Aligning Controlled vocabularies for enabling semantic matching in a distributed knowledge management system

The underlying idea of the Semantic Web is that web content should be expressed not only in natural language but also in a language that can be unambiguously understood, interpreted and used by software agents, thus permitting them to find, share and integrate information more easily. The central notion of the Semantic Web's syntax are ontologies, shared vocabularies providing taxonomies of concepts, objects and relationships between them, which describe particular domains of knowledge. A vocabulary stores words, synonyms, word sense definitions (i.e. glosses), relations between word senses and concepts; such a vocabulary is generally referred to as the Controlled Vocabulary (CV) if choice or selection of terms are done by domain specialists. A facet is a distinct and dimensional feature of a concept or a term that allows a taxonomy, ontology or CV to be viewed or ordered in multiple ways, rather than in a single way. The facet is clearly defined, mutually exclusive, and composed of collectively exhaustive properties or characteristics of a domain. For example, a collection of rice might be represented using a name facet, place facet etc. This thesis presents a methodology for producing mappings between Controlled Vocabularies, based on a technique called \Hidden Semantic Matching". The \Hidden" word stands for it not relying on any sort of externally provided background knowledge. The sole exploited knowledge comes from the \semantic context" of the same CVs which are being matched. We build a facet for each concept of these CVs, considering more general concepts (broader terms), less general concepts (narrow terms) or related concepts (related terms).Together these form a concept facet (CF) which is then used to boost the matching process

Towards Explaining Semantic Matching

Interoperability among systems using different term vocabularies requires some mapping between terms in the vocabularies. Matching applications generate such mappings. When the matching process utilizes term meaning (instead of simply relying on syntax), we refer to the process as semantic matching. If users are to use the results of matching applications, they need information about the mappings. They need access to the sources that were used to determine relations between terms and potentially they need to understand any deductions performed on the information. In this paper, we present our approach to explaining semantic matching. Our initial work uses a satisfiability-based approach to determine subsumption and semantic matches and uses the Inference Web and its OWL encoding of the proof markup language to explain the mappings. The Inference Web solution also includes a registration of the OWL reasoning component of JTP, as well as other reasoner registrations, and thus provides a foundation for explaining semantic matching systems