Panel: Computational Methods about Knowledge Graph

This panel will focus on the cutting-edge computation methods, which can be applied to knowledge graph, such as latest NLP technologies to extract entities and relationships to build knowledge graphs, machine learning or deep learning methods on mining knowledge graph, and intelligent search or recommendations powered by knowledge graphs.

Panelists are: Professor and the Vice Chair of the Department of Computer Science and Technology of Tsinghua University. I obtained my Ph.D. in DCST of Tsinghua University in 2006. My research interests include artificial intelligence, data mining, social networks, machine learning and knowledge graph, with an emphasis on designing new algorithms for mining social and knowledge networks. Associate Professor of Computer Science at Stanford University. My research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. Problems I investigate are motivated by large scale data, the web and on-line media Managing Director of MSR Outreach, an organization with the mission to serve the research community. In addition to applying the intelligent technologies to make Bing and Cortana smarter in gathering and serving academic knowledge, we are also starting an experimental website, academic.microsoft.com (powered by Academic API), and mobile apps dedicated to exploring new service scenarios for active researchers like myself A leading expert in knowledge representation and reasoning languages and systems and has worked in ontology creation and evolution environments for over 20 years. Most recently, McGuinness is best known for her leadership role in semantic web research, and for her work on explanation, trust, and applications of semantic web technology, particularly for scientific applications. Director of Artificial Intelligence at Amazon Web Services, passionate about opening up new markets and opportunities with smart application of Artificial Intelligence and application-driven research in AI, in particular in language technology, speech processing, computer vision and computational reasoning.