On ambiguity and query-specific ontology mapping

In the course of developing an ontology-based data integration system (OBDI) that includes automatic integration of data sources, and thus, includes algorithmic ontology mapping, we have made the following observations. A mapping method may determine that an entity in one ontology maps with equal likelihood to two or more entities in the other ontology. The mapping and reformulation of certain queries is correct only if one pairing is chosen. The correct choice may be different for different queries. Finally, the query itself may lend additional semantics that correctly resolve the ambiguity. These observations suggest a targeted ontology mapping problem, query-specific ontology mapping. In addition to the two ontologies, a query serves as a third argument to the mapping algorithm. Further, the mapping algorithm need not produce a complete mapping, but only a partial mapping sufficient to correctly reformulate the query. We detail a number of open issues on how this problem statement might be refined, and consider features of its evaluation. Ambiguity in Ontology Mapping: Consider the idealized representation (Fig. 1) of a critical issue in the automatic integration of new data sources in an OBDI system. T and S respectively represent target and data source ontologies. Looking at the ontologies alone, there is insufficient information to determine if the class T:People should be mapped to S:Teacher or to S:Student. A third possibility is a one-to-many mapping entailing both. Given the SPARQL query (Fig. 1c), it becomes clear that the query should be reformulated using only the mapping {T:People = S:Teacher}. A complementary query about students should be reformulated using only the complementary mapping. Thus, any static chose of one mapping will yield reformulated queries that return incorrect results. Formulations of Query-Specific Ontology Mapping: In our system we compute a similarity matrix between all entities in the two ontologies [3]. The details may be borrowed from any ontology mapping algorithm that includes this step [2]. Given a query on the target ontology, our system uses a joint probability model to identify a maximal scoring, partial mapping that covers the target ontology entities mentioned in the query or that are needed to reformulate the query. Thus, our solution can be characterized as one that takes three arguments, and produces a partial mapping specific to the query. There are at least two other approaches that may be considered and that produce a complete mapping and thus retain more of the standard definition of ontology matching. First is to consider complex mappings. For example, instead of choosing {T:People =