Enhancing Controlled Query Evaluation through Epistemic Policies
Authors: Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati, Domenico Fabio Savo
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show the expressive abilities of our framework and study the data complexity of CQE for (unions of) conjunctive queries when ontologies are specified in the Description Logic DL-Lite R. Interestingly, while we show that the problem is in general intractable, we prove tractability for the case of acyclic epistemic dependencies by providing a suitable query rewriting algorithm. The latter result paves the way towards the implementation and practical application of this new approach to CQE. |
| Researcher Affiliation | Academia | 1Sapienza University of Rome 2University of Bergamo |
| Pseudocode | Yes | Algorithm 1: SC-Entails(E, q) input: DL-Lite R CQE instance E = T , A, P , BUCQ q and Algorithm 2: IC-Entails(E, q) input: DL-Lite R CQE instance E = T , A, P , BUCQ q |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology or a direct link to a code repository. |
| Open Datasets | No | The paper is theoretical in nature and does not conduct experiments on specific, publicly available datasets. It uses abstract ABoxes and TBoxes for theoretical definitions and examples rather than empirical data. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments, therefore, it does not provide dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running experiments or computations. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve an experimental setup with hyperparameters, optimizer settings, or system-level training configurations. |