Mardi 8 octobre 2024
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From Data Independence to Ontology Based Data Access (and back)
Abstract
Accessing information using a high-level data model or ontology has been a long-standing objective of research communities in several areas. In work based on knowledge representation in artificial intelligence (AI), this objective commonly falls under the heading of OBDA and of ontology mediated querying (OMQ), and has fostered the development of approaches using query rewriting or using variants of the so-called combined approach. However, the underlying idea of separating an ontological view of how information must be understood by users from a physical view of the layout of data in data structures---called data independence---has been the focus of work in the area of information systems for more than fifty years.
This tutorial explores how the original idea of data independence evolved and ultimately culminated in logic-based approaches to information management by systems that has enabled high-level ontological views of information entirely devoid of any low-level physical views of concrete data layout. An integral part of the tutorial is to explore the relationship between such high-level ontologies that users see and an understanding of the physical representation of such information in computer systems that is necessary to attain acceptable performance. The tutorial will address the latter by showing how ontologies derived by ontology design in AI can be used in a way that achieves an understanding of physical encoding of information sufficiently fine grained to ensure the performance of code ultimately executed to satisfy users' information requests can be competitive with solutions hand-written in low-level programming languages such as C.
Biography
Dr. David Toman is a professor of Computer Science at the University of Waterloo, Canada. He has published and presented results in the area of knowledge representation over the last 20 years at premier AI conferences. He received two Ray Reiter Prizes at KR 2010 and at KR 2016, the later for work related to the use of referring expressions in knowledge representation (jointly with Grant Weddell and Alex Borgida). This work was later extended to the area of conceptual modelling and was awarded the 2018 Bob Wielinga Best Paper Award for furthering the use of referring expressions in conceptual modelling. Dr. Toman has also given numerous tutorials in the area of temporal representation and reasoning and temporal databases (that has led to an invited chapter in the Handbook of Temporal Reasoning in Artificial Intelligence), on identification issues in knowledge representation systems, and on logic-based approaches to query compilation and optimization, all at premier AI conferences such as IJCAI, ECAI, and KR.
Date and place
Tuesday Oct 8, at 2:00 P.M
room 306
Organisé par
Sihem AMER-YAHIA
SLIDE Team Leader
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