Notice: 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. Full accuracy metrics and methodology are described in [1].
Instance-Driven Ontology Evolution in DL-Lite
Authors: Zhe Wang, Kewen Wang, Zhiqiang Zhuang, Guilin Qi
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we introduce a model-theoretic approach to such a contraction problem by using an alternative semantic characterisation of DL-Lite TBoxes. We show that entailment checking (without necessarily first computing the contraction result) is in co NP, which does not shift the corresponding complexity in propositional logic, and the problem is tractable when the size of the new data is bounded. and We showed that the proposed operator possesses several desired properties and have also developed efficient algorithms for reasoning with and computing the contraction. |
| Researcher Affiliation | Academia | Zhe Wang and Kewen Wang and Zhiqiang Zhuang School of Information and Communication Technology, Griffith University, Australia Guilin Qi School of Computer Science and Engineering, Southeast University, China State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China |
| Pseudocode | Yes | Algorithm 1: Contraction Input: TBox T and ABox A in DL-Litecore Output: a DL-Litecore TBox T T := ; foreach B1, B2 B s.t. B1 = B2 do if T . T (A) |= B1 B2 then T := T {B1 B2} ; end if T . T (A) |= B1 B2 then T := T {B1 B2} ; end end return T ; |
| Open Source Code | No | The paper does not include any statement or link indicating that the source code for their methodology is openly available. |
| Open Datasets | No | The paper does not mention the use of any datasets for training or evaluation, nor does it provide information about dataset availability. |
| Dataset Splits | No | The paper does not describe any experimental setup involving training, validation, or test data splits. |
| Hardware Specification | No | The paper does not specify any hardware details (like GPU/CPU models, memory) used for running experiments or computations. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, such as programming languages, libraries, or solvers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details, such as hyperparameter values or training configurations, as it is a theoretical work. |