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| Research on Optimization of Scientific Literature Similarity Calculation Based on the Co-citation Feature |
| Han Qing, Zhou Xiaoying |
| School of Information Resources Management, Renmin University of China, Beijing 100872 |
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Abstract Calculating similarity for scientific literature is the basis of applications such as literature search and literature analysis, and the results have a direct impact on the final effectiveness of the related applications. The co-citation information is an important feature that is different from that of ordinary text. It can effectively represent the correlation between two text inputs. Further, it can be used to improve the validity and reliability of literature similarity calculation. Based on the vector space model, semantic features and co-citation features are introduced into the literature similarity calculation, and a hybrid model is proposed to optimize the similarity calculation of scientific literature. Through the verification of seven research fields, such as university library, online public opinion, and information quality, the results show that the proposed model can make full use of the co-citation features of scientific literature, and thus compensate for the problem of insufficient features in the vector space model and improve the overall performance of scientific literature similarity calculation.
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Received: 07 April 2018
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