CN113688217B - Intelligent question and answer method oriented to search engine knowledge base - Google Patents

Intelligent question and answer method oriented to search engine knowledge base Download PDF

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CN113688217B
CN113688217B CN202110972592.3A CN202110972592A CN113688217B CN 113688217 B CN113688217 B CN 113688217B CN 202110972592 A CN202110972592 A CN 202110972592A CN 113688217 B CN113688217 B CN 113688217B
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knowledge base
path
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舒明雷
刘浩
周书旺
高天雷
许继勇
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Qilu University of Technology
Shandong Institute of Artificial Intelligence
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Shandong Institute of Artificial Intelligence
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3329—Natural language query formulation or dialogue systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
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    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
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    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models

Abstract

An intelligent question-answering method facing a search engine knowledge base is based on the search engine knowledge base, constructs a reasoning path with symbol Markov property through dynamic search path reasoning and deep reinforcement learning, realizes path information coding and calculation of action space probability distribution based on an LSTM and a feedforward neural network, and sets judgment conditions according to vector representation of the search reasoning path. Based on the judgment condition and the reasoning path when searching the answers of the questions, the intelligent question answering of the search engine based on the knowledge base is realized. The method does not need to define rules and limit the length of the reasoning path, can be used for a complex question-answer reasoning process based on a search engine knowledge base, and realizes efficient and accurate search engine intelligent question-answer.

