With the increasingly growth of scientific documents in the Web, it is difficult to select a concerned document. A citation recommendation system receives a text and recommends documents to be cited by the text. Such recommendation helps a researcher in hitting his/her concerned texts. Based on sematic relations, this paper presents a new indicator to measure the similarity between documents and presents a citation recommendation system exploiting the indicator along with other document features. The experimental results showed that the indicator succeeds in the document similarity recognition and leads to improvement in the recommendation.
Zarrinkalam, F., & Kahani, M. (2021). Using Semantic Relations to Improve Quality of a Citation Recommendation System. Soft Computing Journal, 1(2), 36-45.
MLA
Fattane Zarrinkalam; Mohsen Kahani. "Using Semantic Relations to Improve Quality of a Citation Recommendation System". Soft Computing Journal, 1, 2, 2021, 36-45.
HARVARD
Zarrinkalam, F., Kahani, M. (2021). 'Using Semantic Relations to Improve Quality of a Citation Recommendation System', Soft Computing Journal, 1(2), pp. 36-45.
VANCOUVER
Zarrinkalam, F., Kahani, M. Using Semantic Relations to Improve Quality of a Citation Recommendation System. Soft Computing Journal, 2021; 1(2): 36-45.
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