Semantic matching is a type of ontology matching technique that relies on semantic information encoded in lightweight ontologies to identify nodes that are semantically related. For example, applied to file systems it can identify that a folder labeled “car” is semantically equivalent to another folder “automobile” because they are synonyms in English. This information can be taken from a linguistic resource like WordNet. Another example, shown below, compares extracts of two University course catalogs. This is a typical example of data integration where we need to match these course catalogs in the case of a transfer of a student from one University to another, where the receiving university has to decide which courses to recognize from the former University.
Semantic matching represents a fundamental technique in many applications in areas such as resource discovery, data integration, data migration, query translation, peer to peer networks, agent communication, schema and ontology merging. It has been proposed as a valid solution to the semantic heterogeneity problem, namely managing the diversity in knowledge. Interoperability among people of different cultures and languages, having different viewpoints and using different terminology has always been a huge problem. Especially with the advent of the Web and the consequential information explosion, the problem seems to be emphasized. People face the concrete problems to retrieve, disambiguate and integrate information coming from a wide variety of sources.
The S-MATCH Semantic Matcher Tool
S-Match is a semantic matching framework that provides several semantic matching algorithms and facilities for developing new ones. S-Match was heavily used inside KnowDive group for several years to conduct experiments in semantic matching. The goal of this project is to make S-Match available for the community by releasing it under a permissive open source license.
Language and Domain Aware Semantic Matching
Our ongoing research on semantic matching concerns multilingual and multi-domain approaches. Our cross-lingual ontology matcher is capable of computing alignments between ontologies in different languages without using an external machine translator service. This is made possible through the integration of our SCROLL multilingual label parser into the matcher. Domain-aware matching concerns the use of domain-specific background knowledge for the understanding of ontology labels. For domain-aware matching S-MATCH uses the Diversicon Framework.