Building on previous work on Weighted Description Logic (WDL), we present and assess an algorithm for concept combination grounded in the experimental research in cognitive psychology. Starting from two WDL formulas representing concepts in a way similar to Prototype Theory and a knowledge base (KB) modelling background knowledge, the algorithm outputs a new WDL formula which represent the combination of the input concepts. First, we study the logical properties of the operator defined by our algorithm. Second, we collect data on the prototypical representation of concepts and their combinations and learn WDL formulas from them. Third, we evaluate our algorithm and the role of the KB by comparing the algorithm’s outputs with the learned WDL formulas.

Concept Combination in Weighted DL

Galliani P.;
2023-01-01

Abstract

Building on previous work on Weighted Description Logic (WDL), we present and assess an algorithm for concept combination grounded in the experimental research in cognitive psychology. Starting from two WDL formulas representing concepts in a way similar to Prototype Theory and a knowledge base (KB) modelling background knowledge, the algorithm outputs a new WDL formula which represent the combination of the input concepts. First, we study the logical properties of the operator defined by our algorithm. Second, we collect data on the prototypical representation of concepts and their combinations and learn WDL formulas from them. Third, we evaluate our algorithm and the role of the KB by comparing the algorithm’s outputs with the learned WDL formulas.
2023
AA.VV.
18th European Conference on Logics in Artificial Intelligence, JELIA 2023
9783031436185
18th European Conference on Logics in Artificial Intelligence, JELIA 2023
Dresden
20 September 2023 through 22 September 2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2167732
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