Abstract. With new standards like RDF or OWL paving the way for the much anticipated Semantic Web, a new breed of very large scale semantic systems is about to appear. Traditional semantic reconciliation techniques, dependent upon shared vocabularies or global ontologies, cannot be used in such open and dynamic environments. Instead, new heuristics based on emerging properties and local consensuses have to be exploited in order to foster semantic interoperability in the large. In this paper, we outline the main differences between traditional semantic reconciliation methods and these new heuristics. Also, we characterize the resulting emergent semantics systems and provide a couple of hints vis-à-vis their potential applications. 1

Knowledge Sharing is a crucial issue in the Semantic Web: SW services expose and share knowledge content (expressed through ontologies and related knowledge bases) arising from distinct languages, locales, and personal perspectives; in this scenario, semantic alignment approaches play a pivotal role, providing viable solutions for integrating heterogeneous resources, still maintaining their local independence. We focus here on a 3-step approach to ontology mapping, which is strongly based on the exploitation of (monolingual and multilingual) linguistic resources for content publishing and discovery, and on a human intervention for supervising the process and assessing semantic links between mapped resources. Our methodology is also being supported by the development of dedicated tools for accompanying knowledge engineers and users across the different steps of creating and integrating ontology resources.

We introduce here a framework for adding Linguistic Expressivity to conceptual knowledge, as represented in ontologies. Both the multilingual aspects which characterize the (Semantic) Web and the demand for more easy-to-share forms of knowledge representation, being equally accessible by humans and machines, push in fact the need for a linguistically motivated approach to ontology development. Ontologies should thus express knowledge by associating formal content with explicative linguistic expressions, possibly in different languages. By adopting such an approach, the intended meaning of concepts and roles becomes more clearly expressed for humans, thus facilitating (among others) reuse of existing knowledge, while content mediation between autonomous agents gets far more chances than otherwise. 1.

Abstract. Both the multilingual aspects which characterize the (Semantic) Web and the demand for more easy-to-share forms of knowledge representation, being equally accessible by humans and machines, push the need for a more “linguistically aware ” approach to ontology development. Ontologies should thus express knowledge by associating formal content with explicative linguistic expressions, possibly in different languages. By adopting such an approach, the intended meaning of concepts and roles becomes more clearly expressed for humans, thus facilitating (among others) reuse of existing knowledge, while automatic content mediation between autonomous information sources gets far more chances than otherwise. In past work we introduced OntoLing [7], a Protégé plug-in offering a modular and scalable framework for performing manual annotation of ontological data with information from different, heterogeneous linguistic resources. We present now an improved version of OntoLing, which supports the user with automatic suggestions for enriching ontologies with linguistic content. Different specific linguistic enrichment problems are discussed and we show how they have been tackled considering both algorithmic aspects and profiling of user interaction inside the OntoLing framework. 1

Abstract. A document taxonomy alignment method, relying on document glosses and utilizing a soft ontology expansion, enables us to devise some allnew hierarchical leaf nodes for the purpose of better aligning a plurality of document taxonomies. 1.