Peer-to-peer semantic coordination

Semantic coordination, namely the problem of finding an agreement on the meaning of heterogeneous schemas, is one of the key issues in the development of the Semantic Web (see [11, 10, 9, 8, 3, 5, 7, 1, 4, 6] for examples of proposed techniques). In environments with more or less well-defined boundaries, like a corporate Intranet, the problem of semantic coordination can be addressed a priori by defining and using shared schemas (e.g. ontologies) throughout the entire organization1. However, in open environments, like the Semantic Web, this “centralized” approach to semantic coordination is not viable for several reasons, such as the difficulty of “negotiating” a shared model that suits the needs of all parties involved, the practical impossibility of maintaining such a model in a highly dynamic environment, the problem of finding a satisfactory mapping of pre-existing local schemas onto such a global model. In such a scenario, the problem of exchanging meaningful information across locally defined schemas (each possibly presupposing heterogeneous semantic models) seems particularly tough, as we cannot assume an a priori agreement, and therefore its solution requires a more dynamic and flexible form of coordination, which we call “peer-to-peer” semantic coordination. In this paper, we address an important instance of the problem of peer-to-peer semantic coordination, namely the problem of coordinating hierarchical classifications (HCs). HCs are structures having the explicit purpose of organizing/classifying some kind of data (such as documents, records in a database, goods, activities, services). The main technical contribution is a logic–based algorithm, called CTXMATCH, for coordinating HCs. It takes in input two HCs S and S ′ and, for each pair of concepts m ∈ S and n ∈ S , returns their semantic relation. The relations we consider in this version of CTXMATCH are: m is less general than n, m is more general than n, m is equivalent to n, m is compatible with (possibly overlappings) n, and m is incompatible with (i.e., disjoint from) n. The formal semantics of these relations will be made precise in the paper. With respect to other approaches to semantic coordination proposed in the literature (often under different “headings”, such as schema matching, ontology mapping, semantic integration), our approach is innovative in three main aspects: (1) we introduce a new method for making explicit the meaning of nodes in a HC (and in general, in structured semantic models) by combining three different types of knowledge, each of which has a specific role; (2) the result of applying this method is that we are able to produce a new representation of a HC, in which all relevant knowledge about the nodes (including their meaning in that specific HC) is encoded as a set of logical formulae; (3) mappings across nodes of two HCs are then deduced via logical reasoning, rather then derived through some more or less complex heuristic procedure, and thus can be assigned a clearly defined model-theoretic semantics. As we will show, this leads to a major conceptual shift, as the problem of semantic coordination between HCs is no longer tackled as a problem of computing linguistic or structural similarities (possibly with the help of a thesaurus and of other information about the type of arcs between nodes), but rather as a problem of deducing relations between formulae that represent the meaning of each concept in a given HC. This explains, for example, why our approach performs much better than other ones when two concepts are intuitively equivalent, but occur in