Forgetting and Unfolding for Existential Rules
Authors: Zhe Wang, Kewen Wang, Xiaowang Zhang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we lay the foundation for a theory of forgetting for existential rules by developing a novel notion of unfolding. In particular, we introduce a definition of forgetting for existential rules in terms of query answering and provide a characterisation of forgetting by the unfolding. A result of forgetting may not be expressible in existential rules, and we then capture the expressibility of forgetting by a variant of boundedness. While the expressibility is undecidable in general, we identify a decidable fragment. Finally, we provide an algorithm for forgetting in this fragment. |
| Researcher Affiliation | Academia | Zhe Wang, Kewen Wang School of Information and Communication Technology Griffith University, Australia Xiaowang Zhang School of Computer Science and Technology Tianjin University, China |
| Pseudocode | Yes | Algorithm 1 Compute forgetting via GRA-guided unfolding |
| Open Source Code | No | The paper states 'Also, we hope to implement our algorithm and evaluate it over practical ontologies' in the future work section, indicating the code is not yet released. |
| Open Datasets | No | The paper is theoretical and does not use datasets for empirical evaluation. Therefore, it does not mention public dataset availability. |
| Dataset Splits | No | The paper is theoretical and does not report on empirical experiments with dataset splits. It provides no information on training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and proofs. It does not mention any specific hardware used for experiments, as no empirical experiments are described. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and proofs. It does not mention specific software dependencies with version numbers, as no empirical experiments requiring them are described. |
| Experiment Setup | No | The paper is theoretical and describes an algorithm and its properties. It does not include details about an experimental setup, such as hyperparameters or training configurations, as no empirical experiments are reported. |