Assessing the Exposure to Public Knowledge in Policy-Protected Description Logic Ontologies
Authors: Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati, Domenico Fabio Savo
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We analyze the computational complexity (specifically, data complexity) of these problems, focusing on the DL-Lite R and EL Description Logics. Our findings show that, for DL-Lite R with restricted forms of policy, both the problems can be efficiently solved through query rewriting methods. For EL , we establish conditions for tractable computational bounds. Our results highlight the potential of this framework for practical applications in confidentiality-preserving knowledge management. All our complexity results pertain to data complexity [Vardi, 1982], which in our context is the complexity computed with respect to the size of the ABox. |
| Researcher Affiliation | Academia | 1Sapienza University of Rome 2University of Bergamo {lastname}@diag.uniroma1.it, EMAIL |
| Pseudocode | Yes | Algorithm 1: Exists GAView input: A DL-Lite R or EL PPKB E = K, Kpub, P 1 if there exists A Cons GA(K) such that 2 Kpub A |= ED-Cons(Kpub A , E) 3 then return true; 4 return false |
| Open Source Code | No | The paper focuses on theoretical analysis and computational complexity. There is no explicit mention of code release, a link to a repository, or code provided in supplementary materials. |
| Open Datasets | No | The paper describes a theoretical framework and computational complexity analysis using abstract knowledge bases and policies. It does not perform experiments on specific datasets, therefore no concrete access information for open datasets is provided. Example 1 introduces a hypothetical 'KB of patients genetic data' for illustrative purposes only. |
| Dataset Splits | No | This paper presents theoretical results and complexity analysis. No empirical experiments using datasets were conducted, thus no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical, focusing on computational complexity analysis rather than empirical experiments. Therefore, it does not describe specific hardware used for running experiments. |
| Software Dependencies | No | The paper describes a theoretical framework and computational complexity. It does not mention specific software dependencies with version numbers for experimental replication. |
| Experiment Setup | No | The paper presents theoretical findings on computational complexity and does not include empirical experiments. Consequently, there are no specific experimental setup details, hyperparameters, or training configurations provided. |