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Semantic Reranking of Scientific Papers According to its Research Area and Research

Because of the rapid growth of the number of electronic publications, locating the most relevant ones to the concrete search query (and research needs) is becoming more and more difficult. The research in this paper is based on the abstract model, representing scientific research as a tuple of research area and technologies, used for performing the research in this area. The assumption is that if two scientific papers have the same research area, and use similar research technologies, they present closely related research. As main reading goals of scientists are directed in some research area, or they investigate concrete research technology, the search queries usually are intended for finding papers in narrow research area, or using specific research methods. Every of the scientific paper structural parts (as abstract, title, introduction, etc.) contain some terms, belonging to the domains of these research parts (research area and used research technologies). In this paper we propose a vector representation of the scientific paper parts, consisting of two components: research area and research technology. These two components are numbers, represented frequency of usage of specialized terminology, related to corresponding area. We define concepts “research vector” and “relative research vector” for this purpose, analyze 90 scientific paper abstracts to determine statistically the most likely range of research vector components values, and propose scientific paper ranking algorithm, using them in reranking of scientific papers, returned from several digital libraries. We evaluate the importance of the presented paper model in query refinement stage by sending queries, classified in four groups to ACM digital library, and comparing the all returned results, and relevant ones among first 60.