Semantic Integration Workshop at the Second International Semantic Web Conference (ISWC-2003)

By Doan, AnHai; Halevy, Alon et al. | AI Magazine, Spring 2004 | Go to article overview

Semantic Integration Workshop at the Second International Semantic Web Conference (ISWC-2003)


Doan, AnHai, Halevy, Alon, Noy, Natalya F., AI Magazine


In numerous distributed environments, including today's World Wide Web, enterprise data management systems, large science projects, and the emerging semantic web, applications will inevitably use the information described by multiple ontologies and schemas. Interoperability among applications depends critically on the ability to map between them. Semantic integration issues have now become a key bottleneck in the deployment of a wide variety of information management applications. The high cost of this bottleneck has motivated numerous research activities on methods for describing mappings, manipulating them, and generating them semiautomatically. This research has spanned several communities (databases, AI, World Wide Web), but unfortunately, there has been little cross-fertilization between the communities considering the problem.

To bring these communities together, we organized the Workshop on Semantic Integration at the Second International Semantic Web Conference on Sanibel Island, Florida. In addition to presenting the state of the art of semantic integration research, we wanted to start a discussion on what semantic integration really is, what different communities bring to the table, and how to develop a common research agenda and outline the next big challenges. Hence, the emphasis on the day of the workshop was on discussion rather than formal presentations.

The workshop generated a lot of interest: There were more than 70 registered participants, twice as many as for any other workshop at the conference. We received more than 40 research papers and demonstration proposals for review. The workshop proceedings contain 19 research papers and 7 demonstration descriptions of semantic integration systems that passed a rigorous peer review of the international program committee. (1) Many workshop participants submitted position statements, which also appear in the proceedings. We invite the reader to browse the workshop proceedings for the content of these papers and statements. In this article, we focus on presentations and discussions that are not part of the proceedings.

The format of the workshop reflected our goal of fostering discussion and active exchange of ideas. We had two excellent invited talks by Philip Bernstein (Microsoft Research) and Eduard Hovy (University of Southern California Information Sciences Institute [USC/ISI]). (2) There were three panel discussions: (1) one on controversial topics in semantic integration, (2) one on automated techniques for mapping definition and discovery, and (3) one on future research directions. There was a lively poster and demonstration session and, despite a large number of participants, active discussion throughout the day.

Invited Talks

The workshop opened with a talk by Philip Bernstein (Microsoft) on a general framework for model management. The goal of model management is to provide a set of high-level operators for manipulating models of data, rather than the data itself. A model is a representation of any structure, such as relational database schema, XML schema, and ontology. In the model-management framework, both models and the mappings between them are first-class objects. Models and mappings are manipulated by operators such as Match, Merge, Diff, Compose, and Extract. Bernstein discussed possible semantics of these operators and specific implementation of the operators and their combinations.

Although Bernstein set out an infrastructure on which integration projects can be built, Eduard Hovy (USC/ISI) reported on results from many practical integration projects that his group has performed. Hovy argued that it is paramount to experiment with different matching techniques, using different heuristics, sources, and combinations of techniques to understand what works and what does not. He also argued that at the current point in ontology research, what's important is building content of ontologies, doing it bit by bit, and figuring out what works and what doesn't rather than concentrating on different formalisms and formal methods. …

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