WO2020119063A1

WO2020119063A1 - Expert knowledge recommendation method and apparatus, computer device, and storage medium

Info

Publication number: WO2020119063A1
Application number: PCT/CN2019/092507
Authority: WO
Inventors: 吴壮伟
Original assignee: 平安科技（深圳）有限公司
Priority date: 2018-12-11
Filing date: 2019-06-24
Publication date: 2020-06-18
Also published as: CN109325132A

Abstract

The present application discloses an expert knowledge recommendation method and apparatus, a computer device, and a storage medium. Said method comprises: upon reception of an uploaded counseling question to be replied to, acquiring a semantic network vector corresponding thereto; performing a retrieval in a constructed answer reply database to obtain semantic vectors of which the similarity with the semantic network vector is greater than a similarity threshold as target semantic vectors and a list of experts corresponding thereto; acquiring the popularity of each expert in the list of experts, ranking, per popularity, the semantic vectors in a descending order and taking semantic vectors ranked prior to a first rank value to obtain filtered semantic vectors; and acquiring, from the filtered semantic vectors, corresponding expert knowledge recommendation information, and sending same to an uploading end corresponding to said counseling question.

Description

Expert knowledge recommendation method, device, computer equipment and storage medium

This application requires the priority of the Chinese patent application submitted to the Chinese Patent Office on December 11, 2018, with the application number 201811510416.2 and the application name "expert knowledge recommendation method, device, computer equipment and storage medium", the entire content of which is cited by reference Incorporated in this application.

Technical field

This application relates to the field of artificial intelligence technology, and in particular, to a method, device, computer equipment, and storage medium for recommending expert knowledge.

Background technique

At present, when a user has a professional question to be consulted in order to get a reply, generally, after posting the question on the consulting platform, the answerer manually edits to get the reply answer. The existing intelligent online customer service can only answer simple questions. For highly professional questions (such as legal issues, professional technical problems in the high-tech field), the existing intelligent online customer service cannot provide accurate answers.

Summary of the invention

The embodiments of the present application provide an expert knowledge recommendation method, device, computer equipment, and storage medium, which are designed to solve the user's professional problems in the prior art and need to be consulted to obtain a reply. Answers obtained by a person who edits manually cannot obtain a timely response to the consultation, and the professional degree of the answer is greatly limited by the professional knowledge of the respondent.

In the first aspect, the embodiment of the present application provides an expert knowledge recommendation method, which includes: receiving the uploaded consultation questions to be answered, segmenting and extracting the consultation questions to be answered, and obtaining consultation with the pending answers The semantic network vector corresponding to the question; calculating the similarity between the semantic network vector and the semantic vector included in the pre-built reply answer library to obtain the similarity between the reply answer library and the semantic network vector is greater than the preset similarity The semantic vector of degree threshold as the target semantic vector; obtain the expert list corresponding to the target semantic vector; and obtain the heat value of each expert in the expert list, and sort the semantic vectors in descending order according to the heat value of the expert to obtain the ranking After the semantic vector, obtain the semantic vector ranked before the preset first ranking value in the sorted semantic vector to obtain the filtered semantic vector; obtain the corresponding semantic vector in the filtered semantic vector in the reply answer library To obtain expert knowledge recommendation information, and send the expert knowledge recommendation information to the uploader corresponding to the consultation question to be answered.

In a second aspect, an embodiment of the present application provides an expert knowledge recommendation device, which includes: a consultation question acquisition unit for receiving the uploaded consultation questions to be answered, and segmenting and extracting the consultation questions to be answered, Obtain a semantic network vector corresponding to the query question to be answered; a target semantic vector acquisition unit for calculating the similarity between the semantic network vector and the semantic vector included in the pre-built reply answer database to obtain a reply answer database The semantic vector whose similarity between the semantic network vector and the semantic network vector is greater than a preset similarity threshold is used as the target semantic vector; the expert list obtaining unit is used to obtain the expert list corresponding to the target semantic vector; and the sorting unit is used To obtain the heat value of each expert in the expert list, sort the semantic vectors in descending order according to the heat value of the experts to obtain a sorted semantic vector, and obtain the ranking of the sorted semantic vector before the preset first ranking value Semantic vectors to obtain the filtered semantic vectors; consultation reply unit, used to obtain the corresponding response content of each semantic vector in the filtered semantic vectors in the reply answer database to obtain expert knowledge recommendation information, and recommend the expert knowledge recommendation information Send to the uploader corresponding to the question to be answered.

In a third aspect, an embodiment of the present application further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the computer The program implements the expert knowledge recommendation method described in the first aspect above.

According to a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor causes the processor to execute the first On the one hand, the expert knowledge recommendation method.

BRIEF DESCRIPTION

In order to more clearly explain the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings used in the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present application. Ordinary technicians can obtain other drawings based on these drawings without creative work.

