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XcelLog: A Deductive Spreadsheet System
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| – Dr. C.R. Ramakrishnan, Dr. I.V. Ramakrishnan, Dr. David S. Warren |
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The promise of rule-based computing was to allow end users to create, modify, and maintain applications without the need to engage programmers. But experience has shown that rule sets often interact in subtle ways, making them difficult to understand and reason about. This has impeded the wide-spread adoption of rule-based computing. This paper describes the design and implementation of XcelLog, a user-centered deductive spreadsheet system, to empower non-programmers to specify and manipulate rule-based systems. The driving idea underlying the system is to treat sets as the fundamental data type and rules as specifying relationships among sets, and use the spreadsheet metaphor to create and view the materialized sets. The fundamental feature that makes XcelLog suitable for non-programmers is that the user mainly sees the effect of the rules; when rules or basic facts change, the user sees the impact of the change immediately. This enables the user to gain confidence in the rules and their modification, and also experiment with what-if scenarios without any programming. Preliminary experience with using XcelLog indicates that it is indeed feasible to put the power of deductive spreadsheets for doing rule-based computing into the hands of end users and do so without the requirement of programming or the constraints of canned application packages.
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Preference Logic Grammars: Fixed Point Semantics and Application to Data Standardization
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| – Dr. Terrance L. Swift |
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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 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 modeled directly using default negation, it is often easier to define preference rules than it is to add negation to the bodies of rules. 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 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.
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