Description

Intelligent question and answer method oriented to search engine knowledge base
Technical Field
The invention relates to the field of question answering only of search engines, in particular to an intelligent question answering method oriented to a search engine knowledge base.
Background
The intelligent question-answering of the search engine, namely a natural language question sentence of a given search engine, searches corresponding entities from the existing knowledge base to be used as answers of the question sentence. Specifically, entity recognition and relation extraction are carried out on the question sentence, the question sentence is linked to the corresponding entity and relation in the knowledge base, candidate answers are inquired, matched and deduced, and the target answer is obtained. Nowadays, the following problems are mainly faced in the reasoning process of intelligent question answering in the field of search engines:
1) the traditional semantic analysis and information retrieval methods need to manually write a large number of templates or define a large number of rules.
2) The partial deep learning method can only process simple problems, cannot be applied to a complex reasoning process, and consumes a large amount of computing resources.
Disclosure of Invention
In order to overcome the defects of the technologies, the invention provides an efficient and accurate intelligent question-answering method facing a search engine knowledge base.
The technical scheme adopted by the invention for overcoming the technical problems is as follows:
an intelligent question-answering method oriented to a search engine knowledge base comprises the following steps:
a) setting the question searched in the search engine and the corresponding answer, and obtaining the entity e of the question through a natural language processing toolsAnd query relation r of questionqSetting the answer corresponding to the question as eoEntity e for processing Embedding problem by using natural languagesQuerying the relationship rqAnd the answer eoMapping into dense low latitude vector space, resulting in a vector representation epsilon for the entity in questionsQuery vector representation of relationships gammaqAnd vector representation epsilon of the entity of the answero；
b) Entity e extracted from search questionssAnd relation rqDefining the path of reasoning question answers based on the search engine knowledge base as ((r)0,es),(r1,e1),…,(rn,en) Wherein e) isi1, n denotes the ith entity in the path, n is the maximum inference path length, riN denotes the ith relationship in the inference path, r0For the introduced redundancy relationship, r0Entity e associated with the questionsForming actions together, defining a tuple (e, r) formed by an entity e and a relation r traversed in the search process of a search engine knowledge base as an action a, and defining a set of all actions as an action space A;
c) passing the action a of the search path in the search engine knowledge base through natureThe Embedding technique of language processing is mapped to a dense low-dimensional vector space, and a vector representation alpha corresponding to the action a is obtained as (gamma; epsilon), wherein; "is the operation of vector splicing, gamma is the vector representation of the relation r, epsilon is the vector representation of the entity e, and t is the vector alpha of the action a corresponding to the time steptInputting into long and short term memory network LSTM, and processing by formula ht＝LSTM(ht-1,αt) Obtaining a vector representation h of historical memory information corresponding to the time step ttIn the formula ht-1Vector representation of historical memory information corresponding to the t-1 time step;
d) by the formula
Figure GDA0003548812410000021
Calculating to obtain an action space A corresponding to the t time steptCorresponding action fraction piθ(at) In the formula
Figure GDA0003548812410000022
Is an action space AtVector representation, W, through natural language processing Embedding mapping1And W2For the weights of the network model, Relu (. cndot.) is the ReLU function, softmax (. cndot.) is the softmax function,
Figure GDA0003548812410000023
is a matrix product, εtSelecting the motion corresponding to the maximum motion score as the vector representation corresponding to the t-th time of the path of the reasoning question answers of the search engine knowledge base
Figure GDA0003548812410000024
Wherein
Figure GDA0003548812410000025
The relationship corresponding to the action a with the maximum action score,
Figure GDA0003548812410000026
the entity corresponding to the action a with the maximum action score;
e) repeating steps c) through c) based on the search engine knowledge baseStep d), formally defining the obtained reasoning search path as
Figure GDA0003548812410000027
Wherein
Figure GDA0003548812410000028
The action tuple corresponding to the maximum action score is i at the time step, i is 1.
Figure GDA0003548812410000029
Is the relation corresponding to the maximum action score of the ith time step,
Figure GDA00035488124100000210
the entity corresponding to the maximum action score of the ith time step;
f) setting the reward value R of the reasoning answer corresponding to the current reasoning searching path based on the searching path of the searching engine knowledge basetIf the answer is inferred
Figure GDA00035488124100000211
Equal to the answer e corresponding to the questionoThen award value Rt1, if the answer is inferred
Figure GDA00035488124100000212
Not equal to answer e corresponding to the questionoThen by the formula
Figure GDA0003548812410000031
Calculating a reward value RtWherein d is a cosine similarity function,
Figure GDA0003548812410000032
is composed of
Figure GDA0003548812410000033
The vector representation obtained by Embedding,
Figure GDA0003548812410000034
is composed of
Figure GDA0003548812410000035
Vector representation obtained by Embedding;
g) by the formula
Figure GDA0003548812410000036
Calculating the maximum reward value R on the path of the reasoning question answers based on the whole search engine knowledge baseu；
h) The parameter gradient is defined as
Figure GDA0003548812410000037
Wherein
Figure GDA0003548812410000038
For the purpose of graduating the network model parameter θ, where R ═ RuThe parameter optimization of the LSTM network and the feedforward neural network is realized through inverse gradient propagation;
i) repeating the steps a) to h) on the data set of the whole question and the corresponding answer based on a search engine knowledge base to complete model training and obtain a multi-hop inference model with prediction and inference capabilities on the question;
j) inputting a certain question in the multi-hop reasoning model, and obtaining the reasoning question answer through the steps a) to f) if the question has a definite question answer
Figure GDA0003548812410000039
Judging and reasoning out answers to questions
Figure GDA00035488124100000310
Whether the answer is equal to the answer of the real question or not, if the question does not have the answer of the question compared with the answer of the real question, the judgment condition of the search reasoning path on the answer of the question is set as
Figure GDA00035488124100000311
Wherein lambda is a hyperparameter, if the judgment condition is satisfied, predicting the answer entity vector epsilontCorresponding predicted answer entity etFor the answer to the search question, the reasoning process is exited.
Further, the natural language processing tool in step a) is a HanLP natural language processing tool or a deep natural language processing tool.
Preferably, λ in step j) is 0.5.
