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.
9:00 | Workshop starts |
9:00-9:30 | Invited Talk: Denny Vrandecic, Google |
9:30-10:00 | Invited Talk: Jure Leskovec, Stanford |
10:00-10:30 | Coffee Break |
10:30-11:20 | Paper Presentation 1. Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolution Network. Shumin Deng, Ningyu Zhang, Wen Zhang, Jiaoyan Chen, Jeff Z. Pan and Huajun Chen 2. How new is the (RDF) news? Assessing knowledge graph completeness over news feed entities. Tomer Sagi, Katja Hose and Yael Wolf 3. Content based News Recommendation via Shortest Entity Distance over Knowledge Graphs. Kevin Joseph and Hui Jiang 4. Trust and Privacy in Knowledge Graphs. Daniel Schwabe and Carlos Laufer |
11:20-12:30 | Panel: Computational Methods about Knowledge Graph 1. Jie Tang, Tsinghua University 2. Jure Leskovec, Stanford 3. Kuansan Wang, Microsoft Academic Search 4. Deborah McGuinness, RPI 5. Hassan Sawaf, Amazon |
12:30-1:30 | Lunch Break |
1:30-2:00 | Invited Talk: Yuqing Gao, Microsoft |
2:00-2:30 | Invited Talk: Doug Raymond, Allen Institute for AI |
2:30-3:30 | Paper Presenation 1. Building a Knowledge Graph for the Air Traffic Management Community Rich Keller 2. WebProtégé: A Cloud-Based Ontology Editor Matthew Horridge, Rafael S Gonçalves, Csongor I Nyulas and Mark A Musen 3. Scalable Knowledge Graph construction over text using Deep Learning based Predicate Mapping Aman Mehta, Aashay Singhal and Kamalakar Karlapalem 4. An Atention-based Model for Joint Extraction of Entities and Relations with Implicit Entity Features Yan Zhou, Longtao Huang, Tao Guo, Songlin Hu and Jizhong Han |
3:30-4:00 | Coffee Break |
4:00-5:30 | Panel: Knowledge Graph Industry Applications 1. Joshua Shinavier, Uber 2. Kim Branson, Genentech 3. Wei Zhang, Alibaba 4. Shima Dastgheib, NuMedii 5. Yuqing Gao, Microsoft 6. Bogdan Arsintescu, LinkedIn 7. Fatma Özcan, IBM Almaden 8. Edgar Meij, Bloomberg 9. David Newman, Wells Fargo |
Construction and Maintenance of Knowledge Graphs
Operations over Knowledge Graphs
Mining Knowledge Graphs
Storage mechanisms for Knowledge Graphs
Knowledge Graphs for NLP and IR
Knowledge Graphs in the industrial domain
Industry use cases and best practices
All papers submitted should have a maximum length of 8 pages and demo papers should be no more than 4 pages. All must be prepared using the ACM camera-ready template. Authors are required to submit their papers electronically in PDF format.
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
Alibaba-Zhejiang University Frontier Tech Center (AZFT)