1 is a schematic diagram of an application scenario of an expert knowledge recommendation method provided by an embodiment of this application;

2 is a schematic flowchart of an expert knowledge recommendation method provided by an embodiment of the application;

3 is a schematic diagram of a sub-process of a method for recommending expert knowledge provided by an embodiment of the present application;

4 is a schematic diagram of another sub-process of the expert knowledge recommendation method provided by the embodiment of the present application;

5 is a schematic block diagram of an expert knowledge recommendation device provided by an embodiment of this application;

6 is a schematic block diagram of a subunit of an expert knowledge recommendation device provided by an embodiment of this application;

7 is a schematic block diagram of another subunit of an expert knowledge recommendation device provided by an embodiment of this application;

8 is a schematic block diagram of a computer device provided by an embodiment of the present application.

detailed description

The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

It should be understood that when used in this specification and the appended claims, the terms "including" and "comprising" indicate the presence of described features, wholes, steps, operations, elements, and/or components, but do not exclude one or The presence or addition of multiple other features, wholes, steps, operations, elements, components, and/or collections thereof.

It should also be understood that the terminology used in the description of this application is for the purpose of describing particular embodiments only and is not intended to limit this application. As used in the specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

It should also be further understood that the term "and/or" used in the specification of the present application and the appended claims refers to any and all possible combinations of one or more of the associated listed items and includes these combinations .

Please refer to FIGS. 1 and 2, FIG. 1 is a schematic diagram of an application scenario of an expert knowledge recommendation method provided by an embodiment of the present application, and FIG. 2 is a schematic flowchart of an expert knowledge recommendation method provided by an embodiment of the present application. The expert knowledge recommendation method is applied to In the server, this method is executed by the application software installed in the server.

As shown in FIG. 2, the method includes steps S110-S150.

S110. Receive the uploaded consultation questions to be answered, extract the consultation questions to be segmented and extract keywords, and obtain a semantic network vector corresponding to the consultation questions to be answered.

In this embodiment, when a user needs to consult a question online, edit the question to be answered on the user terminal (ie, the uploading end corresponding to the question to be answered), and upload the question to be answered to the server, and the server receives the After the consultation question is to be answered, semantic recognition is performed on the consultation question to be answered, and a semantic network vector corresponding to the consultation question to be answered is obtained. Since the server cannot directly understand the meaning of the consultation question to be answered, but after word segmentation and keyword extraction for the consultation question to be answered, the consultation question to be answered can be converted into a quantized multidimensional row vector or multidimensional column vector, At this time, the approximate question and its answer can be searched in the pre-built reply answer database according to the consultation question to be answered, and the obtained reply is more accurate.

In an embodiment, as shown in FIG. 3, step S110 includes:

S111: Segment the consultation question to be answered by a probability-based word segmentation model to obtain a word segmentation result corresponding to the consultation question to be answered;

S112. Using the word frequency-inverse text frequency index model, extract the keyword information in the word segmentation result before the preset second ranking value as the target keyword set;

S113: Acquire a target word vector corresponding to each keyword information in the target keyword set;

S114. Acquire a semantic network vector corresponding to the consultation question to be answered according to each target word vector and the weight corresponding to each target word vector.

In this embodiment, the word segmentation process of the query question to be answered through a probability-based word segmentation model is as follows:

For example, let C=C1C2...Cm, C be the Chinese character string to be segmented, let W=W1W2...Wn, W be the result of segmentation, Wa, Wb,..., Wk are all possible of C Split the plan. Then, the word segmentation model based on probability statistics can find the target word string W, so that W satisfies: P(W|C)=MAX(P(Wa|C), P(Wb|C)...P(Wk|C) ) Word segmentation model, the word string W obtained by the above word segmentation model is the word string whose estimated probability is the largest. which is:

For a substring S to be segmented, all candidate words w1, w2, ..., wi, ..., wn are taken from left to right; the probability value P(wi) of each candidate word is found in the dictionary, and Record all the left neighbor words of each candidate word; calculate the cumulative probability of each candidate word, and compare to get the best left neighbor word of each candidate word; if the current word wn is the last word of the string S, and the cumulative probability P (wn) is the largest, then wn is the end word of S; from wn, in accordance with the order from right to left, the best left neighbor of each word is output in turn, that is, the word segmentation result of S.

After obtaining the word segmentation results corresponding to the consultation questions to be answered, the word segmentation results are extracted through the word frequency-inverse text frequency index model (ie, TF-IDF model, TF-IDF is the abbreviation of Term, Frequency-Inverse Document Frequency) The keyword information before the preset second ranking value is used as the target keyword set. The keyword information before the preset ranking value in the word segmentation result is extracted through the TF-IDF model, as follows:

1) Calculate the word frequency of each participle i in the participle result, and record it as TF _i ;

2) Calculate the inverse document frequency IDF _i of each participle i in the word segmentation result;

When calculating the inverse document frequency IDFi of each word segmentation i, a corpus (similar to a dictionary in the word segmentation process) is needed to simulate the language usage environment;

Inverse document frequency IDF _i =lg[total number of documents in the corpus/(number of documents containing the participle+1)];

If a word is more common, then the denominator is larger, and the inverse document frequency is smaller and closer to 0. The reason why the denominator is increased by 1 is to avoid the denominator being 0 (that is, all documents do not contain the word).

