Evonne is a web application primarily designed to explain Description Logic (DL) entailments using an interactive visualization approach for proofs. This paper introduces an extension of Evonne to DLs with concrete domains, which are needed for formalizing concepts whose definitions involve quantitative information. Specifically, we focus on two extensions of the DL \(\mathcal{E}\mathcal{L}_\bot \) : one with constraints formulated as linear equations and the other with difference constraints. First, we have extended Evonne to enable the generation and presentation of proofs involving these concrete domains. Then, leveraging the unique properties of each domain, we have designed and incorporated alternative visual explanations for the numerical parts of the proofs. Finally, we have assessed the effectiveness of these visual explanations through qualitative user studies and a performance benchmark. While opinions on one of these explanations varied, the other was widely recognized for its clarity and ease of understanding.

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The Concrete Evonne: Visualization Meets Concrete Domain Reasoning

  • Christian Alrabbaa,
  • Franz Baader,
  • Raimund Dachselt,
  • Alisa Kovtunova,
  • Julián Méndez

摘要

Evonne is a web application primarily designed to explain Description Logic (DL) entailments using an interactive visualization approach for proofs. This paper introduces an extension of Evonne to DLs with concrete domains, which are needed for formalizing concepts whose definitions involve quantitative information. Specifically, we focus on two extensions of the DL \(\mathcal{E}\mathcal{L}_\bot \) : one with constraints formulated as linear equations and the other with difference constraints. First, we have extended Evonne to enable the generation and presentation of proofs involving these concrete domains. Then, leveraging the unique properties of each domain, we have designed and incorporated alternative visual explanations for the numerical parts of the proofs. Finally, we have assessed the effectiveness of these visual explanations through qualitative user studies and a performance benchmark. While opinions on one of these explanations varied, the other was widely recognized for its clarity and ease of understanding.