Discovering complex matchings across web query interfaces: a correlation mining approach

To enable information integration, schema matching is a critical step for discovering semantic correspondences of attributes across heterogeneous sources. While complex matchings are common, because of their far more complex search space, most existing techniques focus on simple 1:1 matchings. To tackle this challenge, this paper takes a conceptually novel approach by viewing schema matching as correlation mining, for our task of matching Web query interfaces to integrate the myriad databases on the Internet. On this "deep Web," query interfaces generally form complex matchings between attribute groups (e.g., {author} corresponds to {first name, last name} in the Books domain). We observe that the cooccurrences patterns across query interfaces often reveal such complex semantic relationships: grouping attributes (e.g., {first name, last name}) tend to be co-present in query interfaces and thus positively correlated. In contrast, synonym attributes are negatively correlated b...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Bin He, Kevin Chen-Chuan Chang, Jiawei Han
• Keywords: Complex Matchings, Data Mining, Form Complex Matchings, KDD 2004, Query Interfaces