Abstract
The mapping efficiency is key to the performance of dynamic ontology mapping in semantic web service discovery, context-awareness in smart spaces and so on. The existing methods simplify the current methods of similarity computation to promotes the efficiency, nevertheless they fail in the case that the number of candidate mapping entity pairs increases when ontology gets larger. An efficient ontology mapping method based on ontology partition is proposed, which divides an ontology into a set of blocks through bottom-up clustering. Then, the blocks are mapped and candidate mapping entity pairs are selected from the block mapping result. The experimental results show that the proposed method promotes the efficiency of mapping significantly with 6 times faster than it of Falcon-AO.
| Original language | English |
|---|---|
| Pages (from-to) | 243-248 |
| Number of pages | 6 |
| Journal | Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence |
| Volume | 24 |
| Issue number | 2 |
| State | Published - Apr 2011 |
Keywords
- Clustering
- Ontology mapping
- Ontology partition
- Vector space model