A Knowledge-Based Approach to Ontologies Data Integration Maria Vargas-Vera and Enrico Motta Knowledge Media Institute (KMi), The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom {m.vargas-vera@open.ac.uk} Abstract This paper describes a proposal of multiple ontology data integration system for a question answering framework called AQUA. We propose an approach for mediating between a given query and a set of resources. This method is based on a Meta-ontology (which contains contents of each individual sources) and our similarity algorithm based on analysis of neighborhood of classes. We argue that AQUA can perform mappings between queries and an ontological space by using a mediator agent based on a Meta-ontology and our similarity algorithm. Keywords: Ontologies, Data Integration, Meta-ontology, Similarity 1. Introduction This paper focuses on the problem of incorporating an integration system to AQUA (Vargas-Vera and Motta 2004; Vargas-Vera et al 2003a,b), with a mediator agent. AQUA was developed at the Open University in England, United Kingdom. AQUA joins two different paradigms of closed- domain and open domain question answering into a single framework. One of the main characteristics of AQUA is that it uses knowledge (encoded in ontologies). This knowledge is used in several steps of the question answering process like query reformulation. AQUA translates user query written in English to first order logic (FOL). Currently, AQUA when is used as closed-domain question answering uses a single populated ontology. Therefore, we aimed to extend the architecture in order to handle multiple-ontologies. To achieve this goal we needed a mediator agent which could perform mappings between terms in queries and ontological relations. Our solution to the ontology data integration problem was the use a Meta-ontology coupled with our similarity algorithm (described in section 3.1). This Meta-ontology contains information about relations of each resource. This is not a limitation of the system because new meta- information (a new resource) can be added incrementally. The process of answering a query is divided in four steps 1. A query planner which takes a given a query written as first order logic (FOL) and divides into sub-queries. 2. A selection procedure provides with a subset of ontologies relevant to the query. 3. The query-satisfaction algorithm answers the using standard techniques in question answering already implemented in AQUA Vargas-Vera and Motta 2004; Vargas-Vera et al 2003a,b).