The invention has the beneficial effects that: based on a search engine knowledge base, an inference path with symbol Markov property is constructed through dynamic search path inference and deep reinforcement learning, path information coding and action space probability distribution calculation are realized based on an LSTM and a feedforward neural network, and meanwhile, judgment conditions are set according to vector representation of the search inference path. Based on the judgment condition and the reasoning path when searching the answers of the questions, the intelligent question answering of the search engine based on the knowledge base is realized. The method does not need to define rules and limit the length of the reasoning path, can be used for a complex question-answer reasoning process based on a search engine knowledge base, and realizes efficient and accurate search engine intelligent question-answer.
Drawings
FIG. 1 is a flow diagram of a multi-hop inference model of the present invention.
Detailed Description
The invention is further described below with reference to fig. 1.
An intelligent question-answering method oriented to a search engine knowledge base comprises the following steps:
a) setting the question searched in the search engine and the corresponding answer, and obtaining the entity e of the question through a natural language processing toolsAnd query relation r of questionqSetting the answer corresponding to the question as eoEntity e for processing Embedding problem by using natural languagesQuerying the relationship rqAnd the answer eoMapping into dense low latitude vector space, resulting in a vector representation epsilon for the entity in questionsQuery vector representation of relationships gammaqAnd vector representation epsilon of the entity of the answero；
b) Entity e extracted from search questionssAnd relation rqDefining a path of reasoning question answers based on a search engine knowledge baseIs ((r)0,es),(r1,e1),…,(rn,en) Wherein e) isiWhere i is 1, …, n denotes the ith entity in the path, n is the maximum inference path length, riN denotes the ith relationship in the inference path, r0For the introduced redundancy relationship, r0Entity e associated with the questionsForming actions together, defining a tuple (e, r) formed by an entity e and a relation r traversed in the search process of a search engine knowledge base as an action a, and defining a set of all actions as an action space A;
c) mapping the action a of the search path in the search engine knowledge base to a dense low-dimensional vector space through an Embedding technology of natural language processing to obtain a vector representation alpha (gamma; epsilon), where "; "is the operation of vector splicing, gamma is the vector representation of the relation r, epsilon is the vector representation of the entity e, and t is the vector alpha of the action a corresponding to the time steptInputting into long and short term memory network LSTM, and processing by formula ht＝LSTM(ht-1,αt) Obtaining a vector representation h of historical memory information corresponding to the time step ttIn the formula ht-1Vector representation of historical memory information corresponding to the t-1 time step;
d) by the formula
Figure GDA0003548812410000051
Calculating to obtain an action space A corresponding to the t time steptCorresponding action fraction piθ(at) In the formula
Figure GDA0003548812410000052
Is an action space AtVector representation, W, through natural language processing Embedding mapping1And W2For the weights of the network model, Relu (. cndot.) is the ReLU function, softmax (. cndot.) is the softmax function,
Figure GDA0003548812410000053
is a matrix product, εtSelecting a maximum action for a vector representation corresponding to the tth time of a path of reasoning question answers to a search engine knowledge baseThe corresponding actions of the score are recorded as
Figure GDA0003548812410000054
Wherein
Figure GDA0003548812410000055
The relationship corresponding to the action a with the maximum action score,
Figure GDA0003548812410000056
the entity corresponding to the action a with the maximum action score;
e) based on the search engine knowledge base, repeating the steps c) to d) to formally define the obtained inference search path as
Figure GDA0003548812410000057
Wherein
Figure GDA0003548812410000058
The action tuple corresponding to the maximum action score is i at the time step, i is 1.
Figure GDA0003548812410000059
Is the relation corresponding to the maximum action score of the ith time step,
Figure GDA00035488124100000510
the entity corresponding to the maximum action score of the ith time step;
f) setting the reward value R of the reasoning answer corresponding to the current reasoning searching path based on the searching path of the searching engine knowledge basetIf the answer is inferred
Figure GDA00035488124100000511
Equal to the answer e corresponding to the questionoThen award value Rt1, if the answer is inferred
Figure GDA00035488124100000512
Not equal to answer e corresponding to the questionoThen by the formula
Figure GDA00035488124100000513
Calculating a reward value RtWherein d is a cosine similarity function,
Figure GDA00035488124100000514
is composed of
Figure GDA00035488124100000515
The vector representation obtained by Embedding,
Figure GDA00035488124100000516
is composed of
Figure GDA00035488124100000517
Vector representation obtained by Embedding;
g) by the formula
Figure GDA00035488124100000518
Calculating the maximum reward value R on the path of the reasoning question answers based on the whole search engine knowledge baseuWherein R ═ RuT is u, and T is the timestamp corresponding to the maximum reward value;
h) the parameter gradient is defined as
Figure GDA00035488124100000519
Wherein
Figure GDA00035488124100000520
For the purpose of graduating the network model parameter θ, where R ═ RuThe parameter optimization of the LSTM network and the feedforward neural network is realized through inverse gradient propagation;
i) repeating the steps a) to h) on the data set of the whole question and the corresponding answer based on a search engine knowledge base to complete model training and obtain a multi-hop inference model with prediction and inference capabilities on the question;
j) inputting a certain question in the multi-hop inference model, and obtaining the question through steps a) to f) if the question has a definite question answerAnswers to questions of reasoning
Figure GDA0003548812410000061
Judging and reasoning out answers to questions
Figure GDA0003548812410000062
Whether the answer is equal to the answer of the real question or not, if the question does not have the answer of the question compared with the answer of the real question, the judgment condition of the search reasoning path on the answer of the question is set as
Figure GDA0003548812410000063
Wherein lambda is a hyperparameter, if the judgment condition is satisfied, predicting the answer entity vector epsilontCorresponding predicted answer entity etFor the answer to the search question, the reasoning process is exited.
Based on a search engine knowledge base, an inference path with symbol Markov property is constructed through dynamic search path inference and deep reinforcement learning, path information coding and action space probability distribution calculation are realized based on an LSTM and a feedforward neural network, and meanwhile, judgment conditions are set according to vector representation of the search inference path. Based on the judgment condition and the reasoning path when searching the answers of the questions, the intelligent question answering of the search engine based on the knowledge base is realized. The method does not need to define rules and limit the length of the reasoning path, can be used for a complex question-answer reasoning process based on a search engine knowledge base, and realizes efficient and accurate search engine intelligent question-answer.
Further, the natural language processing tool in step a) is a HanLP natural language processing tool or a deep natural language processing tool.
Preferably, λ in step j) is 0.5.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (3)

