Name
Abstract | ||
|---|---|---|
DOME (Deep Ontology MatchEr) is a scalable matcher which
relies on large texts describing the ontological concepts. Using the doc2vec
approach, these texts are used to train a fixed-length vector representation
of the concepts. Mappings are generated if two concepts are close to
each other in the resulting vector space. If no large texts are available,
DOME falls back to a string based matching technique. Due to its high
scalability, it can also produce results in the largebio track of OAEI and
can be applied to very large ontologies. The results look promising if
huge texts are available, but there is still a lot of room for improvement. |
Year | Venue | Field |
|---|---|---|
2018 | OM@ISWC | Ontology (information science),Ontology,Vector space,Information retrieval,Computer science,Dome,Scalability |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sven Hertling | 1 | 61 | 12.33 |
| Heiko Paulheim | 2 | 1095 | 84.19 |