Name
Abstract | ||
|---|---|---|
Assessing concept similarity is an importance activity in ontology engineering. In this paper, a new similarity measure model is proposed, in which the information content of meet-irreducible elements is employed to evaluate the similarity degree of the two concepts of concept lattice. The proposed method combines featural and structural information into decision and preserves more structure information of concept lattice, which can be viewed as the development of Souza and Davis's model. Moreover, we give a method to find meet-irreducible elements of concept lattice by using attributes classes, rather than constructing Hasse Diagram or looking through all concepts of the context. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/FSKD.2010.5569473 | FSKD |
Keywords | Field | DocType |
souza and davis model,concept similarity evaluation,featural information,concept lattice,pattern matching,formal concept analysis,meet irreducible element,structural information,content management,ontologies (artificial intelligence),hasse diagram,structural information method,ontology engineering,concept similarity,lattices,information content,mathematical model,computational modeling,ontologies | Ontology (information science),Ontology engineering,Lattice (order),Similarity measure,Computer science,Hasse diagram,Theoretical computer science,Artificial intelligence,Content management,Pattern matching,Formal concept analysis,Machine learning | Conference |
Volume | ISBN | Citations |
4 | 978-1-4244-5931-5 | 2 |
PageRank | References | Authors |
0.41 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lidong Wang | 1 | 154 | 23.64 |
| Dianxuan Gong | 2 | 9 | 5.91 |