TY - JOUR
T1 - Preference Logic Grammars
T2 - Fixed point semantics and application to data standardization
AU - Cui, Baoqiu
AU - Swift, Terrance
N1 - Funding Information:
The authors owe David S. Warren a debt of thanks for help in rewriting the Prolog standardizer to use tabling and preferences. The authors would like to thank Bharat Jayaraman and Kannan Govindarajan for their comments on a prelimiary version of this paper. This work was partially supported by NSF grants CCR-9702581, EIA-97-5998, and INT-96-00598.
PY - 2002/6
Y1 - 2002/6
N2 - 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 A 1 is preferred to that of an atom A 2, a preference rule can be defined so that A 2 is derived only if A 1 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.
AB - 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 A 1 is preferred to that of an atom A 2, a preference rule can be defined so that A 2 is derived only if A 1 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.
KW - Logic Programming
KW - Natural language processing
KW - Non-monotonic reasoning
KW - Reasoning with preferences
KW - XSB
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U2 - 10.1016/S0004-3702(02)00185-6
DO - 10.1016/S0004-3702(02)00185-6
M3 - Article
AN - SCOPUS:0345537162
SN - 0004-3702
VL - 138
SP - 117
EP - 147
JO - Artificial Intelligence
JF - Artificial Intelligence
IS - 1-2
ER -