The workshop will take place on Sunday, May 14, 2017, at the Hilton Chicago (room Williford A).
The program includes four sessions. In the first session, we will have a brief introduction, and a keynote talk by Bing Liu. The accepted papers will be presented in the other three sessions. Each paper is alloted a 30 minute timeslot, consisting of 25 minutes presentation, and 5 minutes of questions and discussion.
Detailed Program (Tentative)
- 9:00-10:30: Welcome and Keynote
- Introduction and welcome
- Keynote: Bing Liu. Opinions, Deceptions, and Lifelong Machine Learning [slides]
- 10:30-11:00: Coffee break
- 11:00-12:30: Paper session
- "Tell me more" using Ladders in Wikipedia. by Siarhei Bykau, Jihwan Lee, Divesh Srivastava and Yannis Velegrakis. [slides]
- A Path Querying Language for Federation of RDF and Relational Database. by Jiahui Zhang, Xiaowang Zhang and Zhiyong Feng. [slides]
- Enabling Completeness-aware Querying in SPARQL. by Luis Galárraga, Katja Hose and Simon Razniewski. [slides]
- 12:30-14:00: Lunch (provided)
- 14:00-15:30: Paper session
- Crowdsourcing with Diverse Groups of Users. by Sara Cohen and Moran Yashinski. [slides]
- Role-aware Conformity Influence Analysis in Recommender Systems. by Mengzi Tang and Li Li. [video (148MB)]
- Cost-Effective Conceptual Design Over Taxonomies. by Ali Vakilian, Yodsawalai Chodpathumwan, Arash Termehchy and Amir Nayyeri. [slides]
- 15:30-16:00: Coffee break
- 16:00-17:00: Paper session
Keynote Talk
Bing Liu: Opinions, Deceptions, and Lifelong Machine Learning
Abstract. The Web is full of opinions. These opinions have motivated the active research area of opinion mining (OM) (or sentiment analysis). In recent years, the OM research has also spread from computer science to management science, economics, health science, and social sciences due to its importance to the society as a whole. This importance of opinions, however, also gives strong incentives for imposters to post deceptive or fake opinions to secretly promote or to discredit some target products or services without disclosing their true intentions. In this talk, I will first introduce the problems of opinion mining and deceptive opinion detection and their current solution techniques, and then discuss some recent research on lifelong machine learning (LML) to solve the OM problem. Unlike the classic machine learning paradigm, LML aims to learn like humans: learning continuously, accumulating the knowledge learned in the past, using it to help future learning, and adapting it for real-life problem solving.
Bio. Bing Liu is a professor of Computer Science at the University of Illinois at Chicago. He received his Ph.D. in Artificial Intelligence from the University of Edinburgh. His research interests include lifelong machine learning, sentiment analysis and opinion mining, data mining, machine learning, and natural language processing (NLP). Two of his papers have received 10-year Test-of-Time awards from KDD. He also authored four books on lifelong machine learning, sentiment analysis, and Web data mining. His work on sentiment analysis and fake review detection has also been widely reported in the press, including a front-page article in the New York Times. On professional services, he serves as the current Chair of ACM SIGKDD. He has served as program chair of many leading data mining conferences, including KDD, ICDM, CIKM, WSDM, SDM, and PAKDD, as associate editor of leading journals such as TKDE, TWEB, and DMKD, and as area chair or senior PC member of numerous NLP, AI, Web, and data mining conferences. He is a Fellow of ACM, AAAI, and IEEE.