Contraction and Revision over DL-Lite TBoxes
Authors: Zhiqiang Zhuang, Zhe Wang, Kewen Wang, Guilin Qi
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The key to our approach is the introduction of an alternative semantics called type semantics which is more succinct than DL semantics. More importantly, with a finite signature, type semantics always yields finite humber of models. We then define model-based contraction and revision for DL-Lite TBoxes under type semantics and provide representation theorems for them. Finally, the succinctness of type semantics allows us to develop tractable algorithms for both operations. |
| Researcher Affiliation | Academia | Zhiqiang Zhuang1 Zhe Wang1 Kewen Wang1 Guilin Qi2,3 1 School of Information and Communication Technology, Griffith University, Australia 2 School of Computer Science and Engineering, Southeast University, China 3 State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China |
| Pseudocode | Yes | Algorithm 1: CONT; Algorithm 2: REVI |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on logical formalisms and algorithms for DL-Lite TBoxes, not empirical training on datasets. Therefore, no information about public dataset availability for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, so there are no details regarding validation splits. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and describes algorithms and logical formalisms, but does not specify any software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and describes algorithms and logical frameworks, not empirical experiments that would require details about hyperparameters or system-level training settings. |