Name
Abstract | ||
|---|---|---|
Ontology learning has become a popular research field recently. However, the typical ontology may not be sufficient to represent uncertainty information. Fuzzy ontology is proposed to solve the uncertainty reasoning problems. But the construction of fuzzy ontology is still a tedious and painstaking task. The cognitive model of fuzzy ontology learning is an automatic model of fuzzy ontology construction that simulates the process of human being recognizing the world. Induction is an important step of the model. In this paper, we present a strategy for the fuzzy ontology induction in the cognitive model of ontology learning which includes some generalization principles and the corresponding induction operators. It generates induction hypothesis through a series of operations. As a result, induction hypothesis are generated from the present ontology and it can generalize the existing ontology. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 16TH IEEE/ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS 2017) | ontology induction, cognitive model, fuzzy ontology, induction hypothesis |
Field | DocType | Citations |
Ontology (information science),Ontology alignment,Ontology-based data integration,Process ontology,Computer science,Ontology Inference Layer,Natural language processing,Artificial intelligence,Suggested Upper Merged Ontology,Upper ontology,Ontology learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dehai Zhang | 1 | 4 | 1.76 |
| Naiyao Wang | 2 | 1 | 0.69 |
| Ye Yuan | 3 | 38 | 4.92 |
| Bin Wang | 4 | 0 | 0.34 |
| Yang, Y. | 5 | 68 | 10.69 |