens of e-readers or smartphones, PDFs are rather annoying, requiring too much scrolling.
This eBook uses Watermark-DRM, a „soft” copy protection. This means that there are no technical restrictions to prevent illegal distribution. However, there is a personalised watermark embedded in the eBook that can be used to identify the purchaser of the eBook in the event of misuse and to provide evidence for legal purposes.
For more information, see our eBook Help page.