基于学术论文质量模型的检索排序算法研究

The Realization of an Article Quality-Based Search and Rank Algorithm

为提高论文检索的效率和质量,在原有检索技术的基础上,建立学术论文质量评价模型,提出1种基于此模型的检索排序算法。采用"元数据思想"实现将PDF全文转换成XML格式输出,在此基础上利用基于词条位置权重的向量空间技术检索XML文档进行内容匹配;然后利用论文质量评价模型进行排序;最后将检索排序后的论文推荐给作者,既可以提高作者论文检索的效率和质量,又能增加期刊被引的可能性。

Based on the traditional information retrieval technologies, this paper provides a search and rank algorithm by making an article quality model. First of all, the ＂Metadata Model＂ is applied to transform the PDF file into XML code. Secondly, to search articles in XML format, this paper uses a term weighting formula that takes into account the important information such as the position, length and frequency. Then, it applies the article quality model to rank the articles, which are the result of the second step. Finally, the result is provided to the author（s）. Therefore, It can improve the quality of the article and the probability of article citation.