Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Contraction and Revision over DL-Lite TBoxes
Authors: Zhiqiang Zhuang, Zhe Wang, Kewen Wang, Guilin Qi
AAAI 2014 | Venue PDF | LLM Run Details | Input Tokens: 15,600 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,015 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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, Grifο¬th 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. |