Mining EL Bases with Adaptable Role Depth
Authors: Ricardo Guimarães, Ana Ozaki, Cosimo Persia, Baris Sertkaya6367-6374
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We then present a new strategy for mining ELK 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 ELK concept inclusions holding in the interpretation, not only the ones up to a fixed role depth. In the next section, we present basic definitions and notation. In Section 3, we present the problem of mining ELK CIs and establish lower bounds for this problem. In Section 4, we present our main result for mining ELK bases with adaptable role depth. Our result uses a notion that relates each vertex in a graph to a set of vertices, called maximum vertices from (MVF). In Section 5, we show that the MVF of a vertex in a graph can be computed in linear time in the size of the graph. |
| Researcher Affiliation | Academia | Ricardo Guimarães,1 Ana Ozaki,1 Cosimo Persia,1 Baris Sertkaya2 1 Department of Informatics, University of Bergen, Norway 2 Frankfurt University of Applied Sciences, Germany |
| Pseudocode | Yes | Algorithm 1: Computing MVF via Lemma 6 Input: A description graph G p V, E, Lq and a vertex v P V Output: The MVF of v in G, i.e., mvfp G, vq 1 V Ð SCCp Gq 2 E Ð condensep G, V q 3 G Ð p V , E q 4 for V 1 P V do 5 wgtr V 1s Ð null 6 return max Weightp G , sccp G, vq, wgtq // Auxiliary function 7 Function max Weightp G , V 1, wgtq: |
| Open Source Code | No | The paper states, 'As future work, we plan to... implement our approach using knowledge graphs as datasets.' This indicates that the code is not yet available. |
| Open Datasets | No | The paper is theoretical and uses abstract 'finite interpretations' and 'description graphs' for its examples (e.g., 'Consider the interpretation I in Figure 4'). It does not use or provide access to publicly available datasets for empirical training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental setup involving training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. It mentions the lack of an efficient reasoner for ELK gfp but not its own use of specific software. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training settings. |