Name
Abstract | ||
|---|---|---|
The large amounts of linked data are a valuable resource for the development of semantic applications. However, these applications often meet the challenges posed by flawed or incomplete schema, which would lead to the loss of meaningful facts. Association rule mining has been applied to learn many types of axioms. In this paper, we first use a statistical approach based on the association rule mining to enrich OWL ontologies. Then we propose some improvements according to this approach. Finally, we describe the quality of the acquired axioms by evaluations on DBpedia datasets. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-981-10-3168-7_12 | Communications in Computer and Information Science |
Keywords | DocType | Volume |
Linked data,RDF,OWL2,Association rule mining | Conference | 650 |
ISSN | Citations | PageRank |
1865-0929 | 0 | 0.34 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanyuan Li | 1 | 0 | 0.34 |
| Huiying Li | 2 | 30 | 5.32 |
| Jing Shi | 3 | 0 | 0.34 |