WWW-19 Workshop on Knowledge Graph Technology and Applications


San Francisco / May 13-17, 2019

WWW2019 Workshop on Knowledge Graph Technology and Applications

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.

Invited Speakers and Panelists

Denny Vrandečić

Google AI

Jure Leskovec

Stanford University

Jie Tang

Tsinghua University

Kuansan Wang


Deborah McGuinness

Rensselaer Polytechnic Institute

Hassan Sawaf


Joshua Shinavier


Kim Branson


Zhang Wei


Shima Dastgheib


Benjamin Han


Bogdan Arsintescu


Fatma Özcan

IBM Almaden

Edgar Meij


Doug Raymond

Allen Institute for AI

David Newman

Wells Fargo

Details of Invited Talks

Title: Embedding logical queries on knowledge graphs

Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict "What drugs are likely to target proteins involved with both diseases X and Y?" -- a query that requires reasoning about all possible proteins that might interact with diseases X and Y.

In this talk we introduce a framework to efficiently make predictions about conjunctive logical queries -- a flexible but tractable subset of first-order logic -- on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum.

Slides: Reasoning in Knowledge Graphs using Deep Learning


Jure Leskovec is Associate Professor of Computer Science at Stanford University, Chief Scientist at Pinterest, and investigator at Chan Zuckerberg Biohub. His research focuses on machine learning and data mining large social, information, and biological networks, their evolution, and the diffusion of information over them. Computation over massive data is at the heart of his research and has applications in computer science, social sciences, and biomedicine. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper awards. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, and his PhD in in machine learning from the Carnegie Mellon University and postdoctoral training at Cornell University.

Location and Agenda

Workshop Location

Hyatt Regency San Francisco hotel

Workshop Agenda (May 13, 2019)

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: Benjamin Han, 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. Benjamin Han, 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.

Important Dates

Feb 10, 2019

Workshop paper submissions

Feb 25, 2019

Workshop paper notifications

Mar 1, 2019

Final submission of workshop program and materials

May 13, 2019

Workshop date


Chen Huajun

Zhengjiang University

Laura Dietz

University of New Hampshire

Ying Ding

Indiana University Bloomington

Wendy Hall

University of Southampton

James Hendler

Rensselaer Polytechnic Institute

Deborah McGuinness

Rensselaer Polytechnic Institute

Edgar Meij

Bloomberg LP

Sam Molyneux

Chan Zuckerberg Initiative

Varish Mulwad

GE Global Research Center

Raghava Mutharaju


Jeff Z. Pan

University of Aberdeen

Xiang Ren

University of Southern California

Jie Tang

Tsinghua University

Alex Wade

Chan Zuckerberg Initiative

Mengting Wan

University of California, San Diego

Chenyan Xiong

Carnegie Mellon University

Min Zhang

Tsinghua University

Program Committee

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)