A New Subject-Based Document Retrieval from Digital Libraries Using Vector Space Model

A New Subject-Based Document Retrieval from Digital Libraries Using Vector Space Model
Federated Conference on Computer Science and Information Systems ‪(FedCSIS)‬
Poznan, Poland
تاریخ کنفرانس: 
يكشنبه - ۱۸ شهريور ۱۳۹۷ تا چهارشنبه - ۲۱ شهريور ۱۳۹۷
چکیده

Document retrieval from digital libraries based on user's query is highly affected by the terms appeared in the query.‎ In many cases, there are some documents in the digital libraries that do not share exactly the same terms with the query, but they are related to the user's need.‎ We address this challenge in this paper by introducing a new subject-based retrieval approach in which, apart from ranking documents based on the terms in the query, a new subject-based scoring scheme is defined between the query and a document.‎ We define this score by introducing a new vector space model in which a vectorized subject-based representation is defined for each document and its keywords, and the terms in the query, as well.‎ We have tested the new subject-based scoring scheme on a database of scientific papers obtained from Web of Science.‎ Our Experimental results show that in 83% of times users prefer the proposed scoring scheme with respect to the classic scoring ones.‎

استناد: 

Bakhshayesh, Sayed Mahmood, Azadeh Mohebi, Abbas Ahmadi, and Amir Badamchi.‎ 2018.‎ A New Subject-Based Document Retrieval from Digital Libraries Using Vector Space Model. Article Presented at Federated Conference on Computer Science and Information Systems (FedCSIS), Poznan.‎

 

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