Sonia Bergamaschi
Abstract
Taxonomic reasoning is a typical task performed by many AI knowledge representation systems. In this paper, the effectiveness of taxonomic reasoning techniques as an active support to knowledge acquisition and conceptual schema design is shown. The idea developed is that by extending conceptual models with defined concepts and giving them rigorous logic semantics, it is possible to infer isa relationships between concepts on the basis of their descriptions. From a theoretical point of view, this approach makes it possible to give a formal definition for consistency and minimality of a conceptual schema. From a pragmatic point of view it is possible to develop an active environment that allows automatic classification of a new concept in the right position of a given taxonomy, ensuring the consistency and minimality of a conceptual schema. A formalism that includes the data semantics of models giving prominence to type constructors (E/R, TAXIS, GALILEO) and algorithms for taxonomic inferences are presented: their soundness, completeness, and tractability properties are proved. Finally, an extended formalism and taxonomic inference algorithms for models giving prominence to attributes (FDM, IFO) are given.
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Index Terms
- On taxonomic reasoning in conceptual design
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Reviews
Jaroslav Pokorny
The formalization of conceptual schema design presented here is based on so-called taxonomic reasoning. The roots of this technique are in knowledge representation hybrid systems, particularly the well-known KL-ONE. Taxonomic reasoning is an automatic classification technique for the determination of the right place for a concept in a taxonomy. A database designer exploits the isa relationship as a basic modeling principle. A new concept description is compared to the given concepts' taxonomy, and the implicit isa links are discovered. Section 2 introduces a simple KL-ONE–like frame description language (FDL) and the notion of subsumption function. Constructs of the language remind one of the binary entity-relationship (ER) formalism. In Section 3, the authors extend the language, present its extensional semantics formally, and give a tractable subsumption algorithm. As concepts are described as a conjunction of ancestors (not necessarily parents) and local properties, a notion of minimality can be considered. With the help of the concept of contradiction, it is possible to define the consistency of a concept set. Section 4 offers a classification algorithm for the insertion of a new concept into the given concept set, preserving its consistency and minimality. In Section 5, the ER formalism is expressed by means of the tools developed earlier. In Section 6, the notion of an inverse role is added to the FDL, and the subsumption algorithm including this construct is presented. (No tractability considerations are discussed here.) In Section 7, the authors apply this FDL to the DAPLEX model. Finally, Section 8 examines related works in detail, and Section 9 sketches the application of subsumption computation to database queries and instances. The paper is well structured and easy to read. It not only contributes to conceptual schema theory but makes a connection between AI ideas and databases.
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