Supporting Top-K Keyword Search in XML Databases

Keyword search is considered to be an effective information discovery method for both structured and semistructured data. In XML keyword search, query semantics is based on the concept of Lowest Common Ancestor (LCA). However, naive LCA-based semantics leads to exponential computation and result size. In the literature, LCA-based semantic variants (e.g. ELCA and SLCA) were proposed, which define a subset of all the LCAs as results. While most existing work focuses on algorithmic efficiency, top-K processing for XML keyword search is an important issue that has received very little attention. Existing algorithms focusing on efficiency are designed to optimize the semantic pruning and are incapable of supporting top-K processing. On the other hand, straightforward applications of top-K techniques from other areas (e.g. relational databases) generate LCAs that may not be the results and unnecessarily expand efforts in the semantic pruning. In this paper, we propose a series of join-based ...

• Type: Conference
• Year: 2010
• Where: ICDE
• Authors: Liang Jeff Chen, Yannis Papakonstantinou
• Keywords: Database, ICDE 2010, Keyword Query Evaluation, Semantic Pruning, XML Keyword Search