Luciano Serafini

Contextualizing ontologies

Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a partys subjective view of a domain. In this paper, we show how ontologies can be contextualized, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation of contextual ontologies.

Mental States Recognition from Communication

In order to perform effective communication, agents must be able to foresee the effects of their utterances on the addressee’s mental state. In this paper we study the consequences of an utterance on the mental state of a hearer. Given an agent communication language with a STRIPS-like semantics, we propose a set of criteria that allow the binding of the speaker’s mental state to its uttering of a certain sentence. On the basis of these criteria, we give an abductive procedure that the hearer can adopt to partially r ecognize the speaker’s mental state that led to a specific utterance.

Semantic Coordination of Heterogeneous Classifications Schemas

A large amount of data, organized in heterogeneous schemas, is now available through the web. The problem of matching such schemas in order to allow global access to information has been recognized as one of the main challenges of the Semantic Web. In this paper, we propose a method for discovering mappings across schemas based on a new approach. This shifts the problem of schema matching from the process of computing linguistic or structural similarities between schema elements (what most other proposed approaches do) to the problem of deducing relations between sets of logical formulas representing the meaning of schema elements. We then show how to apply the approach to an interesting family of schemas, namely hierarchical classifications.