Data Management for Emerging Problems in Large Networks
Graphs are widely used in many application domains, including social networks, knowledge graphs, biological networks, software collaboration, geo-spatial road networks, interactive gaming, among many others. One major challenge for graph querying and mining is that non-professional users are not familiar with the complex schema and information descriptions. It becomes hard for users to formulate a query (e.g., SPARQL or exact subgraph pattern) that can be properly processed by the existing systems. As an example, Freebase that powers Google’s knowledge graph alone has over 22 million entities and 350 million relationships in about 5428 domains. Before users can query anything meaningful over this data, they are often overwhelmed by the daunting task of attempting to even digest and understand it. Without knowing the exact structure of the data and the semantics of the entity labels and their relationships, can we still query them and obtain the relevant results? In this talk, I shall give an overview of our user-friendly, embedding-based, scalable techniques and systems for querying big graphs, including knowledge graphs.
Arijit Khan is an Associate Professor at Aalborg University, Denmark. His PhD is from University of California, Santa Barbara, USA, and he did a post-doc in the Systems group at ETH Zurich, Switzerland. He has been an assistant professor in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. His research is on data management for the emerging problems in large graphs. He is an IEEE senior member and ACM distinguished speaker. He is the author of a book on uncertain graphs and over 70 publications in top venues including ACM SIGMOD, VLDB, IEEE TKDE, IEEE ICDE, SIAM SDM, USENIX ATC, EDBT, The Web Conference (WWW), ACM WSDM, and ACM CIKM. Dr Khan is serving as an associate editor of IEEE TKDE 2019-now, proceedings chair of EDBT 2020, and IEEE ICDE TKDE poster track co-chair 2023