Placement of Preference Semantics in CONTO

Determine whether preference semantics for relaxable, prioritized constraints should be added to the instance-based Configuration Vocabulary (ConfigVoc) or the axiom-based Configuration DL ontology (ConfigDL), accounting for the different ways each representation supports consistency checking and explanation.

Background

CONTO provides two complementary OWL-based representations for product configuration. ConfigVoc represents constraints as reified data, while ConfigDL represents them as OWL axioms enforced through description-logic consistency checking. The proposed multi-stakeholder extension introduces soft constraints, priorities, and preference aggregates, but relaxable constraints do not map naturally onto OWL’s all-or-nothing axiom semantics.

The paper explicitly leaves unresolved whether the preference extension should be hosted in ConfigVoc, where formula constraints can be exported to a solver and optimized, or in ConfigDL, where structural reasoning is native but relaxable axioms lack a direct representation. This choice affects how preferences are checked and how inconsistency justifications are produced in the ontology-mediated acquisition loop.

References

Whether the preference semantics are better added to the instance-based ConfigVoc (as additional data) or to the axiom-based ConfigDL (where relaxable constraints do not map cleanly onto OWL's all-or-nothing axiom semantics) is itself an open question we return to in Section~\ref{sec:discussion};

— Ontology-Mediated Neurosymbolic Constraint Acquisition from Multiple Stakeholders  (2609.29876 - Bischof et al., 24 Sep 2026) in Section 3, Approach; Section 6, paragraph “Limitations and open questions”

We describe an architecture rather than an evaluated system, and several questions remain open. The preference extension and the explanation feedback loop are proposed: the extension and the assistants are not yet built, and the loop has been exercised only on the OWL path. A first evaluation would target the acquisition loop rather than schedule quality: how often the static check catches a preference that could never be satisfied, how many renegotiation turns follow, and whether checking and solver encoding stay within interactive time. A first design question is where the preference semantics belong, the ConfigVoc-versus-ConfigDL choice detailed in Section~\ref{sec:loop}; because both paths supply inconsistency justifications, it governs how preferences are checked, not whether the loop can run. Modeling preferences as weighted soft constraints further raises the questions of how to set and compare priorities across stakeholder groups, and how to handle multiple competing soft constraints (multi-objective optimization); because the groups are self-interested, priority elicitation also carries a social-choice dimension, with the risk of strategic misreporting. The approach also assumes the LLM assistants translate faithfully between natural language and the ontology vocabulary; a mis-grounding that clashes with the hard limits is caught, but a consistent yet wrong one would propagate silently, making faithful grounding the make-or-break assumption.

— Ontology-Mediated Neurosymbolic Constraint Acquisition from Multiple Stakeholders  (2609.29876 - Bischof et al., 24 Sep 2026) in Section 6, paragraph “Limitations and open questions”