Domain-Specific Knowledge Graph Construction
Kejriwal, Mayank.
Domain-Specific Knowledge Graph Construction [electronic resource] / by Mayank Kejriwal. - 1st ed. 2019. - XIV, 107 p. 19 illus. online resource. - SpringerBriefs in Computer Science, 2191-5776 . - SpringerBriefs in Computer Science, .
1. What is a knowledge graph? -- 2. Information Extraction -- 3. Entity Resolution -- 4. Advanced Topic: Knowledge Graph Completion -- 5. Ecosystems .
The vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as knowledge graphs. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This book will synthesize Knowledge Graph Construction over Web Data in an engaging and accessible manner. The book describes a timely topic for both early -and mid-career researchers. Every year, more papers continue to be published on knowledge graph construction, especially for difficult Web domains. This book serves as a useful reference, as well as anaccessible but rigorous overview of this body of work. The book presents interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. The book also appeals to practitioners in industry and data scientists since it has chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations.
9783030123758
10.1007/978-3-030-12375-8 doi
Data mining.
Information storage and retrieval systems.
Application software.
Computer science--Mathematics.
Mathematical statistics.
Data Mining and Knowledge Discovery.
Information Storage and Retrieval.
Computer and Information Systems Applications.
Probability and Statistics in Computer Science.
QA76.9.D343
006.312
Domain-Specific Knowledge Graph Construction [electronic resource] / by Mayank Kejriwal. - 1st ed. 2019. - XIV, 107 p. 19 illus. online resource. - SpringerBriefs in Computer Science, 2191-5776 . - SpringerBriefs in Computer Science, .
1. What is a knowledge graph? -- 2. Information Extraction -- 3. Entity Resolution -- 4. Advanced Topic: Knowledge Graph Completion -- 5. Ecosystems .
The vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as knowledge graphs. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This book will synthesize Knowledge Graph Construction over Web Data in an engaging and accessible manner. The book describes a timely topic for both early -and mid-career researchers. Every year, more papers continue to be published on knowledge graph construction, especially for difficult Web domains. This book serves as a useful reference, as well as anaccessible but rigorous overview of this body of work. The book presents interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. The book also appeals to practitioners in industry and data scientists since it has chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations.
9783030123758
10.1007/978-3-030-12375-8 doi
Data mining.
Information storage and retrieval systems.
Application software.
Computer science--Mathematics.
Mathematical statistics.
Data Mining and Knowledge Discovery.
Information Storage and Retrieval.
Computer and Information Systems Applications.
Probability and Statistics in Computer Science.
QA76.9.D343
006.312