Benchmark construction and experimental evaluations for incoherent ontologies

Ji, Qiu; Li, Weizhuo; Zhou, Shiqi; Qi, Guilin; Li, Yuan Fang

doi:10.1016/j.knosys.2021.108090

Cited by 4 publications

(4 citation statements)

References 33 publications

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“…The data set comes from our previous work about benchmarking incoherent ontologies presented in [39]. Since the approach proposed in this paper considers the semantics of axioms, those ontologies with meaningless entity names are excluded from our experiments.…”

Section: Data Setmentioning

confidence: 99%

“…For instance, we choose the sub-ontology km1500-3500 instead of its complete version km1500. In this way, most of the existing incoherent ontologies (i.e., 13 incoherent ontologies) and 7 merged incoherent ontologies provided by [39] are chosen.…”

Section: Data Setmentioning

confidence: 99%

“…The ontology names that consist of three parts and are connected with two symbols of "-" indicate those ontologies constructed by merging two source ontologies and an alignment between them. Here, an alignment consists of a set of correspondences, and correspondence can be translated into an OWL axiom (see [39] for more details). Take ontology ALOD2Vecconfof-edas as an example.…”

Section: Data Setmentioning

confidence: 99%

“…Furthermore, the information about MIPS in a selected ontology can be seen in Table 3, where all MIPS are obtained from the work presented in [39]. It can be observed that ontology km1500-3500 has quite a lot of unsatisfiable concepts (i.e., 734), and most of the others have no more than 30 unsatisfiable concepts.…”

Section: Data Setmentioning

confidence: 99%

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An Embedding-Based Approach to Repairing OWL Ontologies

Yang

et al. 2022

Applied Sciences

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High-quality ontologies are critical to ontology-based applications, such as natural language understanding and information extraction, but logical conflicts naturally occur in the lifecycle of ontology development. To deal with such conflicts, conflict detection and ontology repair become two critical tasks, and we focus on repairing ontologies. Most existing approaches for ontology repair rely on the syntax of axioms or logical consequences but ignore the semantics of axioms. In this paper, we propose an embedding-based approach by considering sentence embeddings of axioms, which translates axioms into semantic vectors and provides facilities to compute semantic similarities among axioms. A threshold-based algorithm and a signature-based algorithm are designed to repair ontologies with the help of detected conflicts and axiom embeddings. In the experiments, our proposed algorithms are compared with existing ones over 20 real-life incoherent ontologies. The threshold-based algorithm with different distance metrics is further evaluated with 10 distinct thresholds and 3 pre-trained models. The experimental results show that the embedding-based algorithms could achieve promising performances.

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Section: Data Setmentioning

confidence: 99%

Section: Data Setmentioning

confidence: 99%

Section: Data Setmentioning

confidence: 99%

Section: Data Setmentioning

confidence: 99%

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