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清华大数据论坛——图数据管理与分析 May 13, 2019, 14:00-17:00 East Main Building东主楼10-316 Hosted by Tsinghua University Big Data Research Center
Program Overview13 May, 2019 (Monday) Time |
| Speaker | Host | 14:00-14:05 | Welcome | Jianmin Wang | Shaoxu Song | 14:05-14:55 | Keynote 1: Natural Language Question Answering over Knowledge Graph 知识图谱中的自然语言问答 | Lei Zou | 14:55-15:00 | Q & A |
| 15:00-15:50 | Keynote 2: Efficient Maximal Clique Computation over Large Sparse Graphs 大规模稀疏图中的最大团计算 | Lijun Chang | 15:50-15:55 | Q & A |
| 15:55-16:45 | Keynote 3: High-Quality Community Search on Graphs 图中高质量社区搜索 | Xin Huang | 16:45-16:50 | Q & A |
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Detailed ProgramKeynote 1Title: Natural Language Question Answering over Knowledge Graph Presenter: Lei Zou Abstract: As more and more structured data become available on the web, the question of how end users can access this body of knowledge becomes of crucial importance. As a de facto standard of a knowledge base, RDF (Resource Description Framework) repository is a collection of triples, denoted as In this talk, I first review two categories of existing methods on natural language question answering (Q/A) over RDF knowledge graph---one is IR (Information Retrieval)-based and the other one is called semantic parsing method. Then, I will talk about our RDF Q/A system (gAnswer), which is based on graph matching-based technique. The most challenge to RDF Q/A task is the ambiguity of natural language question sentence. The contribution of our method is that we combine the disambiguation and query evaluation in a uniform process, i.e., we push down the disambiguation into the query evaluation stage. Based on the query results over RDF graphs, we can address the ambiguity issue efficiently. gAnswer joined QALD-6 knowledge graph Q/A competition (hosted by ESWC) and it won the second place in the Q/A precision. We host an online demo of our system at ganswer.gstore-pku.com and the relevant source codes are released at Github https://github.com/pkumod/gAnswer Keynote 2Title: Efficient Maximal Clique Computation over Large Sparse Graphs Presenter: Lijun Chang Abstract: Maximum clique computation, although being a classic NP-hard problem, has been extensively studied in the literature due to its importance in analyzing large real-world graphs. In this talk, I will give an overview of the existing maximum clique computation algorithms over large sparse graphs, and present our new MC-BRB algorithm that has been accepted to KDD 2019. In MC-BRB, we first transform an instance of maximum clique computation over sparse graphs to instances of k-clique finding over dense subgraphs, and then develop a branch-reduce-and-bound framework for k-clique finding over dense graphs. In addition, we also designed an ego-centric algorithm MC-EGO for heuristically computing a near-maximum clique in near-linear time. Keynote 3Title: High-Quality Community Search on Graphs Presenter: Xin Huang Abstract: Communities serve naturally exist in many real-world networks, such as social, biological, collaboration, and communication networks. High-quality communities are important for understanding the organization of complex networks. Recently, community search over large graphs has attracted significantly increasing attention, from simple and static graphs to evolving, attributed, location-based graphs. In this talk, we introduce the state-of-the-art community search on various kinds of network data. Most community models adopt the well-known dense subgraphs of k-core and k-truss to achieve high-quality cohesive structure and efficient computations. Moreover, to fairly evaluate the community quality on public-private networks, we generated and released five ground-truth datasets using real-world DBLP records as benchmarks. This talk finally highlights the challenges and opportunities in this important and growing area.
Invited Speakers
Prof. Lei Zou Professor Peking University Lei Zou is a professor in the Institute of Computer Science and Technology of Peking University. He received his BS degree and Ph.D. degree in Computer Science at Huazhong University of Science and Technology (HUST) in 2003 and 2009, respectively. He received a CCF (China Computer Federation) Doctoral Dissertation Nomination Award in 2009, won Second Class Prize of CCF Natural Science Award in 2014 and Second Class Prize of Natural Science of the Ministry of Education, China in 2017. His recent research interests include graph databases, knowledge graph, particularly in graph-based RDF data management. He has published more than 50 papers, including more than 30 papers published in reputed journals and major international conferences, such as SIGMOD, VLDB, ICDE, TODS, TKDE, VLDB Journal. |
Prof. Lijun Chang Senior Lecturer The University of Sydney Dr. Lijun Chang is a Senior Lecturer and Future Fellow in the School of Computer Science at the University of Sydney. He received Bachelor degree from Renmin University of China in 2007, and Ph.D. degree from The Chinese University of Hong Kong in 2011. He worked as a Postdoc and then DECRA research fellow at the University of New South Wales from 2012 to 2017. His research interests are in the fields of big graph (network) analytics, with a focus on designing practical algorithms and developing theoretical foundations for massive graph analysis. He has co-authored two monographs, and published over 50 papers in top venues such as SIGMOD, KDD, PVLDB, ICDE, VLDB Journal, TKDE, and Algorithmica. |
Prof. Xin Huang Assistant Professor Hong Kong Baptist University Xin Huang is an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University. He received the Ph.D. degree from the Department of Systems Engineering and Engineering Management at the Chinese University of Hong Kong in 2014. His research interests are graph data management, big graph mining and visualization, social network analysis, and privacy-aware computing. His works are published in several top-tier database conference/journal including ACM SIGMOD, VLDB, IEEE ICDE, and VLDB Journal. He serves in over 30 organization committees and program committees for international conferences/workshops including the VLDB, ICDE, KDD, WWW, and also the Publication Chair of WISE'2019. |
Contact PersonShaoxu Song Tsinghua University, China sxsong@tsinghua.edu.cn
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