Name
Abstract | ||
|---|---|---|
In recent years, while Internet has brought various technological evolutions, users have been required to collect, select and integrate information according to a purpose. Based on this background, ontology that systemizes knowledge of the target world has been received a lot of attention. As a method of automatically constructing a super-sub relation which is a one of the important concept of ontology, there is a method of using a Lexico-syntactic pattern and a word dictionary. However, there are problems that cannot be classified correctly because it does not consider semantic relation of words so that cannot deal with words not existed in the dictionary. Therefore, a method to classify super-sub relation using a wedge product of word vectors is proposed to solve the problem. As a result, it has been confirmed that the effectiveness of the research to get higher precision/recall than that of the baseline method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICCC.2018.00027 | 2018 IEEE International Conference on Cognitive Computing (ICCC) |
Keywords | Field | DocType |
Ontological Classification, Word Embedding, Machine Learning, Outer Product, Word Feature Vector | Ontology (information science),Ontology,Computer science,Semantic relation,Natural language processing,Artificial intelligence,Word embedding,Recall,Principal component analysis,Semantics,The Internet | Conference |
ISBN | Citations | PageRank |
978-1-5386-7242-6 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazuki Omine | 1 | 0 | 0.34 |
| Incheon Paik | 2 | 241 | 38.80 |