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- W2895828488 abstract "Knowledge Bases (KBs) are valuable resources of human knowledge which contribute to many applications. However, since they are manually maintained, there is a big lag between their contents and the upto- 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." @default.
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- W2895828488 date "2018-12-01" @default.
- W2895828488 modified "2023-09-24" @default.
- W2895828488 title "A latent entity-document class mixture of experts model for cumulative citation recommendation" @default.
- W2895828488 doi "https://doi.org/10.26599/tst.2018.9010011" @default.
- W2895828488 hasPublicationYear "2018" @default.
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