A Parameterized Complexity View on Description Logic Reasoning
Ronald de Haan

TL;DR
This paper advocates applying parameterized complexity theory to analyze the computational complexity of description logic reasoning problems, providing more nuanced insights than traditional classical complexity analysis.
Contribution
It introduces the use of parameterized complexity framework to description logic reasoning, supported by three case studies demonstrating its effectiveness.
Findings
Parameterized complexity offers detailed analysis of reasoning problems.
Case studies show benefits in understanding concept satisfiability.
Data complexity analysis is enhanced through parameterized methods.
Abstract
Description logics are knowledge representation languages that have been designed to strike a balance between expressivity and computational tractability. Many different description logics have been developed, and numerous computational problems for these logics have been studied for their computational complexity. However, essentially all complexity analyses of reasoning problems for description logics use the one-dimensional framework of classical complexity theory. The multi-dimensional framework of parameterized complexity theory is able to provide a much more detailed image of the complexity of reasoning problems. In this paper we argue that the framework of parameterized complexity has a lot to offer for the complexity analysis of description logic reasoning problems---when one takes a progressive and forward-looking view on parameterized complexity tools. We substantiate our…
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Taxonomy
TopicsSemantic Web and Ontologies · Logic, Reasoning, and Knowledge · Rough Sets and Fuzzy Logic