3) Calculate the word frequency-inverse text frequency index TF-IDFi corresponding to each participle i in the word segmentation result according to TF _i *IDF _i ;

Obviously, TF-IDF is directly proportional to the number of occurrences of a word in the document, and inversely proportional to the number of occurrences of the word in the entire language. Therefore, automatically extracting keywords is to calculate the TF-IDF value of each participle of the document, and then arrange them in descending order, and take the top N words as the keyword list of the document.

4) Sort the word frequency-inverse text frequency index corresponding to each word segmentation in the word segmentation results in descending order, and take the word segment composition ranked before the preset ranking value (for example, the preset ranking value is 21) and the question to be answered Corresponding target keyword set.

After acquiring the target keyword set corresponding to the query question to be answered, the target word vector corresponding to each keyword in the target keyword set can be correspondingly acquired. Among them, the word vector corresponding to the keyword information is obtained based on a pre-constructed vocabulary table query. The process of acquiring the word vector is called word2vec, and its function is to convert words in natural language into dense vectors that can be understood by the computer. For example, in a corpus (that is, a vocabulary), AA, BB, CC, and DD (where AA, BB, CC, and DD represent a Chinese word) each correspond to a vector. Only one value in the vector is 1, and the rest are 0. . That is, the words are converted into discrete individual symbols through One-Hot Encoder (one-hot code), and then converted into low-dimensional continuous values, that is, dense vectors, through Word2Vec dimensionality reduction, and words with similar meanings will be mapped To a similar location in vector space.

Finally, according to the word frequency of each keyword in the word segmentation result, the weight corresponding to each target word vector can be obtained. At this time, according to each target word vector and the weight corresponding to each target word vector, the consultation questions to be answered can be obtained. Corresponding semantic network vector. The specific calculation formula is as follows:

Among them, Vector refers to the semantic network vector corresponding to the query question to be answered, Word_Embedding(kwi) is the target word vector i, and ω _i is the weight corresponding to the target word vector i. Through the above process, the consultation question to be answered can be converted into a multi-dimensional row vector or multi-dimensional column vector, and the quantitative conversion of the consultation question to be answered can be realized.

S120. Calculate the similarity between the semantic network vector and the semantic vector included in the pre-built reply answer library to obtain a semantic vector whose similarity between the reply answer library and the semantic network vector is greater than a preset similarity threshold , As the target semantic vector.

In this embodiment, each piece of answer data in the reply answer database is in the format of expert name, answer content, keyword combination, and semantic vector; where the keyword combination is the answer content, extracted by the TF-IDF model The top N keywords are combined to form a keyword combination (where N is a custom-set value in the server, such as setting N equal to the second ranking value +1); the semantic vector is each keyword corresponding to the content of the answer, and The weight corresponding to each keyword is calculated, and the calculation process is the same as obtaining the semantic network vector corresponding to the query question to be answered. For example, the data of the target answer library is as follows:

序号Serial number	专家姓名Expert name	回答内容Answer content	关键词组合Keyword combination	语义向量Semantic vector
11	AA1AA1	B1B2B3B4B1B2B3B4	B1+B2B1+B2	[C1C2……C3][C1C2……C3]
22	AA2AA2	B1B2B4B7B1B2B4B7	B2+B7B2+B7	[C4C5……C6][C4C5...C6]
……...
NN	AANAAN	B3B4B7B9B3B4B7B9	B3+B9B3+B9	[C7C8……C9][C7C8...C9]

Since the reply answer database is pre-built, when the semantic network vector corresponding to the consultation question to be answered is obtained, the similarity calculation of the semantic network vector and the semantic vector included in the pre-built reply answer database can be performed In order to obtain the answer content with high correlation with the semantic network vector, based on the semantic network matching question and the answer in the question database, it is more able to identify similar questions and improve the quality of question answering.

In an embodiment, as shown in FIG. 4, step S120 includes:

S121. Acquire a target keyword set corresponding to the semantic network vector;

S122. Compare the target keyword set with the keyword combination in the reply answer library, and obtain a keyword combination including the target keyword set in the reply answer library, to obtain a keyword matching result;

S123. Calculate the cosine of the angle between the semantic vector corresponding to each keyword combination in the keyword matching result and the semantic network vector to obtain each keyword combination and the semantic network in the keyword matching result The similarity between vectors is used as a similarity set;

S124: Obtain a similarity degree in the similarity degree set that is greater than the similarity threshold value to obtain a target similarity degree set;

S125. Acquire a semantic vector corresponding to each similarity in the target similarity set as a target semantic vector.

