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Knowledge base completion method based on WCUR algorithm

A knowledge base and completion technology, applied in the field of knowledge base completion based on WCUR algorithm, can solve the problems of incomplete knowledge base, ignoring rich semantic information, inaccuracy, etc., and achieve the effect of efficiently completing knowledge base

Pending Publication Date: 2020-04-17
FUZHOU UNIV
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Problems solved by technology

[0003] There are many imprecise and incomplete knowledge bases in the real world due to the lack of human intervention, and deterministic reasoning research does not perform well on such knowledge bases.
In addition, existing models for uncertainty reasoning mainly model isolated triples, ignoring the rich semantic information contained in interconnected nodes.

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  • Knowledge base completion method based on WCUR algorithm
  • Knowledge base completion method based on WCUR algorithm
  • Knowledge base completion method based on WCUR algorithm

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Embodiment Construction

[0050] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0051] Please refer to figure 1 , the present invention provides a kind of knowledge base completion method based on WCUR algorithm, comprises the following steps:

[0052] Step S1: traverse the entire knowledge base to obtain context information of entities and relationships;

[0053]Step S2: Combining the context information of entities and relations in the previous stage on the basis of considering individual triples, calculate the three parts of triple confidence P(h|Neighbor(h)), P(t|Neighbor (t)) and P(r|Path(r));

[0054] Step S3: optimize the objective function by the gradient descent algorithm, reversely update the vector of each entity and relationship, and obtain the optimal representation;

[0055] Step S4: Obtain new knowledge according to the optimal representation, and add it to the original knowledge base to realize knowledge base com...

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Abstract

The invention relates to a knowledge base completion method based on a WCUR algorithm, and the method comprises the following steps: S1, traversing a whole knowledge base, and obtaining the context information of an entity and a relation; S2, on the basis of considering a single triple, combining the context information of the entity and the relationship of the previous stage, and respectively calculating three parts P (h | Networbar (h), P (t | Networbar (t)) and P (r | Path (r)) of the confidence of the triple; S3, optimizing the target function through a gradient descent algorithm, and reversely updating the vector of each entity and relationship to obtain optimal representation; and S4, obtaining new knowledge according to the optimal representation, and adding the new knowledge to theoriginal knowledge base to realize knowledge base completion. The confidence coefficient of each triad can be measured in combination with a probability embedding model, better low-dimensional vectorrepresentation is learned for each entity and relationship, and the knowledge base is complemented more efficiently.

Description

technical field [0001] The invention relates to the field of knowledge representation and reasoning under knowledge graphs, in particular to a knowledge base completion method based on the WCUR algorithm. Background technique [0002] At present, the knowledge representation and reasoning model divides these methods into two categories based on the certainty of knowledge and whether external information is introduced: one is knowledge reasoning based on knowledge representation, which is further divided into deterministic knowledge-based methods according to whether the knowledge is certain or not. knowledge reasoning based on representation and knowledge reasoning based on uncertain knowledge representation; the second is knowledge representation reasoning that introduces external information. Knowledge representation reasoning methods based on deterministic knowledge include: TransE method, TransH method, TransR method and so on. The knowledge representation reasoning met...

Claims

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Application Information

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IPC IPC(8): G06N5/02
CPCG06N5/022G06N5/027
Inventor 汪璟玢林静
Owner FUZHOU UNIV
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