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Expressivity of Datalog Variants - Completing the Picture

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 1230-1235.

Abstract

Computational and model-theoretic properties of logical languages constitute a central field of research in logic-based knowledge representation. Datalog is a very popular formalism, a de-facto standard for expressing and querying knowledge. Diverse results exist regarding the expressivity of Datalog and its extension by input negation (semipositive Datalog) and/or a linear order (order-invariant Datalog). When classifying the expressivity of logical formalisms by their model-theoretic properties, a very natural and prominent such property is preservation under homomorphisms. This paper solves the remaining open questions needed to arrive at a complete picture regarding the interrelationships between the class of homomorphism-closed queries and the query classes related to the four versions of Datalog. Most notably, we exhibit a query that is both homomorphism-closed and computable in polynomial time but cannot be expressed in order-invariant Datalog.

Sebastian Rudolph, and Michael Thomazo. 2016. Expressivity of Datalog Variants - Completing the Picture. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 1230–1235. https://ijcai.org/proceedings/2016/178
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