To manipulate semantic web and integrate different data sources efficiently, automatic schema matching plays a key role. A generic schema matching method generally includes two phases: the linguistic similarity matching phase and the structural similarity matching phase. Since linguistic matching is an essential step for effective schema matching, developing a high accurate linguistic similarity matching scheme is required. In this paper, a schema matching approach called Similarity Yield Matcher (SYM) is proposed. In SYM, a lexical decision tree is presented to determine the linguistic similarity matching of the first phase. A structural matching algorithm is then proposed to find the structure similarity between two tree schemas. The proposed schema matching approach was evaluated by testing on several benchmarks of real schemas and comparing with other methods. The experimental results show that the proposed lexical decision tree substantially improves the linguistic similarity matching effectively and efficiently. The proposed SYM algorithm also performs high effectiveness on 1–1 schema matching.
A Time-Sequence Similarity Matching Algorithm for Seismological Relevant Zones
