Knowledge Graphs are graph structures that capture knowledge in the form of entities and the relationships between them, and optionally the provenance information. Along with Semantic Web standards such as RDF, OWL, and SPARQL, advances in Machine Learning, Deep Learning, Natural Language Processing, and Information Retrieval has led to automated construction of knowledge graphs such as DBpedia, YAGO, Wikidata, Google’s and LinkedIn’s Knowledge Graph, Microsoft’s Satori, and Product Knowledge Graph from Amazon and eBay. Knowledge Graphs are used in several applications such as search, question answering, data integration, recommendation systems etc., across several domains such as healthcare, geosciences, manufacturing, aviation, power, oil and gas. There are several challenges related to knowledge graphs from the perspective of both the technology and its applications. This workshop aims to foster discussions along these perspectives.
Topics of interest include, but are not limited to the following. We especially encourage Knowledge Graph applications related to web search, conversational agents, recommendation systems, and in the Industrial domain such as manufacturing, aviation, power, oil and gas.
Handling noisy and incomplete data
Entity linking and resolution
Consistency checking when adding new knowledge
Collaborative maintenance of knowledge graphs
Provenance solutions for Knowledge Graphs
Handling uncertain content in Knowledge Graphs
Innovative methods for querying and interacting with knowledge graphs
Searching over knowledge graphs
Reasoning over knowledge graphs
Explaining knowledge graph contents
Machine Learning and deep learning techniques for knowledge graph mining
Heterogeneous graph mining
Graph databases, triple stores
New storage and indexing schemes for property graphs
Web search, conversational agents, recommender systems, information access systems, and summary generation
manufacturing, aviation, power, oil and gas, healthcare, banking, finance, and IoT
manufacturing, aviation, power, oil and gas, healthcare, banking, finance, and IoT
Industry use cases and best practices
Authors can submit either full papers of 8 pages in length or short papers of 4 pages length in the ACM format (https://www.acm.org/publications/proceedings-template), with the "sigconf" option. Since we plan to follow single-blind review process, there is no need to anonymize the author list. Since the workshop papers will be included in the companion volume of The Web Conference proceedings, it is important to follow the suggested submission format. Submissions can be made using EasyChair at (https://easychair.org/conferences/?conf=kgtawww19). Authors of the accepted papers will be invited to give a short lightning talk of 5 minutes and/or present a poster at the workshop.
High quality submissions with substantial revisions will be invited to submit to Data Intelligence Journal (http://www.data-intelligence-journal.org/) and the Special Issue on “Linked Data and Knowledge Graph in Large Organisations” at the Information Journal (https://www.mdpi.com/journal/information/special_issues/Knowledge_Graphs).
More members will be added as required
Mark Musen, Stanford, USA
Stefan Decker, Fraunhofer FIT, Germany
Jie Tang, Tsinghua University, China
Paul Groth, Elsevier, the Netherlands
Barend Mons, Leiden University, the Netherlands
Yizhou Sun, UCLA, USA
Michel Dumontier, Universiteit Maastricht, the Netherlands
Natasha Noy, Google, USA
Ramanathan V. Guha, Google
Andrew Moore, CMU, USA
Soren Auer, Leibniz University of Hannover, Germany
Juan Sequeda, Capsenta, USA
Freddy Lecue, Accenture Technology Labs, Ireland
Steve Gustafson, Maana, USA
Craig Knoblock, University of Southern California, USA
Pascal Hitzler, Wright State University, USA
Tim Finin, University of Maryland, Baltimore County, USA
Axel Polleres, WU Vienna, Austria
Amelie Gyrard, Wright State University, USA
Pankesh Patel, Fraunhofer, USA
Peter Haase, metaphacts GmbH, Germany
Muhammad Intizar Ali, Insight Center, Ireland
Krzysztof Janowicz, UCSB, USA
Paul Groth, Elsevier Labs, Netherlands
The workshop participants will be open for the whole conference. Each submitted paper will be evaluated by three reviewers from the aspects of novelty, significance, technique sound, experiments, and presentations. The reviewers will be program committee members or researchers recommended by the members.
Paper submissions: Feb 10, 2019
Author notifications: Feb 25, 2019
Camera-Ready version: Mar 1, 2019
OpenKG.CN
中文开放知识图谱(简称OpenKG.CN)旨在促进中文知识图谱数据的开放与互联,促进知识图谱和语义技术的普及和广泛应用。
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