Bounded Predicates in Description Logics with Counting
Authors: Sanja Lukumbuzya, Mantas Simkus
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We describe a procedure based on integer programming that allows us to decide the existence of upper bounds on the cardinality of some predicate in the models of a given ontology in a data-independent way. Our results yield a promising supporting tool for constructing higher quality ontologies, and provide a new way to push the decidability frontiers. |
| Researcher Affiliation | Academia | Sanja Lukumbuzya and Mantas ˇSimkus Institute of Logic and Computation, TU Wien, Austria lukumbuzya@kr.tuwien.ac.at, simkus@dbai.ac.at |
| Pseudocode | No | The paper describes its procedures mathematically and discursively, but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured steps in a code-like format. |
| Open Source Code | No | The paper is purely theoretical and does not mention releasing any source code for its methodology. It only provides a link to an extended version of the paper. |
| Open Datasets | No | The paper is theoretical and does not involve experimental evaluation on datasets. It uses illustrative examples but no real-world or benchmark datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation using datasets, thus no dataset splits for training, validation, or testing are specified. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical and logical contributions. It does not mention any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper is purely theoretical. It describes a procedure based on integer programming but does not specify any particular software, libraries, or solvers with version numbers that would be required to reproduce any computational aspects. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup. There are no hyperparameters, training configurations, or system-level settings discussed as would be found in experimental research. |