Stefano Zanobini

Semantic coordination: a new approach and an application

Semantic coordination, namely the problem of finding an agreement on the meaning of heterogeneous semantic models, is one of the key issues in the development of the Semantic Web. In this paper, we propose a new algorithm for discovering semantic mappings across hierarchical classifications based on a new approach to semantic coordination. This approach shifts the problem of semantic coordination from the problem of computing linguistic or structural similarities (what most other proposed approaches do) to the problem of deducing relations between sets of logical formulae that represent the meaning of concepts belonging to different models. We show how to apply the approach and the algorithm to an interesting family of semantic models, namely hierarchical classifications, and present the results of preliminary tests on two types of hierarchical classifications, web directories and catalogs. Finally, we argue why this is a significant improvement on previous approaches.

Peer-to-peer semantic coordination

Semantic coordination, namely the problem of finding an agreement on the meaning of heterogeneous schemas, is one of the key issues in the development of the Semantic Web. In this paper, we propose a method for discovering semantic mappings across hierarchical classifications (HCs) based on a new approach, which shifts the problem of semantic coordination from the problem of computing linguistic or structural similarities (what most other proposed approaches do) to the problem of deducing relations between sets of logical formulae that represent the meaning of concepts belonging to different schema. We show how to apply the approach and the algorithm to an interesting family of schemas, namely hierarchical classifications, and present the results of preliminary tests on two types of hierarchical classifications, web directories and catalogs. Finally, we argue why this is a significant improvement on previous approaches.

A SAT-based algorithm for context matching

The development of more and more complex distributed applications over large networks of computers has raised the problem of semantic interoperability across applications based on local and autonomous semantic schemas (e.g., concept hierarchies, taxonomies, ontologies). In this paper we propose to view each semantic schema as a context (in the sense defined in [1]), and propose an algorithm for automatically discovering relations across contexts (where relations are defined in the sense of [7]). The main feature of the algorithm is that the problem of finding relationships between contexts is encoded as a problem of logical satisfiability, and so the discovered mappings have a well-defined semantic. The algorithm we describe has been implemented as part of a peer-to-peer system for Distributed Knowledge Management, and tested on significant cases.

Bootstrapping semantics on the web: meaning elicitation from schemas

In most web sites, web-based applications (such as web portals, e-marketplaces, search engines), and in the file systems of personal computers, a wide variety of schemas (such as taxonomies, directory trees, thesauri, Entity-Relationship schemas, RDF Schemas) are published which (i) convey a clear meaning to humans (e.g. help in the navigation of large collections of documents), but (ii) convey only a small fraction (if any) of their meaning to machines, as their intended meaning is not formally/explicitly represented. In this paper we present a general methodology for automatically eliciting and representing the intended meaning of these structures, and for making this meaning available in domains like information integration and interoperability, web service discovery and composition, peer-to-peer knowledge management, and semantic browsers. We also present an implementation (called CtxMatch2) of how such a method can be used for semantic interoperability.

Soundness of schema matching methods

One of the key challenges in the development of open semantic-based systems is enabling the exchange of meaningful information across applications which may use autonomously developed schemata. One of the typical solutions for that problem is the definition of a mapping between pairs of schemas, namely a set of point–to–point relations between the elements of different schemas. A lot of (semi-)automatic methods for generating such mappings have been proposed. In this paper we provide a preliminary investigation on the notion of correctness for schema matching methods. In particular we define different notions of soundness, strictly depending on what dimension (syntactic, semantic, pragmatic) of the language the mappings are defined on. Finally, we discuss some preliminary conditions under which a two different notions of soundness (semantic and pragmatic) can be related.

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.

Matching hierarchical classifications with attributes

Hierarchical Classifications with Attributes are tree-like structures used for organizing/classifying data. Due to the exponential growth and distribution of information across the network, and to the fact that such information is usually clustered by means of this kind of structures, we assist nowadays to an increasing interest in finding techniques to define mappings among such structures. In this paper, we propose a new algorithm for discovering mappings across hierarchical classifications, which faces the matching problem as a problem of deducing relations between sets of logical terms representing the meaning of hierarchical classification nodes.

Coordinating Semantic Peers

The problem of finding an agreement on the meaning of heterogeneous schemas is one of the key issues in the development of the Semantic Web. In this paper, we propose a new algorithm for discovering semantic mappings across hierarchical classifications based on a new approach to semantic coordination. This approach shifts the problem of semantic coordination from the problem of computing linguistic or structural similarities (what most other proposed approaches do) to the problem of deducing relations between sets of logical formulas that represent the meaning of nodes belonging to different schemas. We show how to apply the approach and the algorithm to an interesting family of schemas, namely hierarchical classifications. Finally, we argue why this is a significant improvement on previous approaches.