In this embodiment, before the semantic network vector corresponding to the question to be answered, the target keyword set corresponding to the question to be answered is obtained. In this case, in order to improve the retrieval efficiency of obtaining the answer content of the question to be answered, The keyword combination including the target keyword set in the reply answer library may be obtained by comparing the target keyword set with the keyword combination in the reply answer library to obtain a keyword matching result .

Then, in multiple answer data corresponding to the keyword matching result, the similarity between the semantic vector corresponding to each keyword combination in the keyword matching result and the semantic network vector is calculated. When calculating the similarity between vectors, it can be calculated by the following formula:

Among them, a and b represent two vectors respectively, θ is the angle between vector a and vector b. In specific implementation, the similarity threshold is set to 0.5. It can be seen that through the above process, it is possible to quickly screen and obtain the answer content with a high correlation with the semantic network vector.

S130. Acquire an expert list corresponding to the target semantic vector.

In this embodiment, the target semantic vector is obtained, that is, the answer content that is highly relevant to the query question to be answered is obtained, each answer content corresponds to a piece of answer data in the answer answer library, and each answer data is corresponding to An expert name, so after acquiring the target semantic vector, the name of the expert corresponding to each semantic vector included in the target semantic vector can be obtained, thereby forming an expert list.

S140: Obtain the heat value of each expert in the expert list, sort the semantic vectors in descending order according to the heat value of the experts to obtain a sorted semantic vector, and obtain the ranking in the sorted semantic vector before the preset first ranking value To obtain the semantic vector after filtering.

In this embodiment, in order to push more reliable answer content to the uploader corresponding to the query question to be answered, each expert in the expert list corresponding to the target semantic vector may be statistically calculated to rank the heat value The content of the answer of the top expert is regarded as the content of priority recommendation, that is, the answer of the expert trusted by the user can be more recommended, and the accuracy is improved.

In an embodiment, as the first embodiment for calculating the heat value, obtaining the heat value of each expert in the expert list in step S140 includes:

According to the total number of citations of each expert's article in the expert list, to obtain the heat value of each expert.

That is, as the first embodiment for calculating the heat value, the total number of times the articles of each expert in the expert list are cited can be used as the heat value of the expert. If the expert publishes multiple articles, the total number of times that each article is cited in the multiple articles is summed to obtain the expert's heat value.

In an embodiment, as a second embodiment for calculating the heat value, obtaining the heat value of each expert in the expert list in step S140 includes:

Obtaining the sum of the cited values of the articles of each expert in the expert list according to a preset reference value model to obtain the heat value corresponding to each expert in the expert list; wherein, the expert list is

Where value _k represents the heat value of expert k in the expert list, and the reference value between other experts i and expert k in the expert list is

The publication time of the article of expert k in the expert list is T ₀ , the citation time of the article of expert k in the expert list cited by other experts i is T, and λ is the preset adjustment parameter.

That is, as a second embodiment for calculating the heat value, when calculating the heat value of each expert in the expert list, a directed social network structure of experts can be constructed, where the subject is the name of each expert, and the directed side refers to, Expert A quotes expert B's article, then expert A points to expert B. The directed boundary value is the reference value with time decay factor. When calculating the heat value of an expert, the calculation formula is as follows:

The publication time of the article of expert k in the expert list is T ₀ , the citation time of the article of expert k in the expert list cited by other experts i is T, and λ is the preset adjustment parameter (such as setting the adjustment parameter to 0.5).

S150. Acquire corresponding answer content of each semantic vector in the filtered semantic vector in the reply answer database to obtain expert knowledge recommendation information, and send the expert knowledge recommendation information to the uploading end corresponding to the consultation question to be answered.

In this implementation, after calculating the heat value of each expert in the expert list, and using this as a sort to obtain a sorted semantic vector, a semantic vector ranked before the preset first ranking value in the sorted semantic vector is obtained (For example, the top 10 semantic vectors are selected, and the first ranking value is set to 11), and the answer content corresponding to the semantic vectors to obtain expert knowledge recommendation information, which is pushed to the uploading end corresponding to the question to be answered, The pushed expert knowledge recommendation information includes sorting, answer content, and expert name. For example, the pushed expert knowledge recommendation information is as follows:

序号Serial number	专家姓名Expert name	回答内容Answer content
11	AA1AA1	B1B2B3B4B1B2B3B4
22	AA2AA2	B1B2B4B7B1B2B4B7
……...
1010	AA10AA10	B3B4B7B9B3B4B7B9

Through the above-mentioned expert knowledge recommendation information, the uploading end corresponding to the query question to be answered can obtain a highly reliable reply content.

