Foundations of logic programming
Foundations of logic programming
Every logic program has a natural stratification and an iterated least fixed point model
PODS '89 Proceedings of the eighth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The well-founded semantics for general logic programs
Journal of the ACM (JACM)
Preferred answer sets for extended logic programs
Artificial Intelligence
Tabling for non-monotonic programming
Annals of Mathematics and Artificial Intelligence
Reasoning with Prioritized Defaults
LPKR '97 Selected papers from the Third International Workshop on Logic Programming and Knowledge Representation
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
Well-founded semantics for extended logic programs with dynamic preferences
Journal of Artificial Intelligence Research
Computer Languages
Logic programming and knowledge representation-the A-prolog perspective
Artificial Intelligence
A Compilation of Updates plus Preferences
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
A semantic framework for preference handling in answer set programming
Theory and Practice of Logic Programming
Mode-directed preferences for logic programs
Proceedings of the 2005 ACM symposium on Applied computing
Optimization with mode-directed preferences
PPDP '05 Proceedings of the 7th ACM SIGPLAN international conference on Principles and practice of declarative programming
Design patterns for tabled logic programming
INAP'09 Proceedings of the 18th international conference on Applications of declarative programming and knowledge management
Xsb: Extending prolog with tabled logic programming
Theory and Practice of Logic Programming - Prolog Systems
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The addition of preferences to normal logic programs is a convenient way to represent many aspects of default reasoning. If the derivation of an atom A1 is preferred to that of an atom A2, a preference rule can be defined so that A2 is derived only if A1 is not. Although such situations can be modelled directly using default negation, it is often easier to define preference rules than it is to add negation to the bodies of rules. As first noted by Govindarajan et al. [Proc. Internat. Conf. on Logic Programming, 1995, pp. 731-746], for certain grammars, it may be easier to disambiguate parses using preferences than by enforcing disambiguation in the grammar rules themselves. In this paper we define a general fixed-point semantics for preference logic programs based on an embedding into the well-founded semantics, and discuss its features and relation to previous preference logic semantics. We then study how preference logic grammars are used in data standardization, the commercially important process of extracting useful information from poorly structured textual data. This process includes correcting misspellings and truncations that occur in data, extraction of relevant information via parsing, and correcting inconsistencies in the extracted information. The declarativity of Prolog offers natural advantages for data standardization, and a commercial standardizer has been implemented using Prolog. However, we show that the use of preference logic grammars allow construction of a much more powerful and declarative commercial standardizer, and discuss in detail how the use of the non-monotonic construct of preferences leads to improved commercial software.