Visiting from the National University of Singapore, Tok Wang Ling will discuss his latest research into refining keyword search over relational databases using semantics.
Keyword search over relational databases has been widely studied in recent years. Relational keyword search can be broadly classified into two categories: (a) data graph approach and (b) schema graph approach. In data graph approach, the relational database is modelled as a graph where each node represents a tuple and each edge represents a foreign key-key reference. An answer to a keyword query is typically defined as a minimal connected subgraph, which contains all the keywords.
In this talk, we identify the serious limitations of existing RDB keyword search, such as, that answers depend on the normal form of the RDB; incomplete answers; meaningless answers; difficulties to understand the meanings of the answers; etc. Our works classify the relations in a relational database into object relations, relationship relations, mixed relations, and their component relations. We refer to these semantics as the Object-Relationship-Mixed (ORM) semantics. Based on the ORM semantics, we construct an Object-Relationship-Mixed (ORM) data graph where each node represents either an object, or a relationship, or both, in the database. A semantic approach to RDB keyword search via the ORM data graph is proposed to solve the existing limitations of RDB keyword search.
Professor Tok Wang Ling is based in the Department of Computer Science at the National University of Singapore. His current research interests include Database Modeling, Entity-Relationship Approach, Object-Oriented Data Model, Normalization Theory, Semi-Structured Data Model, XML Twig Pattern Query Processing, XML and Relational Database Keyword Query Processing.
He serves or has served on the steering committees of 5 international conferences, and has served as Conference Co-chair of 11 international conferences. He served as Program Committee Co-chair of 6 international conferences and received the ACM Recognition of Service Award in 2007, the DASFAA Outstanding Contributions Award in 2010, and the Peter P. Chen Award in 2011.
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