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Knowledge graph intelligent question-answer method fusing pointer generation network

A technology of knowledge graph and intelligent question answering, which is applied in the field of question answering based on knowledge graph, can solve the problem of low knowledge storage and achieve the effect of saving time

Pending Publication Date: 2021-06-22
DALIAN NATIONALITIES UNIVERSITY
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  • Summary
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  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to meet the above-mentioned needs in the prior art, the present invention provides a knowledge graph intelligent question answering method that integrates the pointer generation network. Storage can also solve the problem of low knowledge storage in a single text, improve the accuracy of question and answer; and can be presented to users in the form of natural language, improving user experience

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  • Knowledge graph intelligent question-answer method fusing pointer generation network
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  • Knowledge graph intelligent question-answer method fusing pointer generation network

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Experimental program
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Embodiment 1

[0083] The overall implementation process of the present invention mainly includes three parts, namely, a knowledge vocabulary building module, a word vector acquisition module, and a generative model building module.

[0084] The construction flow chart of the present invention is as figure 1 As shown, each step will be described in detail below.

[0085] Step 1: Use the jieba word segmentation tool to segment the original text and questions in the original WebQA dataset, and remove punctuation marks and stop words, and then check the processed data. If there are words that are not correctly segmented, perform Manually segment words and add them to custom dictionaries;

[0086] Step 2: After preprocessing the data, train it as a word vector, and then use BiLSTM-CRF for named entity recognition;

[0087] Step 3: Then use the cypher statement to query all the triplet information of the entity in the Neo4j graph database;

[0088] Step 4: Query all the triples of the entity i...

Embodiment 2

[0092] Depend on figure 1 As shown, a knowledge graph intelligent question answering method fused with a pointer generation network is mainly constructed from four aspects.

[0093] Step 1: Perform named entity recognition on the dataset;

[0094] Step 2: Retrieve the entity in Neo4j and count the word frequency, and store the entity in the knowledge vocabulary;

[0095] Step 3: word vector acquisition;

[0096] Step 4: Construct a pointer combined with the knowledge map to generate a network model and return the answer;

[0097] Each step is described in detail below:

[0098] Step 1: Use the jieba word segmentation tool and the custom dictionary set in advance according to the data set to segment the data, remove stop words, etc., and then use the word embedding technology to use the original text and questions in the data set as the BiLSTM layer in the entity recognition model. enter. The optimal predicted sequence is then obtained using CRF. In the present invention,...

Embodiment 3

[0166] Step 1: Use the jieba word segmentation tool to segment and check the original text and question parts in the WebQA dataset (original text, question sentence, answer);

[0167] Step 2: Use the BiLSTM-CRF method for named entity recognition on the data after the correct word segmentation;

[0168] Step 3: Query the triplet corresponding to the identified entity in the Neo4j database;

[0169] Step 4: Count the frequency of occurrence of each word in the corresponding triplet, and store the words in the triplet in the knowledge vocabulary in order of word frequency;

[0170] Step 5: Use the deep learning method to obtain the word vector of the question sentence;

[0171] Step 6: Construct a generative model and return an answer.

[0172] Further, for step 1, use jieba word segmentation to segment the original text and questions in the data set, and remove stop words and punctuation marks.

[0173] Further, for step 2, the named entity recognition method is BiLSTM-CRF. ...

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Abstract

The invention discloses a knowledge graph intelligent question-answering method fusing a pointer generation network, and belongs to the field of artificial intelligence question-answering. According to the technical scheme, a word segmentation tool is used for carrying out word segmentation and checking on original text and question parts in a WebQA data set; performing named entity recognition on the data after correct word segmentation by using a BiLSTM-CRF model; querying a triple corresponding to the identified entity in a Neo4j database; counting the occurrence frequency of each word in the corresponding triad, and storing the queried words in the triad into a knowledge word list according to a word frequency sequence; using a deep learning method to obtain word vectors of the question sentences; and constructing a generative model, and returning an answer. The method has the beneficial effects that entity recognition is performed on texts by using a deep learning technology, knowledge is quickly queried by using a knowledge graph technology, and the problems that returned answers are stiff and single and the storage space in a knowledge base is incomplete are effectively solved in combination with a generative model; the time for obtaining the answer is saved, the intention of the user is more fully understood, and the answer more conforming to the reading mode of the user is returned.

Description

technical field [0001] The invention belongs to the field of question answering methods based on artificial intelligence, in particular to a question answering method based on knowledge graphs and a generative method based on pointer networks. Background technique [0002] Knowledge Graph is a method to represent facts in a structured form, which consists of entities, relationships and semantic descriptions. It is a huge semantic network that represents the relationship between entities, expressed in the form of triples (head entity, relationship, tail entity). At present, the knowledge map technology has received extensive attention from researchers and scholars, and the knowledge map is applied to semantic search, intelligent question answering, and personalized recommendation. In this way, the scattered knowledge can be systematized and delivered to users accurately and quickly. [0003] At present, the mainstream methods of knowledge graph question answering mainly inc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06F16/332G06F40/216G06F40/295G06F40/30G06K9/62G06N3/04G06N3/08
CPCG06F16/367G06F16/3329G06F40/295G06F40/216G06F40/30G06N3/08G06N3/048G06N3/044G06N3/045G06F18/2415
Inventor 刘爽谭楠楠孟佳娜于玉海赵丹丹
Owner DALIAN NATIONALITIES UNIVERSITY
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