A Hybrid Approach to Schema and Data Integration for Meta-search Engines

In this paper, we describe an approach to schema and data integration for meta-search engines. The integration of heterogeneous, distributed information from the Web is a complicated task, especially the task of schema/data matching and integration. During the matching and integration process, we need to handle syntactic, semantic and structural heterogeneity between multiple information sources. In this paper, our main objective is to resolve semantic conflicts. The data, ontology and information integration communities face similar types of problems, and we leverage techniques developed by these communities. Our approach is a hybrid one, in that we use multiple matching criteria and multiple matchers. We employ several elementlevel, structure-level and ontology-based techniques during the integration process. A domain ontology serves as a global ontology and allows us to resolve semantic heterogeneity. Our matching process handles different mapping cardinalities (1:1, 1:n, n:1, m:n). The mappings derived are used to generate an integrated meta-search query interface, to support query processing in the meta-search engine, and to resolve semantic conflicts arising during result extraction from the source search engines. Experiments conducted in the job search domain show that the cumulative use of element-level, structurelevel and ontology-based techniques increases the correctness of matching during the automatic integration of source search interfaces.