aiqbench analysis
This paper introduces a refined semantics for description logic programs that resolves computational complexity issues while preserving well-supportedness properties. By maintaining NP-complete consistency checks instead of elevating them to the polynomial hierarchy's second level, it offers more efficient reasoning capabilities for combining rules with ontologies. The work provides significant theoretical advances in formal knowledge representation and automated theorem proving within artificial intelligence.
A New Well-Supported Semantics for Description Logic Programs
arXiv:2607.21203v1 Announce Type: new Abstract: Description logic programs are a powerful formalism for combining rules with ontologies. The well-supported semantics for description logic programs ensures that no answer sets rely on cyclic dependencies. Most popular semantics for logic programming have this property of well-supportedness. We recognize two limitations of the current well-supported semantics for DL programs: its increased computational complexity for the consistency problem and its lack of a reduct transformation characterization. In this work, we present a new semantics which evaluates ontological atoms more strictly than the current semantics. This keeps the complexity of its consistency problem NP-complete, rather than increasing it to the second level of the polynomial hierarchy. Additionally, we identify a syntactic class of description logic programs for which our new semantics is equivalent to the current semantics. We characterize our semantics using a fixpoint operator and a reduct-based transformation. Our new semantics is a strict subset of the current well-supported semantics, so it maintains the prior notion of well-supportedness while inducing its own stricter notion. We prefer our new notion of well-supportedness due to its similarities with logic programming.
Abstract:Description logic programs are a powerful formalism for combining rules with ontologies. The well-supported semantics for description logic programs ensures that no answer sets rely on cyclic dependencies. Most popular semantics for logic programming have this property of well-supportedness. We recognize two limitations of the current well-supported semantics for DL programs: its increased computational complexity for the consistency problem and its lack of a reduct transformation characterization. In this work, we present a new semantics which evaluates ontological atoms more strictly than the current semantics. This keeps the complexity of its consistency problem NP-complete, rather than increasing it to the second level of the polynomial hierarchy. Additionally, we identify a syntactic class of description logic programs for which our new semantics is equivalent to the current semantics. We characterize our semantics using a fixpoint operator and a reduct-based transformation. Our new semantics is a strict subset of the current well-supported semantics, so it maintains the prior notion of well-supportedness while inducing its own stricter notion. We prefer our new notion of well-supportedness due to its similarities with logic programming.
In Proceedings ICLP 2026, arXiv:2607.17707
Artificial Intelligence (cs.AI)
arXiv:2607.21203 [cs.AI]
(or arXiv:2607.21203v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.21203 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
EPTCS 450, 2026, pp. 403-415
https://doi.org/10.4204/EPTCS.450.29 Focus to learn more DOI(s) linking to related resources
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 293 words
Related coverage

Presentation: Architecting the Data Layer for AI Agents: from Transactional Systems to MCP and Semantic Models
InfoQ · Aug 29, 2026
Hybrid MKNF with Classical Negation in the Rule Component
arXiv · Jul 24, 2026

Presentation: Beyond Prompting: Context Engineering for Production-Grade AI
InfoQ · Sep 2, 2026

Rewrite All the Code, All the Time
LessWrong · Aug 4, 2026