This method adopts semantic recognition technology to recommend expert answers trusted by users to improve the accuracy of recommendations, and based on semantic network matching questions and answers in the answer answer library, it can identify similar questions and improve the quality of question answers.

An embodiment of the present application further provides an expert knowledge recommendation device, which is used to execute any of the foregoing embodiments of the expert knowledge recommendation method. Specifically, please refer to FIG. 5, which is a schematic block diagram of an expert knowledge recommendation device provided by an embodiment of the present application. The expert knowledge recommendation device 100 can be configured in a server.

As shown in FIG. 5, the expert knowledge recommendation device 100 includes a consultation question acquisition unit 110, a target semantic vector acquisition unit 120, an expert list acquisition unit 130, a sorting unit 140, and a consultation reply unit 150.

The consultation question obtaining unit 110 is configured to receive the uploaded consultation questions to be answered, perform word segmentation and keyword extraction on the consultation questions to be answered, and obtain a semantic network vector corresponding to the consultation questions to be answered.

In an embodiment, as shown in FIG. 6, the consultation question obtaining unit 110 includes:

The word segmentation unit 111 is configured to perform word segmentation on the consultation question to be answered based on a probability statistical word segmentation model to obtain a word segmentation result corresponding to the consultation question to be answered;

The keyword extraction unit 112 is used to extract the keyword information located before the preset second ranking value in the word segmentation result through the word frequency-inverse text frequency index model as the target keyword set;

A target word vector acquiring unit 113, configured to acquire a target word vector corresponding to each keyword information in the target keyword set;

The semantic network vector obtaining unit 114 is configured to obtain a semantic network vector corresponding to the question to be answered according to each target word vector and the weight corresponding to each target word vector.

The target semantic vector acquiring unit 120 is configured to calculate the similarity between the semantic network vector and the semantic vector included in the pre-built reply answer database, and obtain that the similarity between the reply answer database and the semantic network vector is greater than the Set the semantic vector of the similarity threshold as the target semantic vector.

In an embodiment, as shown in FIG. 7, the target semantic vector acquisition unit 120 includes:

A target keyword set obtaining unit 121, configured to obtain a target keyword set corresponding to the semantic network vector;

The keyword comparison unit 122 is used for comparing the target keyword set with the keyword combination in the reply answer library, and acquiring the keyword combination including the target keyword set in the reply answer library, to Get keyword matching results;

The similarity set acquisition unit 123 is used to calculate the cosine of the angle between the semantic vector corresponding to each keyword combination in the keyword matching result and the semantic network vector to obtain each key in the keyword matching result The similarity between the word combination and the semantic network vector is used as a similarity set;

The target similarity set acquisition unit 124 is configured to acquire a similarity greater than the similarity threshold in the similarity set to obtain a target similarity set;

The target similarity set parsing unit 125 is used to obtain a semantic vector corresponding to each similarity in the target similarity set as a target semantic vector.

The expert list obtaining unit 130 is configured to obtain an expert list corresponding to the target semantic vector.

The sorting unit 140 is configured to obtain the heat value of each expert in the expert list, sort the semantic vectors in descending order according to the heat value of the experts to obtain the sorted semantic vector, and obtain the ranked position in the semantic vector after the sorting in the preset A semantic vector before the ranking value to obtain the filtered semantic vector.

In an embodiment, as the first embodiment for calculating the heat value, the sorting unit 140 is further used to:

In an embodiment, as a second embodiment for calculating the heat value, the sorting unit 140 is further used to:

The consultation reply unit 150 is used to obtain the corresponding response content of each semantic vector in the filtered semantic vector in the reply answer library to obtain expert knowledge recommendation information, and send the expert knowledge recommendation information to the consultation question to be answered The corresponding upload end.

The device uses semantic recognition technology to recommend expert answers trusted by users to improve the accuracy of recommendations, and based on semantic network matching questions and answers in the answer answer library, it can identify similar questions and improve the quality of question answers.

The above expert knowledge recommendation device may be implemented in the form of a computer program, and the computer program may run on a computer device as shown in FIG. 8.

Please refer to FIG. 8, which is a schematic block diagram of a computer device provided by an embodiment of the present application. The computer device 500 is a server. The server may be an independent server or a server cluster composed of multiple servers.

Referring to FIG. 8, the computer device 500 includes a processor 502, a memory, and a network interface 505 connected through a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504. The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, the processor 502 can execute the expert knowledge recommendation method. The processor 502 is used to provide computing and control capabilities and support the operation of the entire computer device 500. The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can cause the processor 502 to perform an expert knowledge recommendation method. The network interface 505 is used for network communication, such as the transmission of data information. Those skilled in the art can understand that the structure shown in FIG. 8 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or less components than shown in the figure, or combine certain components, or have a different arrangement of components.

