Name
Abstract | ||
|---|---|---|
The ontology language OWL 2 EL, is designed for knowledge modeling and has been widely used in real applications. However, modeling knowledge as OWL ontologies is an error-prone process, where logical errors or contradictions would be imported. Further, it is almost impossible to manually find errors occurring in large-scale ontologies. Finding justification, an important service for error pinpointing, has then attracted much attention of researchers and developers. However, current methods of finding justifications suffer from an issue: high-coupling of reasoners, i.e., there is a tight relation between reasoners and the task of finding justifications. This makes the performance of finding justifications be highly influenced by the utilized reasoner, and, it is also restricted to optimize the algorithms effectively. In order to tackle this problem, we consider giving a method such that the task of finding justifications is independent from the utilized reasoner. Specifically, we propose a kind of graph called Explanation Dependency Graph (EDG) which guides to compute justifications from the reasoning results directly. We further give several optimizing strategies and prove the correctness of our method. We implement our method and evaluate it on real ontologies, including SNOMED CT. The experimental results show that our method is practical and performs better than current methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3233/IDA-173583 | INTELLIGENT DATA ANALYSIS |
Keywords | Field | DocType |
OWL 2 EL, classification, justification, graph | Computer science,Theoretical computer science,Artificial intelligence,Dependency graph,Machine learning,Web Ontology Language | Journal |
Volume | Issue | ISSN |
22 | 6 | 1088-467X |
Citations | PageRank | References |
0 | 0.34 | 21 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhangquan Zhou | 1 | 19 | 3.40 |
| Guilin Qi | 2 | 961 | 88.58 |