Repairing Ontologies via Axiom Weakening
Authors: Nicolas Troquard, Roberto Confalonieri, Pietro Galliani, Rafael Peñaloza, Daniele Porello, Oliver Kutz
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through an empirical analysis made over real-life ontologies, we show that our approach preserves significantly more of the original knowledge of the ontology than removing axioms. |
| Researcher Affiliation | Academia | Nicolas Troquard, Roberto Confalonieri, Pietro Galliani, Rafael Pe naloza, Daniele Porello, Oliver Kutz Faculty of Computer Science Free University of Bozen-Bolzano Piazza Domenicani, 3 I-39100 Bozen-Bolzano BZ, Italy |
| Pseudocode | Yes | Algorithm 1 Repair Ontology Weaken(O) ... Algorithm 2 Repair Ontology Remove(O) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It only links to an extended version of the paper on arXiv: 'An extended version of the paper is available at https://arxiv. org/abs/1711.03430.' |
| Open Datasets | Yes | To empirically test whether weakening axioms is a better approach to ontology repair than removing them, we tested our approach on ten ontologies from Bio Portal (Matentzoglu and Parsia 2017), expressed in ALC (see Table 2). ... Matentzoglu, N., and Parsia, B. 2017. Bio Portal snapshot 30.03.2017. last accessed, 2017/08/04. |
| Dataset Splits | No | The paper does not provide specific dataset split information (like train/validation/test percentages or counts) for machine learning model training. The 'procedure was repeated one hundred times per ontology, selecting the axioms to weaken or remove by sampling minimally inconsistent sets, and one further hundred times selecting the axioms to remove or weaken completely randomly.' This describes the experimental repetition and axiom selection strategy, not dataset splits for model training. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper refers to 'DL ALC' and 'DL EL' as logical frameworks and mentions 'OWL 2 EL profile' but does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | The procedure was repeated one hundred times per ontology, selecting the axioms to weaken or remove by sampling minimally inconsistent sets, and one further hundred times selecting the axioms to remove or weaken completely randomly. ... varying the number of minimally inconsistent sets sampled by Find Bad Axiom, which for these experiments was set to one tenth of the ontology size. |