Wherein, the processor 502 is used to run the computer program 5032 stored in the memory to implement the expert knowledge recommendation method of the embodiment of the present application.

Those skilled in the art can understand that the embodiment of the computer device shown in FIG. 8 does not constitute a limitation on the specific configuration of the computer device. In other embodiments, the computer device may include more or fewer components than shown in the figure. Or combine certain components, or arrange different components. For example, in some embodiments, the computer device may include only a memory and a processor. In such an embodiment, the structures and functions of the memory and the processor are consistent with the embodiment shown in FIG. 8 and will not be repeated here.

It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (Central Processing Unit, CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), Application specific integrated circuit (Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor.

In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium may be a non-volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, where the computer program is executed by a processor to implement the expert knowledge recommendation method of the embodiments of the present application.

The storage medium may be an internal storage unit of the foregoing device, such as a hard disk or a memory of the device. The storage medium may also be an external storage device of the device, such as a plug-in hard disk equipped on the device, a smart memory card (Smart) Card (SMC), a secure digital (SD) card, or a flash memory card (Flash Card) etc. Further, the storage medium may also include both an internal storage unit of the device and an external storage device.

Those skilled in the art can clearly understand that, for the convenience and conciseness of the description, the specific working processes of the devices, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and are not repeated here.

The above is only the specific implementation of this application, but the scope of protection of this application is not limited to this, any person skilled in the art can easily think of various equivalents within the technical scope disclosed in this application Modifications or replacements, these modifications or replacements should be covered within the scope of protection of this application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

An expert knowledge recommendation method, including:

Receiving the uploaded consultation questions to be answered, extracting the word consultation and keyword extraction of the consultation questions to be answered, and obtaining a semantic network vector corresponding to the consultation questions to be answered;

Calculating the similarity between the semantic network vector and the semantic vector included in the pre-built reply answer library to obtain a semantic vector whose similarity between the reply answer library and the semantic network vector is greater than a preset similarity threshold, to As the target semantic vector;

Obtaining an expert list corresponding to the target semantic vector;

Obtaining the heat value of each expert in the expert list, sorting the semantic vectors in descending order according to the heat value of the experts to obtain the sorted semantic vector, and obtaining the semantics in the sorted semantic vector ranked before the preset first ranking value Vector to get the filtered semantic vector; and

Obtain corresponding response content of each semantic vector in the filtered semantic vector in the reply answer library to obtain expert knowledge recommendation information, and send the expert knowledge recommendation information to the uploading end corresponding to the consultation question to be answered.
The expert knowledge recommendation method according to claim 1, wherein the word segmentation and keyword extraction of the consultation question to be answered to obtain a semantic network vector corresponding to the consultation question to be answered include:

Segmenting the consultation question to be answered by a probability-based word segmentation model to obtain a word segmentation result corresponding to the consultation question to be answered;

Using the word frequency-inverse text frequency index model, extract the keyword information in the word segmentation result before the preset second ranking value as the target keyword set;

Acquiring a target word vector corresponding to each keyword information in the target keyword set;

According to each target word vector and the weight corresponding to each target word vector, a semantic network vector corresponding to the consultation question to be answered is obtained.
The expert knowledge recommendation method according to claim 2, wherein the similarity calculation is performed between the semantic network vector and the semantic vector included in the pre-built reply answer library to obtain the reply network and the semantic network The semantic vector whose similarity between the vectors is greater than the preset similarity threshold is used as the target semantic vector, including:

Acquiring the target keyword set corresponding to the semantic network vector;

Comparing the target keyword set with the keyword combination in the reply answer library, and acquiring the keyword combination including the target keyword set in the reply answer library to obtain a keyword matching result;

Calculating the cosine of the angle between the semantic vector corresponding to each keyword combination in the keyword matching result and the semantic network vector to obtain the relationship between each keyword combination and the semantic network vector in the keyword matching result Between the similarities, as a set of similarities;

Obtaining a similarity in the similarity set that is greater than the similarity threshold to obtain a target similarity set;

Acquire a semantic vector corresponding to each similarity in the target similarity set as a target semantic vector.
The expert knowledge recommendation method according to claim 1, wherein the obtaining the heat value of each expert in the expert list comprises: accumulating the total number of times that the articles of each expert in the expert list are cited to obtain the corresponding An expert's heat value.
The expert knowledge recommendation method according to claim 1, wherein the acquiring the heat value of each expert in the expert list includes:

Obtaining the sum of the cited values of the articles of each expert in the expert list according to a preset reference value model to obtain the heat value corresponding to each expert in the expert list; wherein, the expert list is
Where value k represents the heat value of expert k in the expert list, and the reference value between other experts i and expert k in the expert list is
The publication time of the article of expert k in the expert list is T 0 , the citation time of the article of expert k in the expert list cited by other experts i is T, and λ is the preset adjustment parameter.
The expert knowledge recommendation method according to claim 2, wherein the keyword information before the preset second ranking value in the word segmentation result is extracted as the target keyword set by the word frequency-inverse text frequency index model ,include:

