By Mauro Di Manzo, Enrico Giunchiglia (auth.), Pietro Torasso (eds.)
This publication includes 22 lengthy papers and thirteen brief ones chosen for the medical music of the 3rd Congress of the Italian organization for man made Intelligence. The lengthy papers document accomplished paintings while the fast papers are often dedicated to ongoing study. The papers document major paintings performed within the varied subfields of man-made intelligence not just in Italy but in addition in different places: eight of the papers come from outdoor Italy, with 2 from the us and 1 eachfrom Australia, Austria, Germany, The Netherlands, Spain, and Turkey. The papers within the booklet are grouped into elements on: automatic reasoning; cognitive versions; connectionist types and subsymbolic ways; wisdom illustration and reasoning; languages, architectures and instruments for AI; computer studying; traditional language; making plans and robotics; and reasoning approximately actual platforms and artifacts.
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Additional info for Advances in Artificial Intelligence: Third Congress of the Italian Association for Artificial Intelligence, AI*IA '93 Torino, Italy, October 26–28, 1993 Proceedings
13. S. Melnik, H. Garcia-Molina and E. Rahm. Similarity Flooding: A Versatile Graph Matching Algorithm and its Application to Schema Matching. In ICDE’02. 14. R. Miller, L. Haas, and M. Hernandez. Schema Mapping as Query Discovery. In VLDB’01. 15. L. Popa, Y. Velegrakis, R. Miller, M. Hernandez, and R. Fagin. Translating Web Data. In VLDB’02. 16. J. Shanmugasundaram et al. Relational Database for Querying XML Documents: Limitations and Opportunities. In VLDB’99. pt 4 Inst. of Informatics, Brandenburg Univ.
Integrating our tool with schema matching tools which automatically generate schema and ontology element correspondences is an open problem to address in the future. We also plan to develop ﬁlters for mappings by making use of instance data to assist users in choosing the correct mapping among a list of possible candidates. 6 Probably some other relationship than rents(X, Y ) needs to be used. 20 Y. An, A. Borgida, and J. Mylopoulos Acknowledgments. We are grateful to anonymous reviewers for oﬀering valuable comments, corrections, and suggestions for improvement.
6 Refining Mappings by Ontology Reasoning Rich ontologies provide a new opportunity for eliminating “unreasonable” mappings. For example, if the ontology speciﬁes that once a Person owns a CellPhone, they do not rent another one, then a candidate semantic formula P erson(X), rents(X, Y ), Cell(Y ), owns(X, Z), Cell(Z) can be eliminated 6 , since no objects X can satisfy it. When ontologies, including constraints such as the one about renting/owning, are expressed in OWL, one can actually use OWL reasoning to detect inconsistent semantics by converting semantic trees into OWL concepts, and then testing them for incoherence with respect to the ontology.
Advances in Artificial Intelligence: Third Congress of the Italian Association for Artificial Intelligence, AI*IA '93 Torino, Italy, October 26–28, 1993 Proceedings by Mauro Di Manzo, Enrico Giunchiglia (auth.), Pietro Torasso (eds.)