Name
Abstract | ||
|---|---|---|
We build a taxonomy of entities which is intended to improve the relevance of search engine in a vertical domain. The taxonomy construction process starts from the seed entities and mines the web for new entities associated with them. To form these new entities, machine learning of syntactic parse trees (their generalization) is applied to the search results for existing entities to form commonalities between them. These commonality expressions then form parameters of existing entities, and are turned into new entities at the next learning iteration. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.ins.2015.09.008 | Information Sciences |
Keywords | Field | DocType |
Learning taxonomy,Web mining,Learning constituency parse tree,Search relevance | Horizontal and vertical,Corporate taxonomy,Web mining,Search engine,Expression (mathematics),Computer science,Artificial intelligence,Natural language processing,Parsing,Hybrid system,Syntax,Machine learning | Journal |
Volume | Issue | ISSN |
329 | C | 0020-0255 |
Citations | PageRank | References |
1 | 0.35 | 33 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boris Galitsky | 1 | 248 | 37.81 |