A Tensor Space Model based Semantic Search Technique
| Autor: | Han-Joon Kim, Jae-Young Chang, Jonghoon Chun, Kee-Joo Hong |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry Semantic search Cognition 02 engineering and technology Ontology (information science) computer.software_genre Term (time) Task (project management) Metadata 020204 information systems Tensor (intrinsic definition) 0202 electrical engineering electronic engineering information engineering Vector space model 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing |
| Zdroj: | The Journal of Society for e-Business Studies. 21:1-14 |
| ISSN: | 2288-3908 |
| Popis: | Semantic search is known as a series of activities and techniques to improve the search accuracy by clearly understanding users’ search intent without big cognitive efforts. Usually, semantic search engines requires ontology and semantic metadata to analyze user queries. However, building a particular ontology and semantic metadata intended for large amounts of data is a very time-consuming and costly task. This is why commercialization practices of semantic search are insufficient. In order to resolve this problem, we propose a novel semantic search method which takes advantage of our previous semantic tensor space model. Since each term is represented as the 2nd-order ‘document-by-concept’ tensor (i.e., matrix), and each concept as the 2nd-order ‘document-by-term’ tensor in the model, our proposed semantic search method does not require to build ontology. Nevertheless, through extensive experiments using the OHSUMED document collection and SCOPUS journal abstract data, we show that our proposed method outperforms the vector space model-based search method. |
| Databáze: | OpenAIRE |
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