Calculate the word frequency of each participle in the word segmentation result;

Calculate the inverse document frequency of each word segmentation in the word segmentation result;

Calculate the word frequency-inverse text frequency index corresponding to each participle in the word segmentation result according to the word frequency*inverse document frequency;

The word frequency-inverse text frequency index corresponding to each word segmentation in the word segmentation result is sorted in descending order, and the word segmentation ranked before the preset second ranking value is used to form a target keyword set corresponding to the query question to be answered.
The expert knowledge recommendation method according to claim 5, wherein the acquisition of the sum of citation values of the articles of each expert in the expert list is obtained according to a preset reference value model to obtain the expert list Before the heat value corresponding to each expert, it also includes:

Construct an expert's directed social network structure; where the subject is the name of each expert in the directed social network structure, and the directed boundary value is the reference value with a time decay factor between experts; the time of publication of expert k's article in the expert list Is T 0 , the citation time of the article of other expert i citing expert k in the expert list is T, and the reference value between other expert i and expert k in the expert list is
λ is the preset adjustment parameter.
An expert knowledge recommendation device, which includes:

The consultation question obtaining unit is configured to receive the uploaded consultation questions to be answered, and perform word segmentation and keyword extraction on the consultation questions to be answered, to obtain a semantic network vector corresponding to the consultation questions to be answered;

A target semantic vector acquiring unit, configured to calculate the similarity between the semantic network vector and the semantic vector included in the pre-built reply answer library to obtain a similarity between the reply answer library and the semantic network vector greater than a preset The semantic vector of the similarity threshold is used as the target semantic vector;

An expert list obtaining unit, configured to obtain an expert list corresponding to the target semantic vector;

The sorting unit is used to obtain the heat value of each expert in the expert list, sort the semantic vectors in descending order according to the heat value of the experts to obtain the sorted semantic vector, and obtain the ranked first position in the semantic vector after sorting The semantic vector before the ranking value to get the filtered semantic vector; and

The consultation reply unit is used to obtain the corresponding response content of each semantic vector in the filtered semantic vector in the reply answer database to obtain expert knowledge recommendation information, and send the expert knowledge recommendation information to correspond to the consultation question to be answered Uploader.
The expert knowledge recommendation device according to claim 8, wherein the consultation question acquisition unit includes:

The word segmentation unit is used to segment the consultation question to be answered based on a probability statistical word segmentation model to obtain a word segmentation result corresponding to the consultation question to be answered;

The keyword extraction unit is used to extract keyword information before the preset second ranking value in the word segmentation result through the word frequency-inverse text frequency index model as the target keyword set;

A target word vector acquiring unit, configured to acquire a target word vector corresponding to each keyword information in the target keyword set;

The semantic network vector acquisition unit is used to acquire the semantic network vector corresponding to the question to be answered according to each target word vector and the weight corresponding to each target word vector.
The expert knowledge recommendation device according to claim 9, wherein the target semantic vector acquisition unit includes:

A target keyword set acquisition unit, configured to acquire a target keyword set corresponding to the semantic network vector;

The keyword comparison unit is used to compare the target keyword set with the keyword combination in the reply answer library, and obtain the keyword combination including the target keyword set in the reply answer library to obtain Keyword matching results;

The similarity set acquisition unit is used to calculate the cosine value of the angle between the semantic vector corresponding to each keyword combination in the keyword matching result and the semantic network vector to obtain each keyword in the keyword matching result Combining the similarity between the semantic network vector as a similarity set;

A target similarity set acquisition unit, configured to acquire a similarity greater than the similarity threshold in the similarity set to obtain a target similarity set;

The target similarity set parsing unit is used to obtain a semantic vector corresponding to each similarity in the target similarity set as a target semantic vector.
A computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the following steps when executing the computer program:

Receiving the uploaded consultation questions to be answered, extracting the word consultation and keyword extraction of the consultation questions to be answered, and obtaining a semantic network vector corresponding to the consultation questions to be answered;

Calculating the similarity between the semantic network vector and the semantic vector included in the pre-built reply answer library to obtain a semantic vector whose similarity between the reply answer library and the semantic network vector is greater than a preset similarity threshold, to As the target semantic vector;

Obtaining an expert list corresponding to the target semantic vector;

Obtaining the heat value of each expert in the expert list, sorting the semantic vectors in descending order according to the heat value of the experts to obtain the sorted semantic vector, and obtaining the semantics in the sorted semantic vector ranked before the preset first ranking value Vector to get the filtered semantic vector; and

Obtain corresponding response content of each semantic vector in the filtered semantic vector in the reply answer library to obtain expert knowledge recommendation information, and send the expert knowledge recommendation information to the uploading end corresponding to the consultation question to be answered.
The computer device according to claim 11, wherein the word segmentation and keyword extraction of the consultation question to be answered to obtain a semantic network vector corresponding to the consultation question to be answered include:

