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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Trust-Sensitive Evolution of DL-Lite Knowledge Bases
Authors: Dmitriy Zheleznyakov, Evgeny Kharlamov, Ian Horrocks
AAAI 2017 | Venue PDF | 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. |