近十年信息检索领域的研究热点与演化趋势研究——基于SIGIR会议论文的分析  

Research Trends of Information Retrieval——Case Study of SIGIR Conference Papers

作　　者：李跃艳 王昊[1,2] 邓三鸿 王伟[1,2] Li Yueyan;Wang Hao;Deng Sanhong;Wang Wei(School of Information Management,Nanjing University,Nanjing 210023,China;Jiangsu Key Laboratory of Data Engineering&Knowledge Service,Nanjing 210023,China)

机构地区：[1]南京大学信息管理学院,南京210023 [2]江苏省数据工程与知识服务重点实验室,南京210023

出　　处：《数据分析与知识发现》2021年第4期13-24,共12页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金面上项目(项目编号:72074108);中央高校基本科研业务费专项资金资助项目(项目编号:010814370113)的研究成果之一。

摘　　要：【目的】实时准确地了解信息检索领域的研究热点和演化趋势,为本领域的研究人员提供参考和帮助,对于加速与交叉学科的融合,促进信息检索技术的快速应用具有至关重要的作用。【方法】以SIGIR年会2008-2019年的录用论文作为数据源。首先,采用LDA模型识别并生成主题;其次,根据文献与主题的相似度过滤边缘文献,并通过计算文献主题区分度进行文档多主题划分;接着,通过构建领域主题在时间序列上的演化路径,展示主题的上升、下降及稳定三种演化方式;最后,通过模块化社团结构发现,构建单一主题的细粒度演化路径,充分展示主题群落内部知识单元间的动态演化过程。【结果】本文方法避免了边缘文献对领域主题识别和演化路径造成的干扰,文献多主题划分有助于揭示主题之间的交叉融合。研究发现,目前信息检索领域主要以用户为中心,检索模型不断优化,注重过滤和推荐,注重语义网技术,深度学习方法得到广泛应用,医疗健康等应用领域逐渐成为信息检索领域重点关注的内容。【局限】设置阈值过滤边缘文献并进行文献多主题划分,具有一定的主观性。【结论】智能化与信息化将逐渐成为一种常态,用户对信息检索的需求更加凸显。[Objective] This paper summarizes the research development trends of information retrieval, aiming to promote interdisciplinary studies and application of related technologies. [Methods] First, we used LDA model to identify topics of papers accepted by the SIGIR Annual Conference from 2008 to 2019. Second, we removed irrelevant papers based on the similarity between documents and topics, and grouped papers into multiple categories by calculating topic discrimination. Third, we constructed the evolution path of domain topics in time series which showed the increasing, decreasing and stable patterns. Finally, we created the fine-grained evolution path of a single topic through the modular community, which demonstrated the dynamic evolution process of knowledge units within the topics. [Results] The proposed method avoids the interference of irrelevant documents on identifying topics and evolution paths. The multi-topic classification of documents helps reveal the cross-fusion among topics. The current information retrieval research trends include user-centric, continuously optimized models, filtering and recommending, semantic web technology, deep learning methods, as well as medical and health information retrieval. [Limitations] It might be subjective to remove irrelevant documents and categorize documents with multi-topics. [Conclusions] Intelligent information services is becoming a new norm, and users’ needs for information retrieval becomes more prominent.

关 键 词：信息检索 LDA 社会网络分析 主题演变 

分 类 号：G250[文化科学—图书馆学]