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Knowledge graph embedding-based interpretable multi-hop question and answer method and system

A knowledge graph and explanatory technology, applied in the field of natural language processing, can solve problems such as lack of reasonability, limitation of accuracy, and inability to know the specific path, so as to achieve the effect of improving the effect and accuracy

Pending Publication Date: 2022-03-11
BEIJING INSTITUTE OF TECHNOLOGYGY +1
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Problems solved by technology

[0006] However, this type of method directly encodes sentences into the relational space, and cannot know the specific path of the graph that the system relies on when obtaining the answer to the question, lacking reasonability
In addition, due to the lack of effective use of the relational information in the knowledge graph, the accuracy is limited to a certain extent

Method used

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  • Knowledge graph embedding-based interpretable multi-hop question and answer method and system
  • Knowledge graph embedding-based interpretable multi-hop question and answer method and system

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Embodiment

[0059] Such as figure 1 As shown, an interpretable multi-hop question answering method based on knowledge graph embedding includes the following steps:

[0060] Step 1: Input the question sentence, and convert the encoded sentence to the relational space of the knowledge graph.

[0061] details as follows:

[0062] Step 1.1: Through the embedding layer, convert the context sentence into its sequence of word vector representations.

[0063] Specifically, the question sentence is segmented first, and a vocabulary is constructed. Among them, the Chinese word segmentation tool Jieba word segmentation (https: / / github.com / fxsjy / jieba) can be used to segment Chinese words. Since English has natural spaces, word segmentation is not required. Finally, through the embedding layer mapping, the sentence is converted into a sequence of word vector representations.

[0064] Step 1.2: Use the encoder to encode the word vector sequence of the question sentence, and output the coded repre...

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Abstract

The invention relates to a multi-hop knowledge graph question answering method and system based on knowledge graph embedding, and belongs to the technical field of natural language processing application. According to the method, firstly, a knowledge graph is embedded, and whether a triple exists or not is judged by calculating the score of a vector combination of the triple (a head entity, a relation and a tail entity); for a multi-hop problem, attention calculation is carried out by utilizing an attention mechanism and all potential relationships with a problem node, and a relationship code with a relatively high weight is fused into a code of the problem, so that the problem code is not generated by voucher, but is obtained by reasoning and calculating depending on a known relationship of a map. Meanwhile, on the basis that the known relation can be deduced, question nodes are inferred reversely through answers, the accuracy of the answers is verified, and the accuracy of the answers is further improved.

Description

technical field [0001] The invention relates to a knowledge base question answering method and system, in particular to a multi-hop knowledge map question answering method and system based on knowledge map embedding, and belongs to the technical field of natural language processing applications. Background technique [0002] Thanks to the development of deep learning, machine question answering technology based on knowledge graph has made great progress. [0003] The existing knowledge question answering method based on deep learning has a good effect on answering technical questions within a single hop of the knowledge graph. The current knowledge graph question answering model is usually based on the method of named entity recognition and relationship classification, and retrieves or distinguishes the tail entity in the knowledge graph according to the head entity and relationship. However, since the knowledge graph is usually incomplete, the question entity and the answe...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/332G06F16/36G06F40/151
CPCG06F16/3329G06F16/367G06F40/151
Inventor 鉴萍王海刘德生简平
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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