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Tsinghua Science and Technology

Keywords

knowledge base acceleration, cumulative citation recommendation, Mixture of Experts (ME), Latent Entity-Document Classes (LEDCs)

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.

Publisher

Tsinghua University Press

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