Citation Context Based Analysis Technologies On Scientific Literature Retrieval

Currently, the time of Big Data is coming, so more and more scientific literature is shown as electronic documents in the Internet, which not only promotes the popularization of literature, but also accelerates the development of scientific research level, as well as achieves the goal of "standing on the shoulder of giants". However, along with these changes, the problem that the good and the bad literature are intermingled in the large amounts of electronic academic documents is becoming more conspicuous. Therefore, we are faced with the new challenges in literature visualization, retrieval, management and application, which have become a hotspot in the research of Bibliometrics and knowledge management.This thesis will put the focus on the related methodologies of scientific literature retrieval based on the theory of citation analysis, text mining and information retrieval. So, some methods will be considered in the following part, i.e., Topic Model, Ranking Algorithm, Language Model, and Graph Theory. First of all, a method of domin knowledge visulation is presented. And then, there is a ranking algorithm of scientific literature by analyzing the semantic knowledge of citation context. Finally, a scientific literature retrieval model was implemented. All of these methods have improved by the experiment. So, the main research content includes:1. Put forward a new computing method for the citation probability distribution distance based on citation analysis, and then apply it into the visualization of literature knowledge domain.2. Extract the text information of citation context, and use the topic model of Labeled-LDA to generate two prior probabilities (vertex weight, edge weight) in the directed and weighted citation network. So a Context-Based Ranking Algorithm (CBRA) was proposed that improving the traditional PageRank algorithm.3. Apply the CBRA into the experiment of author authority ranking analysis. For each topic, we can set up the author authority rankings, which will improve the literature rankings. So that the literature ranking is not only based on the network links, but also take consideration of the authority of author. 4. In accordance with the CRBA, this thesis will improve the traditional information retrieval model which is based on language model. And then, establish a topic based literature retrieval system by system development methods.5. Apply the CBRA into passage retrieval and set up the passage retrieval system based on topic, which can improve the accuracy and relevance of literature retrieval.