MOSIG Master 2ND
ADVISOR: Jérôme Euzenat and Cássia Trojahn dos Santos
TEL: 476 61 53 66 and 476 61 53 52 66
EMAIL: Jerome:Euzenat#inrialpes:fr and Cassia:Trojahn#inrialpes:fr
TEAM AND LAB: Exmo team, INRIA & LIG
MASTER PROFILE: Artificial intelligence and the web
Reference number: Proposal n°805
Matching systems fail to provide arguments/explanations for their results to users or programs using them. Sometimes matchings are no intuitive to human users and they need to be explained. It could be interesting, for instance, to provide explanations such as why a particular match is found, or why a certain match is ranked higher than another. Few works have been done in such direction (see ).
The main task is to specify a mechanism for explaining decisions made by matching systems. It includes the following activities:
Moreover, the contribution can be two fold. First, it will provide matching results explained, making easier their use and evaluation. Second, the notion of explanation can be aggregated into Argumentation Frameworks (AFs) (see , , and ), which specify different notions of acceptability. Argumentation models are based on the construction of arguments and counter arguments, followed by the selection of the most acceptable of them. Explanation can contribute in order to specify different notions of attack relation between arguments (for instance, making arguments stronger or weaker) as well as different notions of acceptability.
 R. Dhamankar, Y. Lee, A. Doan, A. Halevy, and P. Domingos. iMAP: Discovering complex semantic matches between database schemas. In Proceedings of SIGMOD, 2004.
 P. Dung. On the acceptability of arguments and its fundamental role in non monotonic reasoning, logic programming and n-person games. Artificial Intelligenceque 77(2):321-357, 1995.
 T. Bench Capon. Persuasion in practical argument using value-based argumentation frameworks. Journal of Logic and Computation 13(3):429-448, 2003.
 L. Amgoud and C. Cayrol. On the acceptability of arguments in preference -based argumentation. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pages 1-7, San Francisco, California, 1998. Morgan Kaufmann.