Semantic Coordination: A New Approach and an Application

Abstract

Abstract. Semantic coordination, namely the problem of finding an agreement on the meaning of heterogeneous semantic models, is one of the key issues in the development of the Semantic Web. In this paper, we propose a new algorithm for discovering semantic mappings across hierarchical classifications based on a new approach to semantic coordination. This approach shifts the problem of semantic coordination from the problem of computing linguistic or structural similarities (what most other proposed approaches do) to the problem of deducing relations between sets of logical formulae that represent the meaning of concepts belonging to different models. We show how to apply the approach and the algorithm to an interesting family of semantic models, namely hierarchical classifications, and present the results of preliminary tests on two types of hierarchical classifications, web directories and catalogs. Finally, we argue why this is a significant improvement on previous approaches.

A Large Scale Taxonomy Mapping Evaluation

“…It produced slightly more than 5% of mappings. This result is interesting since on the previously evaluated datasets (see [4] for example) the similar baseline algorithm performed quite well and found up to 70% of mappings. This lead us to conclusion that the dataset is not trivial (i.e., it is essentially hard for simple matching techniques).…”

S-Match: an Algorithm and an Implementation of Semantic Matching

“…We concentrate on semantic matching, as introduced in [6], based on the ideas and system described in [2]. The key intuition behind semantic matching is that we should calculate mappings by computing the semantic relations holding between the concepts (and not labels!)…”

“…The current version of S-Match is a rationalized re-implementation of the CTXmatch system [2] with a few added functionalities. S-Match is schema-based, and, as such, it does not exploit the information encoded in documents.…”

Efficient Semantic Matching

“…Semantic matching, as introduced in [1,5], is based on the key intuition that labels at nodes, which are written in natural language, are translated into propositional formulas which codify the intended meaning of the labels themselves. This allows us to codify the matching problem into a propositional unsatisfiability problem, which can then be efficiently implemented using state of the art propositional satisfiability (SAT) solvers [8,9].…”