期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 32, 期 4, 页码 617-630出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2019.2893328
关键词
Mixture models; Task analysis; Training data; Knowledge based systems; Correlation; Encyclopedias; Cumulative citation recommendation; knowledge base acceleration; hybrid latent entity-document classes; mixture model
类别
资金
- National Key Research and Development Program of China [2016YFB1000902]
- National Natural Science Foundation of China [61751217, 61866038]
- Ph.D start project of the Yan'an University of China [YDBK2018-09]
- special research project of the Shaanxi Education Department of China [18JK0876]
This paper explores Cumulative Citation Recommendation (CCR) for Knowledge Base Acceleration (KBA). The CCR task aims to detect potential citations of a set of target entities with priorities from a volume of temporally-ordered stream corpus. Previous approaches for CCR that build an individual relevance model for each entity fail to deal with unseen entities without annotation. A compromised solution is to build a global entity-unspecific model for all entities without respect to the relationship information among entities, which cannot guarantee achieving a satisfactory result for each entity. Moreover, most previous methods can not adequately exploit prior knowledge embedded in entities or documents due to considering all kinds of features indifferently. In this paper, we propose a novel entity and document class-dependent discriminative mixture model by introducing one intermediate layer to model the correlation between entity-document pairs and hybrid latent entity-document classes. The model can better adjust to different types of entities and documents, and achieve better performance when dealing with a broad range of entity and document classes. An extensive set of experiments has been conducted on two offical datasets, and the experimental results demonstrate that the proposed model can achieve the state-of-the-art performance.
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