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Triple Trustworthiness Measurement for Knowledge Graph
TLDR
A knowledge graph triple trustworthiness measurement model that quantify their semantic correctness and the true degree of the facts expressed and achieved significant and consistent improvements compared with other models. Expand
SDT: An integrated model for open-world knowledge graph reasoning
TLDR
A novel model is proposed, SDT, that incorporates the tructural information, entity escriptions, and hierarchical information of entities into a unified framework to learn more representative embeddings for KGs and evaluates the model on both open-world and closed-world reasoning tasks. Expand
TTMF: A Triple Trustworthiness Measurement Frame for Knowledge Graphs
TLDR
A unified knowledge graph triple trustworthiness measurement framework to calculate the confidence values for the triples that quantify its semantic correctness and the true degree of the facts expressed is established. Expand
Incorporating Uncertain Segmentation Information into Chinese NER for Social Media Text
TLDR
A model that specializes in identifying entities from Chinese social media text is proposed that alleviates the segmentation error cascading trouble effectively, and achieves a significant performance improvement of 2% over previous state-of-the-art methods. Expand
Reasoning over temporal knowledge graph with temporal consistency constraints
Knowledge graph reasoning or completion aims at inferring missing facts by reasoning about the information already present in the knowledge graph. In this work, we explore the problem of temporalExpand