1. An intelligent question-answering method oriented to a search engine knowledge base is characterized by comprising the following steps:
a) setting the question searched in the search engine and the corresponding answer, and obtaining the entity e of the question through a natural language processing toolsAnd query relation r of questionqSetting the answer corresponding to the question as eoEntity e for processing Embedding problem by using natural languagesQuerying the relationship rqAnd the answer eoMapping into dense low latitude vector space, resulting in a vector representation epsilon for the entity in questionsQuery vector representation of relationships gammaqAnd vector representation epsilon of the entity of the answero；
b) Entity e extracted from search questionssAnd relation rqDefining the path of reasoning question answers based on the search engine knowledge base as ((r)0,es),(r1,e1),…,(rn,en) Wherein e) isi1, n denotes the ith entity in the path, n is the maximum inference path length, riN denotes the ith relationship in the inference path, r0For the introduced redundancy relationship, r0Entity e associated with the questionsForming actions together, defining a tuple (e, r) formed by an entity e and a relation r traversed in the search process of a search engine knowledge base as an action a, and defining a set of all actions as an action space A;
c) mapping the action a of the search path in the search engine knowledge base to a dense low-dimensional vector space through an Embedding technology of natural language processing to obtain a vector representation alpha (gamma; epsilon), wheretInputting into long and short term memory network LSTM, and processing by formula ht＝LSTM(ht-1,αt) Obtaining a vector representation of historical memory information corresponding at time step thtIn the formula ht-1Vector representation of historical memory information corresponding to the t-1 time step;
d) by the formula
Figure FDA0003548812400000012
Calculating to obtain an action space A corresponding to the t time steptCorresponding action fraction piθ(at) In the formula
Figure FDA0003548812400000013
Is an action space AtVector representation, W, through natural language processing Embedding mapping1And W2For the weights of the network model, Relu (. cndot.) is the ReLU function, softmax (. cndot.) is the softmax function,
Figure FDA0003548812400000014
is a matrix product, εtSelecting the motion corresponding to the maximum motion score as the vector representation corresponding to the t-th time of the path of the reasoning question answers of the search engine knowledge base
Figure FDA0003548812400000021
Wherein
Figure FDA0003548812400000022
The relationship corresponding to the action a with the maximum action score,
Figure FDA0003548812400000023
the entity corresponding to the action a with the maximum action score;
e) based on the search engine knowledge base, repeating the steps c) to d) to formally define the obtained inference search path as
Figure FDA0003548812400000024
Wherein
Figure FDA0003548812400000025
To be at timeStep i is the action tuple corresponding to the maximum action score, i is 1,.. n,
Figure FDA0003548812400000026
is the relation corresponding to the maximum action score of the ith time step,
Figure FDA0003548812400000027
the entity corresponding to the maximum action score of the ith time step;
f) setting the reward value R of the reasoning answer corresponding to the current reasoning searching path based on the searching path of the searching engine knowledge basetIf the answer is inferred
Figure FDA0003548812400000028
Equal to the answer e corresponding to the questionoThen award value Rt1, if the answer is inferred
Figure FDA0003548812400000029
Not equal to answer e corresponding to the questionoThen by the formula
Figure FDA00035488124000000210
Calculating a reward value RtWherein d is a cosine similarity function,
Figure FDA00035488124000000211
is composed of
Figure FDA00035488124000000212
The vector representation obtained by Embedding,
Figure FDA00035488124000000213
is composed of
Figure FDA00035488124000000214
Vector representation obtained by Embedding;
g) by the formula
Figure FDA00035488124000000215
Calculating the maximum reward value R on the path of the reasoning question answers based on the whole search engine knowledge baseu；
h) The parameter gradient is defined as
Figure FDA00035488124000000216
Wherein
Figure FDA00035488124000000217
For the purpose of graduating the network model parameter θ, where R ═ RuThe parameter optimization of the LSTM network and the feedforward neural network is realized through inverse gradient propagation;
i) repeating the steps a) to h) on the data set of the whole question and the corresponding answer based on a search engine knowledge base to complete model training and obtain a multi-hop inference model with prediction and inference capabilities on the question;
j) inputting a certain question in the multi-hop reasoning model, and obtaining the reasoning question answer through the steps a) to f) if the question has a definite question answer
Figure FDA00035488124000000218
Judging and reasoning out answers to questions
Figure FDA00035488124000000219
Whether the answer is equal to the answer of the real question or not, if the question does not have the answer of the question compared with the answer of the real question, the judgment condition of the search reasoning path on the answer of the question is set as
Figure FDA00035488124000000220
Wherein lambda is a hyperparameter, if the judgment condition is satisfied, predicting the answer entity vector epsilontCorresponding predicted answer entity etFor the answer to the search question, the reasoning process is exited.
2. The intelligent question-answering method oriented to the search engine knowledge base according to claim 1, characterized in that: the natural language processing tool in the step a) is a HanLP natural language processing tool or a deep natural language processing tool.
3. The intelligent question-answering method oriented to the search engine knowledge base according to claim 1, characterized in that: λ ═ 0.5 in step j).
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