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</o:shapelayout></xml><![endif]--></head><body lang=ZH-CN link=blue vlink=purple style='text-justify-trim:punctuation'><div class=WordSection1><p class=MsoPlainText><span lang=EN-US>Title: Tackling Usability Challenges in Querying and Exploring Entity Graphs<o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p><p class=MsoPlainText><span lang=EN-US>Time: Wednesday, Dec 18, 2013, 3 PM<o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US>Venue: SEIEE-03-414<o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US>Host: Kenny Q. Zhu (</span><span style='font-family:宋体'>朱其立</span><span lang=EN-US>)<o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p><p class=MsoPlainText><span lang=EN-US>Abstract:<o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p><p class=MsoPlainText><span lang=EN-US>We witness an unprecedented proliferation of entity graphs that capture entities (e.g., persons, products, organizations) and their relationships. Real-world entity graphs include knowledge bases, social graphs, citation graphs, drug and disease databases, and program analysis graphs, to name just a few. Users and developers are trying hard to tap into entity graphs for numerous applications, including search, recommendation systems, business intelligence and health informatics. Both users and application developers are often overwhelmed by the daunting task of understanding and using entity graphs. The challenges lie in the gap between complex/big data and non-expert users. In retrieving data from entity graphs, the norm is often to use structured query languages such as SQL, SPARQL, and those alike. However, graph data is not </span><span lang=EN-US style='font-family:"Courier New"'>“</span><span lang=EN-US>easier</span><span lang=EN-US style='font-family:"Courier New"'>”</span><span lang=EN-US> than relational data in either query language or data model. If querying </span><span lang=EN-US style='font-family:"Courier New"'>“</span><span lang=EN-US>simple</span><span lang=EN-US style='font-family:"Courier New"'>”</span><span lang=EN-US> tables is difficult, aren</span><span lang=EN-US style='font-family:"Courier New"'>’</span><span lang=EN-US>t complex graphs harder to query?<o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p><p class=MsoPlainText><span lang=EN-US>In this talk, I will introduce my group's ongoing efforts in tackling the usability challenges in querying and exploring entity graphs. Specifically, I will discuss GQBE, a system that queries graphs by examples and TableView, a technique that generates preview tables for entity graphs. I will also give an overview of our projects on computational journalism and entity query/exploration in Web text.<o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p><p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p><p class=MsoPlainText><span lang=EN-US>Bio:<o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p><p class=MsoPlainText><span lang=EN-US>Dr. Chengkai Li is an Associate Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington. He received his Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign in 2007, and an M.E. and a B.S. degree in Computer Science from Nanjing University, in 2000 and 1997, respectively. After graduation in 2007, he became an Assistant Professor in the Computer Science and Engineering Department of UT Arlington and was promoted to Associate Professor in 2013. Dr. Li's research interests are in the areas of database, data mining and information retrieval, with the current emphasis on building large-scale human-assisting and human-assisted data and information systems with high usability, low cost and applications for social good. In particular, he works on computational journalism, crowdsourcing and human computation, database exploration by ranking (top-k), skyline and preference queries, database testing, entity search, query and exploration, query processing and optimization, usability challenges in using entity graphs, and Web data management. Dr. Li's papers have appeared in prestigious database, data mining and Web conferences including SIGMOD, VLDB, ICDE, EDBT, KDD, WWW, WSDM and CIKM, as well as in several leading journals such as TKDD and TKDE. He has served in the organizing committee of IEEE IPCCC several times (as General Co-Chair in 2012 and Program Co-Chair in 2010) and in the program committees of premier conferences such as VLDB, ICDE, EDBT, WWW, CIKM and ICDM. He has also been a reviewer for multiple prestigious journals, e.g., TODS, TOIS, TKDE and VLDB Journal. Dr. Li is a recipient of the 2011 and 2012 HP Labs Innovation Research Award.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:10.5pt'><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:10.5pt'><o:p> </o:p></span></p></div></body></html>