Exploiting Linked Data and Knowledge Graphs in Large Organisations

Jeff Z. Pan

ISBN 10: 3319833391 / ISBN 13: 9783319833392

Published by Springer International Publishing, Springer International Publishing, 2018

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book addresses the topic of exploiting enterprise-linked data with a particular focus on knowledge construction and accessibility within enterprises. It identifies the gaps between the requirements of enterprise knowledge consumption and 'standard' data consuming technologies by analysing real-world use cases, and proposes the enterprise knowledge graph to fill such gaps.It provides concrete guidelines for effectively deploying linked-data graphs within and across business organizations. It is divided into three parts, focusing on the key technologies for constructing, understanding and employing knowledge graphs. Part 1 introduces basic background information and technologies, and presents a simple architecture to elucidate the main phases and tasks required during the lifecycle of knowledge graphs. Part 2 focuses on technical aspects; it starts with state-of-the art knowledge-graph construction approaches, and then discusses exploration and exploitation techniques as well as advanced question-answering topics concerning knowledge graphs. Lastly, Part 3 demonstrates examples of successful knowledge graph applications in the media industry, healthcare and cultural heritage, and offers conclusions and future visions.

Seller Inventory # 9783319833392

Bibliographic Details

• Title: Exploiting Linked Data and Knowledge Graphs in Large Organisations
• Publisher: Springer International Publishing, Springer International Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 3319833391
• ISBN 13: 9783319833392
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

This book addresses the topic of exploiting enterprise-linked data with a particular focus on knowledge construction and accessibility within enterprises. It identifies the gaps between the requirements of enterprise knowledge consumption and “standard” data consuming technologies by analysing real-world use cases, and proposes the enterprise knowledge graph to fill such gaps.

It provides concrete guidelines for effectively deploying linked-data graphs within and across business organizations. It is divided into three parts, focusing on the key technologies for constructing, understanding and employing knowledge graphs. Part 1 introduces basic background information and technologies, and presents a simple architecture to elucidate the main phases and tasks required during the lifecycle of knowledge graphs. Part 2 focuses on technical aspects; it starts with state-of-the art knowledge-graph construction approaches, and then discusses exploration and exploitation techniques as well as advanced question-answering topics concerning knowledge graphs.  Lastly, Part 3 demonstrates examples of successful knowledge graph applications in the media industry, healthcare and cultural heritage, and offers conclusions and future visions.

About the Author

About the Editors:

Jeff Z. Pan is a Reader (Professor) at University of Aberdeen. He is the Chief Scientist of the EC Marie Curie K-Drive project and has edited many books/proceedings on Semantic Technologies and Linked Data. He is well known for his work on knowledge construction, reasoning and exploitation. 

Guido Vetere leads the IBM Center for Advanced Studies Italy. He has led/worked in many research and development projects in KR, NLP and ontology. He also leads Senso Comune (www.sensocomune.it), a collaborative initiative for building an open KB of the Italian language.

Jose Manuel Gomez-Perez is the Director R&D at Expert System Iberia (ESI). His expertise is on supporting users in creating, sharing, and accessing knowledge. He has a long experience in European R&D projects, privately-funded technology transfer activities and R&D projects.

Honghan Wu is a data scientist in NIHR Maudsley Biomedical Research Centre at King's College London. His current research focus is on annotating, analysing and searching large scale healthcare data by utilising Knowledge Graph techniques.