Paper Info
Title
TTMF: A Triple Trustworthiness Measurement Frame for Knowledge Graphs.
Abstract
The Knowledge graph (KG) uses the triples to describe the facts in the real world. It has been widely used in intelligent analysis and understanding of big data. In constructing a KG, especially in the process of automation building, some noises and errors are inevitably introduced or much knowledges is missed. However, learning tasks based on the KG and its underlying applications both assume that the knowledge in the KG is completely correct and inevitably bring about potential errors. Therefore, in this paper, we establish 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. It can be used not only to detect and eliminate errors in the KG but also to identify new triples to improve the KG. The framework 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 conducted experiments on the common dataset FB15K (from Freebase) and analyzed the validity of the modelu0027s output confidence values. We also tested the framework in the knowledge graph error detection or completion tasks. The experimental results showed that compared with other models, our model achieved significant and consistent improvements on the above tasks, further confirming the capabilities of our model.
Year
Venue
Field
2018
arXiv: Artificial Intelligence
Knowledge graph,Monad (category theory),Trustworthiness,Computer science,Theoretical computer science
DocType
Volume
Citations 
Journal
abs/1809.09414
1
PageRank 
References 
Authors
0.35
0
4
Name
Order
Citations
PageRank
Shengbin Jia132.45
yang xiang2323.09
Xiaojun Chen3146.41
Shijia E452.12