ISENS: A Multi-ontology Query System for the Semantic Deep Web

We present ISENS, a distributed, end-to-end, ontology-based information integration system. In response to a user's query, our system is capable of retrieving facts from data sources that are found in the surface semantic Web as well as inthe semantic Deep Web. Furthermore, it retrieves facts from sources where the data is not directly described in terms of the query ontology. Instead, its ontology can be translated from the query ontology using mapping axioms. In our solution, we use the concept of source relevance to summarize the content of a data source. Our system can then use this information to select the needed sources to answer a given query. Source relevance is general enough that it can be used with both the surface semantic Web and the semantic Deep Web. In this paper, we show how we have incorporated three particular Deep Web data sources into our system to enable answering queries by composing information from the integrated sources.