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The guest users interested in the HMatch software can contact us by email (hm [__at__] islab [__dot__] di [__dot__] unimi [__dot__] it).
A new version of HMAtch is available
DownloadsThe version 2.0 of HMatch is available for download. HMatch 2.0 provides three concept matching algorithms, each addressing a different notion of similarity: linguistic, contextual and structural. It is also available a specific algorithm for instance matching.

Furthermore HMatch 2.0 condenses all the operations concerning mappings in a separate component that provides extraction, filtering and storage functionalities and a set of operations for combining mapping collections obtained with different matching algorithms.

This version is distributed as a standalone application with a command line user interface.
About HMatch
HMatch is a tool for dynamically matching distributed ontologies at different levels of depth. In particular, four different matching models are defined to span from surface to intensive matching, with the goal of providing a wide spectrum of metrics suited for dealing with many different matching scenarios that can be encountered in comparing concept descriptions of real ontologies.

HMatch takes two ontologies as input and returns the mappings that identify corresponding concepts in the two ontologies, namely the concepts with the same or the closest intended meaning. HMatch mappings are established after an analysis of the similarity of the concepts in the compared ontologies. In HMatch we perform similarity analysis through affinity metrics to determine a measure of semantic affinity in the range [0, 1]. A threshold-based mechanism is enforced to set the minimum level of semantic affinity required to consider two concepts as matching concepts.

Given two concepts, HMatch calculates a semantic affinity value as the linear combination of a linguistic affinity value and a contextual affinity value. For the linguistic affinity evaluation, HMatch relies on a thesaurus of terms and terminological relationships automatically extracted from the WordNet lexical system (Miller, 1995). The contextual affinity function of HMatch provides a measure of similarity by taking into account the contextual features of the ontology concepts. The context of a concept can include properties, semantic relations with other concepts, and property values. The context can be differently composed to consider different levels of semantic complexity, and four matching models, namely, surface, shallow, deep, and intensive, are defined to this end.

In the surface matching, only the linguistic affinity between the concept names is considered to determine concept similarity. In the shallow, deep, and intensive matching, also contextual affinity is taken into account to determine concept similarity. In particular, the shallow matching computes the contextual affinity by considering the context as composed only by concept properties. Deep and intensive matching extend the depth of concept context for the contextual affinity evaluation, by considering also semantic relations with other concepts (deep matching model) as well as property values (intensive matching model), respectively.

HMatch has been developed in the framework of the Helios project, conceived for supporting knowledge sharing and ontology-addressable content retrieval in peer-based systems.


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