Trust-Sensitive Evolution of DL-Lite Knowledge Bases
Authors: Dmitriy Zheleznyakov, Evgeny Kharlamov, Ian Horrocks
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that DL-Lite is not closed under a wide range of trust-sensitive MBAs. We introduce a notion of s-approximation that improves the previously proposed approximations and show how to compute it for various trust-sensitive MBAs. Finally, we develop polynomial time algorithms to compute maximal sound s-approximations for several trust-sensitive and classical evolution semantics. |
| Researcher Affiliation | Academia | Dmitriy Zheleznyakov, Evgeny Kharlamov, Ian Horrocks Department of Computer Science, University of Oxford, UK |
| Pseudocode | Yes | Algorithm 1: TT-SApprox; Algorithm 2: PT-Extend SAx; Algorithm 3: AT-Extend Ax |
| Open Source Code | No | The paper does not provide any concrete access information (link or explicit statement of availability) for its source code. |
| Open Datasets | No | This paper is theoretical and does not use or evaluate on empirical datasets. Therefore, no information about public dataset availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical data splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments or computations, as it is a theoretical work. |
| Software Dependencies | No | The paper describes algorithms and logical frameworks but does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or solvers) required for reproduction. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments with specific hyperparameter values or system-level training settings. |