Evaluation of Terminological Schema Matching and Its Implications for Schema Mapping Sarawat Anam 1,2 , Yang Sok Kim 1 , Byeong Ho Kang 1 and Qing Liu 2 1 School of Computing and Information Systems, University of Tasmania, Sandy Bay, Tasmania, Australia {Sarawat.Anam, YangSok.Kim, Byeong.Kang}@utas.edu.au 2 Intelligent Sensing and Systems Laboratory, CSIRO Computational Informatics, Hobart, Tasmania, Australia Q.Liu@csiro.au Abstract. Recently large amounts of schema data, which describe data structure of various domains such as purchase order, health, publication, geography, ag- riculture, environment and music, are available over the Web. Schema mapping aims to solve schema heterogeneity problem in schema data. This research thoroughly examines how string similarity metrics and text processing tech- niques impact on the performance of terminological schema mapping and high- lights their limitations. Our experimental study demonstrates that the perfor- mance of terminological schema matching is significantly improved by using text processing techniques. However, the performance improvement is slightly different between datasets because of the characteristics of the datasets, and in spite of applying all text processing techniques, some datasets still exhibit low performance. Our research supports the claim that a system which can manage the context dependent characteristics of terminological schema matching is es- sential for better schema mapping algorithms. Keywords: Schema mapping, terminological schema matching, string metrics 1 Introduction There are many different data/conceptual models, including relational database sche- mas, XML-schemas, entity-relationship models, conceptual graphs and UML dia- grams. Mapping takes as input two schemas/ontologies, each consisting of a set of discrete entities, and determines as output the relationships holding between these entities [1]. Most mapping research has been carried out among database schemas, XML-schemas, and ontologies. Our research focuses on schema mapping in XML- schemas over the Web. Schema mapping is necessary in many application domains such as data integration, data exchange, data warehousing and schema evolution [2]. Schema mapping can be conducted by schema matching systems, which combine different mapping algorithms with a mapping selection module [3]. There are three different schema matching approaches - terminological, Structural and semantic matching techniques. Terminological matching compares the schema names using