Triple Trustworthiness Measurement for Knowledge Graph
  The Knowledge graph (KG) uses the triples to describe the facts in the real
world. It has been widely used in intelligent analysis and applications.
However, possible noises and conflicts are inevitably introduced in the process
of constructing. And the KG based tasks or applications assume that the
knowledge in the KG is completely correct and inevitably bring about potential
deviations. In this paper, we establish a knowledge graph triple
trustworthiness measurement model that quantify their semantic correctness and
the true degree of the facts expressed. The model is a crisscrossing neural
network structure. It synthesizes the internal semantic information in the
triples and the global inference information of the KG to achieve the
trustworthiness measurement and fusion in the three levels of entity level,
relationship level, and KG global level. We analyzed the validity of the model
output confidence values, and conducted experiments in the real-world dataset
FB15K (from Freebase) for the knowledge graph error detection task. The
experimental results showed that compared with other models, our model achieved
significant and consistent improvements.
