Invited Talk by Yang Song
Title: Knowledge-powered Next Generation Scholarly Search and Recommendation Engines
Abstract: In recent years, knowledge bases (KB) such as Freebase, Microsoft Satori, and Google Knowledge Graph have drawn more and more attention from both academic researchers and industrial practitioners. Thanks to the huge volume of structured information, KBs have introduced an evolutionary change and advanced the state-of-the-art in many research areas including Web Search, Natural Language Processing, Semantic Web, Recommender Systems, Machine Learning and so on. Research on digital libraries and academic search is an important area for both academia and industry. In this talk, I will focus on the latest development on knowledge-powered academic search and recommendation engines, in which we bridge the gap between unstructured text and structured relationships, by converting the massive amount of unstructured scholar data on the Web into structured entities in KBs. We will show that the usage of KBs have brought unprecedented new opportunities for researchers in digital libraries and related areas.
Speaker: Dr. Yang Song is a Researcher at Microsoft Research Redmond, where he works in the Internet Services and Research Center (ISRC). Dr. Song's research interests include information retrieval, machine learning, recommender systems and etc. He has been with MSR for over 6 years and worked with many teams in Bing including data mining, ranking, relevance, personalization, query understanding and etc. He has authored/co-authored over 35 scientific papers in journals and conferences like ACM TWEB, IEEE TKDE, WWW, SIGIR, WSDM, CIKM and etc. Dr. Song holds a Ph.D. degree in Computer Science and Engineering from Pennsylvania State University, where he worked on the CiteSeer scholarly search engine, and a B.S. degree in Computer Science from Zhejiang University, China. More information about him can be found on his homepage at MSR: http://research.microsoft.com/en-us/people/yangsong/
Invited Talk by Vu Ha
Title: Semantic Scholar: Toward a Semantic Search and Discovery Service for Scientific Papers
Abstract: I will talk about the Semantic Scholar project at the Allen Institute for Artificial Intelligence. The first goal is to help researchers by building a paper search and discovery service with semantic capabilities. The second goal is to use Semantic Scholar as a test bed for research on AI problems like machine reading and computer vision.
Speaker: Vu Ha is the technical lead of the Semantic Scholar project at the Allen Institute for Artificial Intelligence. He is interested in building online consumer products that push the envelope of information search, discovery, and personalization. Previously he spent seven years as an engineering and research lead at Microsoft's Bing where he built the original, widely-used advertising taxonomy and its associated classifier. He has a PhD in AI from the University of Wisconsin.