6th International Conference on Logic Programming

Reverse engineering complex biological systems requires the integration of multiple different databases using detailed background knowledge. Logic programming can provide a means of both performing integrative queries and rule-based inference to account for implicit knowledge. The Biological Logic Programming toolkit (Blipkit) was developed as a means of doing this kind of data integration. Implemented in SWI-Prolog, Blipkit has models of different aspects of life sciences data, including genes and gene sequences, RNA structures, evolutionary relationships, phenotypes and biological interactions. These can be combined to answer complex questions spanning multiple data sources. Blipkit also has means of integrating with and combining life sciences databases and ontologies. Speaker: Marc Denecker Title: A Knowledge Base System Project for FO(.) 08:30-09:30 Wednesday, July 15th Abstract: We will discuss the development of a Knowledge Base (KB) System, for a rich KB language, equipped with several forms of inference able to solve different sorts of tasks using the same KB. The logic FO(.) used in our KBS project is an extension of classical logic with various language primitives such as inductive definitions, aggregates, arithmetic, etc. It is also a natural integration (and further extension) of classical logic and logic programming, based on the view of a logic program as a definition. We discuss informal and formal semantics of definitions in FO(.) and briefly consider the relationship with other knowledge principles such as coinduction, the closed world assumption and causality and with the LP formalisms ASP, ALP and deductive databases. On the computational level, we will report on current attempts to build finite domain inference systems for model expansion, approximate reasoning, theory debugging and model revision, with special focus on the IDP-system, a model expansion system for FO(.) competitive with the best ASP-solvers. We will discuss the development of a Knowledge Base (KB) System, for a rich KB language, equipped with several forms of inference able to solve different sorts of tasks using the same KB. The logic FO(.) used in our KBS project is an extension of classical logic with various language primitives such as inductive definitions, aggregates, arithmetic, etc. It is also a natural integration (and further extension) of classical logic and logic programming, based on the view of a logic program as a definition. We discuss informal and formal semantics of definitions in FO(.) and briefly consider the relationship with other knowledge principles such as coinduction, the closed world assumption and causality and with the LP formalisms ASP, ALP and deductive databases. On the computational level, we will report on current attempts to build finite domain inference systems for model expansion, approximate reasoning, theory debugging and model revision, with special focus on the IDP-system, a model expansion system for FO(.) competitive with the best ASP-solvers.