Name
Abstract | ||
|---|---|---|
This research proposes a novel method of measuring the dynamics of semantic relatedness. Research on semantic relatedness has a long history in the fields of computational linguistics, psychology, computer science, as well as information systems. Computing semantic relatedness has played a critical role in various situations, such as data integration and keyword recommendation. Many researchers have tried to propose more sophisticated techniques to measure semantic relatedness. However, little research has considered the change of semantic relatedness with the flow of time and occurrence of events. The authors' proposed method is validated by actual corpus data collected from a particular context over a specific period of time. They test the feasibility of our proposed method by constructing semantic networks by using the corpus collected during a different period of time. The experiment results show that our method can detect and manage the changes in semantic relatedness between concepts. Based on the results, the authors discuss the need for a dynamic semantic relatedness paradigm. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.4018/JDM.2016040101 | J. Database Manag. |
Keywords | Field | DocType |
Computational Linguistics,Computer Science,Information Systems,Semantic Networks,Semantic Relatedness | Semantic similarity,Data integration,Data mining,Semantic integration,Computer science,Computational linguistics,Semantic network,Natural language processing,Semantic grid,Artificial intelligence,Semantic computing,Semantic compression | Journal |
Volume | Issue | ISSN |
27 | 2 | 1063-8016 |
Citations | PageRank | References |
0 | 0.34 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youngseok Choi | 1 | 9 | 1.18 |
| Jungsuk Oh | 2 | 26 | 2.78 |
| Jinsoo Park | 3 | 4469 | 267.37 |