Tsinghua Science and Technology  2018, Vol. 23 Issue (06): 660-670    doi: 10.26599/TST.2018.9010011
A Latent Entity-Document Class Mixture of Experts Model for Cumulative Citation Recommendation
Lerong Ma, Lejian Liao, Dandan Song*, Jingang Wang
∙ Lerong Ma, Lejian Liao, Dandan Song, and Jingang Wang are with Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing Institute of Technology, Beijing 100081, China. E-mail: malerong@bit.edu.cn; Liaolj@bit.edu.cn;
∙ Lerong Ma is also with College of Mathematics and Computer Science, Yan’an University, Yan’an 716000, China. E-mail: malerong2008@163.com.
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Abstract

Knowledge Bases (KBs) are valuable resources of human knowledge which contribute to manyapplications. However, since they are manually maintained, there is a big lag between their contents and the up-to-date information of entities. Considering a target entity in KBs, this paper investigates how Cumulative Citation Recommendation (CCR) can be used to effectively detect its worthy-citation documents in large volumes of stream data. Most global relevant models only consider semantic and temporal features of entity-document instances, which does not sufficiently exploit prior knowledge underlying entity-document instances. To tackle this problem, we present a Mixture of Experts (ME) model by introducing a latent layer to capture relationships between the entity-document instances and their latent class information. An extensive set of experiments was conducted on TREC-KBA-2013 dataset. The results show that the model can significantly achieve a better performance gain compared to state-of-the-art models in CCR.

Received: 17 March 2017      Published: 28 December 2018
Corresponding Authors: Dandan Song