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Vector constraint embedded transformation knowledge graph inference method

A technology of knowledge graph and reasoning method, applied in the reasoning field of knowledge discovery, can solve problems such as ignoring the rationality of relational semantic types, achieve the effects of enriching and expanding knowledge graph, improving training speed, and training model accuracy

Inactive Publication Date: 2017-03-22
XIAMEN UNIV OF TECH
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Problems solved by technology

[0005] In the existing vector embedding conversion algorithm, when constructing the training model, the shortcomings of using the semantic type of the relationship between entities have not been considered, and the rationality of the semantic type of the relationship has been ignored

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  • Vector constraint embedded transformation knowledge graph inference method
  • Vector constraint embedded transformation knowledge graph inference method
  • Vector constraint embedded transformation knowledge graph inference method

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Embodiment Construction

[0032] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0033] Such as figure 2 As shown, the present invention provides a knowledge map reasoning method for vector constraint embedding conversion, including the following steps:

[0034] Step 1, get the semantic type of each relation and entity

[0035] In order to build a constraint model of the semantic type of the relationship between entities, it is first necessary to obtain the related semantic type of the entity relationship, including the triple set, entity set, and relationship set in the knowledge map, as well as the type of entity, the type of relationship, and the semantic type of the relationship. A collection of , which is used as the input of the inference algorithm. Semantic types of entities and relationships are provided in some existing large-scale knowledge graphs. For example, WordNet provides a brief and summary definitio...

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Abstract

The invention discloses a vector constraint embedded transformation knowledge graph inference method. The method comprises the steps of: step 1, acquiring a semantic type of each relation and each entity in a knowledge graph; step 2, embedding an entity set and a relation set into a low-dimensional continuous vector space, and normalizing the entity set and the relation set; step 3, mapping the normalized entity set and relation set into a corresponding vector matrix according to an original triple corresponding relation; step 4, calculating a scoring loss function value of each triple in the knowledge graph in the low-dimensional continuous space to construct a training model; step 5, optimizing the training model for the triples which satisfy a relation semantic type; step 6, repeating step 5 until a loop end condition is satisfied; and step 7, calculating the next triple, repeating steps 4 to 6 until all triples are calculated, and outputting the entity set and the relation set of the training model. The inference method can improve the inference accuracy of knowledge discovery and enhance the prediction precision.

Description

technical field [0001] The invention belongs to the fields of knowledge representation, knowledge discovery and artificial intelligence, and in particular relates to a reasoning method for knowledge discovery. Background technique [0002] In recent years, with the advent of the era of big data, a large number of knowledge graphs have been constructed, and the topics of research and application of knowledge graphs are also very rich, which has attracted widespread attention in academia and industry. [0003] The knowledge map is a structured semantic knowledge base, which is used to describe concepts and their relationships in the physical world in symbolic form; its basic unit is the "entity-relationship-entity" triplet, mainly using (head, relation, tail ) triplet description, head is the head entity, tail is the tail entity, relation is the relationship (abbreviated as h, r and t below), and the entity and its related attribute-value pairs. The entities are connected to e...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/2237G06F16/288
Inventor 林开标吴运兵朱顺痣杨帆卢萍
Owner XIAMEN UNIV OF TECH
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