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 [1].
Mining ℰℒ⊥ Bases with Adaptable Role Depth
Authors: Ricardo Guimarães, Ana Ozaki, Cosimo Persia, Baris Sertkaya
JAIR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We then present a new strategy for mining EL bases which is adaptable in the sense that it can bound the role depth of concepts depending on the local structure of the interpretation. Our strategy guarantees to capture all EL concept inclusions holding in the interpretation, not only the ones up to a fixed role depth. We also consider the case of confident EL bases, which requires that some proportion of the domain of the interpretation satisfies the base, instead of the whole domain. This case is useful to cope with noisy data. This work extends our conference paper (Guimar aes, Ozaki, Persia, & Sertkaya, 2021) with full proofs of the results presented there and with a section on compact representation of the product graph. |
| Researcher Affiliation | Academia | Ricardo Guimar aes EMAIL Ana Ozaki EMAIL Cosimo Persia EMAIL Department of Informatics, University of Bergen Barı s Sertkaya EMAIL Frankfurt University of Applied Sciences |
| Pseudocode | Yes | Algorithm 1: Computing MVF via Lemma 6 Input: A description graph G = (V, E, L) and a vertex v V Output: The MVF of v in G, i.e., mvf(G, v) |
| Open Source Code | No | The paper does not contain any explicit statements about open-source code availability, nor does it provide links to repositories or mention code in supplementary materials. |
| Open Datasets | Yes | Example 1. Consider the DBpedia knowledge graph (Lehmann, Isele, Jakob, Jentzsch, Kontokostas, Mendes, Hellmann, Morsey, van Kleef, Auer, & Bizer, 2015), where one can represent a city a , which is the capital of a region b , with the facts city(a), region(b), partof(a, b), and capital(b, a). |
| Dataset Splits | No | The paper is theoretical in nature, focusing on mining EL bases and their properties. While it mentions handling "noisy data" and refers to a "proportion of the domain," it does not describe any specific experimental setup involving data splits (e.g., training, test, validation sets) for empirical evaluation. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and mathematical proofs. It does not describe any computational experiments or evaluations that would require specific hardware specifications. |
| Software Dependencies | No | The paper presents algorithms and theoretical results, including pseudocode for computing MVF. However, it does not describe an implementation of these algorithms or list any specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks) that would be needed for replication. |
| Experiment Setup | No | The paper is theoretical and focuses on developing a new strategy for mining EL bases. It provides proofs and defines algorithms but does not describe any empirical experiments, and therefore, no specific experimental setup details like hyperparameter values, training configurations, or system-level settings are provided. |