Author-conference topic-connection model for academic network search

Jianwen Wang, Xiaohua Hu, Xinhui Tu, Tingting He
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引用次数: 18

Abstract

This paper proposes a novel topic model, Author-Conference Topic-Connection (ACTC) Model for academic network search. The ACTC Model extends the author-conference-topic (ACT) model by adding subject of the conference and the latent mapping information between subjects and topics. It simultaneously models topical aspects of papers, authors and conferences with two latent topic layers: a subject layer corresponding to conference topic, and a topic layer corresponding to the word topic. Each author would be associated with a multinomial distribution over subjects of conference (eg., KM, DB, IR for CIKM 2012), the conference(CIKM 2012), and the topics are respectively generated from a sampled subject. Then the words are generated from the sampled topics. We conduct experiments on a data set with 8,523 authors, 22,487 papers and 1,243 conferences from the well-known Arnetminer website, and train the model with different number of subjects and topics. For a qualitative evaluation, we compare ACTC with three others models LDA, Author-Topic (AT) and ACT in academic search services. Experiments show that ACTC can effectively capture the semantic connection between different types of information in academic network and perform well in expert searching and conference searching.
学术网络搜索的作者-会议-主题连接模型
本文提出了一种新颖的学术网络搜索主题模型——作者-会议-主题连接模型。ACTC模型通过添加会议主题和主题与主题之间的潜在映射信息,扩展了作者-会议-主题(ACT)模型。它同时用两个潜在的主题层对论文、作者和会议的主题方面进行建模:一个与会议主题对应的主题层,一个与单词主题对应的主题层。每个作者将与会议主题的多项分布相关联。, KM, DB, IR (CIKM 2012),会议(CIKM 2012)和主题分别由采样主题生成。然后从采样的主题中生成单词。我们对来自知名网站Arnetminer的8,523位作者,22,487篇论文和1,243次会议的数据集进行实验,并使用不同数量的主题和主题来训练模型。为了进行定性评价,我们将ACTC与学术搜索服务中的其他三种模型LDA、作者-主题(AT)和ACT进行了比较。实验表明,ACTC可以有效地捕获学术网络中不同类型信息之间的语义联系,在专家搜索和会议搜索中表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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