ASER: A Large-scale Eventuality Knowledge Graph
Yangqiu Song, Ph.D.
Department of Computer Science and Engineering
Hong Kong University of Science and Technology
Dr. Yangqiu Song is an assistant professor at HKUST. Before that, he was an assistant professor at WVU (2015-2016), a post-doctoral researcher at the Cognitive Computation Group at UIUC (2013-2015), a post-doctoral fellow at HKUST and visiting researcher at Huawei Noah's Ark Lab, Hong Kong (2012-2013), an associate researcher at Microsoft Research Asia (2010-2012) and a staff researcher at IBM Research China (2009-2010) respectively. He received his B.E. and Ph.D. degrees from Tsinghua University, China, in July 2003 and January 2009, respectively. His current research focuses on using machine learning and data mining to extract and infer insightful knowledge from big data. The knowledge helps users better enjoy their daily living and social activities, or helps data scientists do better data analytics. He is particularly interested in large scale learning algorithms, natural language understanding, text mining, and knowledge graph related research.
Understanding human's language requires complex world knowledge. However, existing large-scale knowledge graphs mainly focus on knowledge about entities while ignoring knowledge about activities, states, or events, which are used to describe how entities or things act in the real world. To fill this gap, we develop ASER (activities, states, events, and their relations), a large-scale eventuality knowledge graph extracted from more than 11-billion-token unstructured textual data. ASER contains 15 relation types belonging to five categories, 194-million unique eventualities, and 64-million unique edges among them. Both human and extrinsic evaluations demonstrate the quality and effectiveness of ASER.