[2] It works on lightweight ontologies,[3] namely graph structures where each node is labeled by a natural language sentence, for example in English.
These sentences are translated into a formal logical formula (according to an artificial unambiguous language) codifying the meaning of the node taking into account its position in the graph.
Such use of S-Match technology is prevalent in the career space where it is used to gauge depth of skills through relational mapping of information found in applicant resumes.
[4] In fact, it has been proposed as a valid solution to the semantic heterogeneity problem, namely managing the diversity in knowledge.
People face the concrete problem to retrieve, disambiguate and integrate information coming from a wide variety of sources.