Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering

This volume contains some lecture notes of the 12th Reasoning Web Summer School (RW 2016), held in Aberdeen, UK, in September 2016. In 2016, the theme of the school was Logical Foundation of Knowledge Graph Construction and Query Answering. The notion of knowledge graph has become popular since Google started to use it to improve its search engine in 2012. Inspired by the success of Google, knowledge graphs are gaining momentum in the World Wide Web arena. Recent years have witnessed increasing industrial take-ups by other Internet giants, including Facebook's Open Graph and Microsoft's Satori.

The aim of the lecture note is to provide a logical foundation for constructing and querying knowledge graphs. Our journey starts from the introduction of Knowledge Graph as well as its history, and the construction of knowledge graphs by considering both explicit and implicit author intentions. The book will then cover various topics, including how to revise and reuseontologies (schema of knowledge graphs) in a safe way, how to combine navigational queries with basic pattern matching queries for knowledge graph, how to setup a environment to do experiments on knowledge graphs, how to deal with inconsistencies and fuzziness in ontologies and knowledge graphs, and how to combine machine learning and machine reasoning for knowledge graphs.

Made for students, researchers and practitioners interested in Knowledge Graphs, Question Answering, and Semantic Web Thoroughly revised tutorials cover logical foundations for constructing and querying knowledge graphs, linked data, semantics, fuzzy RDF and OWL knowledge bases Original, readable and useful lecture notes Includes supplementary material: sn.pub/extras

Autorentext
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 the Chief Editor of the book of 'Exploiting Linked Data and Knowledge Graphs in Large Organisations'. He is known for his work on knowledge construction, reasoning and exploitation.

Diego Calvanese is a Full Professor at Free University of Bozen-Bolzano. He is one of the editors of the Description Logic Handbook and a member of the Editorial Board of JAIR and Big Data Research. He has been nominated EurAI Fellow in 2015.

Thomas Eiter is a Full Professor at TU Wien. He is an expert on Knowledge Representation and Non-monotonic Logic Programming. He is a EurAI Fellow, a Corresponding Member of the Austrian Academy of Sciences (ÖAW) and a Member of the Academia Europea.

Ian Horrocks is a Full Professor at Oxford. He chaired the W3C OWL WG and is an Editor-in-Chief of the Journal of Web Semantics. He won the BCS Roger Needham award and is a Fellow of the Royal Society, a Fellow of EurAI and a Fellow of the Academia Europea. He is the Scientific Coordinator of SIRIUS.

Michael Kifer is a Full Professor at Stony Brook University. He won the ACM SIGMOD Test of Time Award twice for the work on object-oriented query languages and the work on F-Logic. He won 20-year Test of Time Award from the Association for Logic Programming for the work on Transaction Logic.

Fangzhen Lin is a Full Professor at Hong Kong University of Science and Technology . He was awarded the Croucher Senior Research Fellowship (2006) and won many best papers awards in top Knowledge Representation and Artificial Intelligence conferences including KR06, KR08, IJCAI97 and AIPS2000. He was an Associate Editor of AI Journal and JAIR. He is an AAAI Fellow (2017).

Yuting Zhao is a Senior Researcher at the Center for Advanced Studies at IBM Italy. Before that, he was a Research Fellow in Prof Jeff Z. Pan's group at University of Aberdeen. He co-edited two books with Prof Pan on Semantic Web and Software Engineering, including 'Ontology Driven Software Development'.

Inhalt

Understanding Author Intentions: Test Driven Knowledge Graph Construction.- Inseparability and Conservative Extensions of Description Logic Ontologies: A Survey .- Navigational and Rule-Based Languages for Graph Databases.- LOD Lab: Scalable Linked Data Processing.- Inconsistency-Tolerant Querying of Description Logic Knowledge Bases.- From Fuzzy to Annotated Semantic Web Languages.- Applying Machine Reasoning and Learning in Real World Applications.