Efficient Paraconsistent Reasoning with Ontologies and Rules
Authors: Tobias Kaminski, Matthias Knorr, João Leite
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To this end, we define two paraconsistent semantics for hybrid KBs which, beyond their differentiating properties, are faithful to well-known paraconsistent semantics as well as the non-paraconsistent logic they extend, and tractable if reasoning in the DL component is. |
| Researcher Affiliation | Academia | Tobias Kaminski and Matthias Knorr and Jo ao Leite NOVA LINCS Departamento de Inform atica Universidade NOVA de Lisboa 2829-516 Caparica, Portugal |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found. |
| Open Source Code | No | In the future, our fixpoint computations can be used to adapt the Prot eg e plug-in No HR [Ivanov et al., 2013] to also consider reasoning with our paraconsistent semantics. |
| Open Datasets | No | The paper presents theoretical semantics and does not involve training on a dataset. Example 1 provides a simplified ground hybrid KB for illustration, not a publicly available dataset used for empirical training. |
| Dataset Splits | No | The paper focuses on theoretical definitions and properties of paraconsistent semantics; it does not describe experimental validation with dataset splits. |
| Hardware Specification | No | The paper describes theoretical work and does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers used for its own described work. It only mentions a Protege plug-in in the context of future work. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |