Stable Model Semantics for Description Logic Terminologies
Authors: Federica Di Stefano, Mantas Šimkus
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies a stable model semantics for Description Logic (DL) knowledge bases (KBs) and for (possibly cyclic) terminologies, ultimately showing that terminologies under the proposed semantics can be equipped with effective reasoning algorithms. Specifically, we show that concept satisfiability in minimal models of an ALCIO KB is undecidable. We then turn our attention to (possibly cyclic) DL terminologies, where ontological axioms are limited to definitions of concept names in terms of complex concepts. We provide a strong undecidability result for reasoning in DLs in the presence of predicate minimization. We show a worst-case optimal complexity result for reasoning in DL terminologies under the proposed semantics. |
| Researcher Affiliation | Academia | Federica Di Stefano1, Mantas ˇSimkus1,2 1Institute of Logic and Computation, TU Wien, Austria 2Department of Computing Science, Ume a University, Sweden |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Figure 1 provides definitions of HT semantics but not an algorithm. |
| Open Source Code | No | The paper is theoretical and does not mention any source code release or provide links to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or evaluation. Therefore, it does not provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments with datasets, thus no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware for execution. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setups, hyperparameters, or training configurations. |