03610nam 22006135 450 991033757490332120251113193539.03-030-12375-810.1007/978-3-030-12375-8(CKB)4100000007758395(MiAaPQ)EBC5724748(DE-He213)978-3-030-12375-8(PPN)235232653(EXLCZ)99410000000775839520190304d2019 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDomain-Specific Knowledge Graph Construction /by Mayank Kejriwal1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (115 pages)SpringerBriefs in Computer Science,2191-57763-030-12374-X 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.SpringerBriefs in Computer Science,2191-5776Data miningInformation storage and retrieval systemsApplication softwareComputer scienceMathematicsMathematical statisticsData Mining and Knowledge DiscoveryInformation Storage and RetrievalComputer and Information Systems ApplicationsProbability and Statistics in Computer ScienceData mining.Information storage and retrieval systems.Application software.Computer scienceMathematics.Mathematical statistics.Data Mining and Knowledge Discovery.Information Storage and Retrieval.Computer and Information Systems Applications.Probability and Statistics in Computer Science.511.5006.33Kejriwal Mayankauthttp://id.loc.gov/vocabulary/relators/aut1063580BOOK9910337574903321Domain-Specific Knowledge Graph Construction2533034UNINA