Affiliation:
1. Engineering College, Xi’an International University, Xi’an 710077, China
Abstract
Background:
A research concern revolves around as to what can make the representation
of entities and relationships fully integrate the structural information of the knowledge atlas to
solve the entity modeling capability in complex relationships. World knowledge can be organized
into a structured knowledge network by mining entity and relationship information in real texts. In
order to apply the rich structured information in the knowledge map to downstream applications, it
is particularly important to express and learn the knowledge map. In the knowledge atlas with expanding
scale and more diversified knowledge sources, there are many types of relationships with
complex types. The frequency of a single relationship in all triples is further reduced, which increases
the difficulty of relational reasoning. Thus, this study aimed to improve the accuracy of
relational reasoning and entity reasoning in complex relational models.
Methods:
For the multi-relational knowledge map, CTransR based on the TransE model and
TransR model adopts the idea of piecewise linear regression to cluster the potential relationships
between head and tail entities, and establishes a vector representation for each cluster separately, so
that the same relationship represented by different clusters still has a certain degree of similarity.
Results:
The CTransR model carried out knowledge reasoning experiments in the open dataset, and
achieved good performance.
Conclusion:
The CTransR model is highly effective and progressive for complex relationships. In
this experiment, we have evaluated the model, including link prediction, triad classification, and
text relationship extraction. The results show that the CTransR model has achieved significant improvement.
Publisher
Bentham Science Publishers Ltd.
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