Segmenting the consultation question to be answered by a probability-based word segmentation model to obtain a word segmentation result corresponding to the consultation question to be answered;

Using the word frequency-inverse text frequency index model, extract the keyword information in the word segmentation result before the preset second ranking value as the target keyword set;

Acquiring a target word vector corresponding to each keyword information in the target keyword set;

According to each target word vector and the weight corresponding to each target word vector, a semantic network vector corresponding to the consultation question to be answered is obtained.
The computer device according to claim 12, wherein the similarity calculation is performed between the semantic network vector and a semantic vector included in a pre-built reply answer library to obtain a relationship between the semantic network vector and the semantic network vector The semantic vector whose inter-similarity is greater than the preset similarity threshold is used as the target semantic vector, including:

Acquiring the target keyword set corresponding to the semantic network vector;

Comparing the target keyword set with the keyword combination in the reply answer library, and acquiring the keyword combination including the target keyword set in the reply answer library to obtain a keyword matching result;

Calculating the cosine of the angle between the semantic vector corresponding to each keyword combination in the keyword matching result and the semantic network vector to obtain the relationship between each keyword combination and the semantic network vector in the keyword matching result Between the similarities, as a set of similarities;

Obtaining a similarity in the similarity set that is greater than the similarity threshold to obtain a target similarity set;

The semantic vector corresponding to each similarity in the target similarity set is obtained as the target semantic vector.
The computer device according to claim 11, wherein the obtaining the heat value of each expert in the expert list comprises: accumulating a total number of times that the articles of each expert in the expert list are cited to obtain each expert correspondingly 'S heat value.
The computer device according to claim 11, wherein the acquiring the heat value of each expert in the expert list comprises:

Obtaining the sum of the cited values of the articles of each expert in the expert list according to a preset reference value model to obtain the heat value corresponding to each expert in the expert list; wherein, the expert list is
Where value k represents the heat value of expert k in the expert list, and the reference value between other experts i and expert k in the expert list is
The publication time of the article of expert k in the expert list is T 0 , the citation time of the article of expert k in the expert list cited by other experts i is T, and λ is the preset adjustment parameter.
The computer device according to claim 12, wherein the keyword information before the preset second ranking value in the word segmentation result is extracted by the word frequency-inverse text frequency index model as the target keyword set, including :

Calculate the word frequency of each participle in the word segmentation result;

Calculate the inverse document frequency of each word segmentation in the word segmentation result;

Calculate the word frequency-inverse text frequency index corresponding to each participle in the word segmentation result according to the word frequency*inverse document frequency;

The word frequency-inverse text frequency index corresponding to each word segmentation in the word segmentation result is sorted in descending order, and the word segmentation ranked before the preset second ranking value is used to form a target keyword set corresponding to the query question to be answered.
The computer device according to claim 15, wherein the acquisition of the sum of citation values of the articles of each expert in the expert list is obtained according to a preset reference value model to obtain Before the heat value corresponding to the expert, it also includes:

Construct an expert's directed social network structure; where the subject is the name of each expert in the directed social network structure, and the directed boundary value is the reference value with time decay factor between experts; the publication time of the expert k's article in the expert list Is T 0 , the citation time of the article of other expert i citing expert k in the expert list is T, and the reference value between other expert i and expert k in the expert list is
λ is the preset adjustment parameter.
A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which when executed by a processor causes the processor to perform the following operations:

Receiving the uploaded consultation questions to be answered, extracting the word consultation and keyword extraction of the consultation questions to be answered, and obtaining a semantic network vector corresponding to the consultation questions to be answered;

Calculating the similarity between the semantic network vector and the semantic vector included in the pre-built reply answer library to obtain a semantic vector whose similarity between the reply answer library and the semantic network vector is greater than a preset similarity threshold, to As the target semantic vector;

Obtaining an expert list corresponding to the target semantic vector;

Obtaining the heat value of each expert in the expert list, sorting the semantic vectors in descending order according to the heat value of the experts to obtain the sorted semantic vector, and obtaining the semantics in the sorted semantic vector ranked before the preset first ranking value Vector to get the filtered semantic vector; and

Obtain corresponding response content of each semantic vector in the filtered semantic vector in the reply answer library to obtain expert knowledge recommendation information, and send the expert knowledge recommendation information to the uploading end corresponding to the consultation question to be answered.
The computer readable storage medium according to claim 18, wherein the word segmentation and keyword extraction of the consultation question to be answered to obtain a semantic network vector corresponding to the consultation question to be answered include:

Segmenting the consultation question to be answered by a probability-based word segmentation model to obtain a word segmentation result corresponding to the consultation question to be answered;

Using the word frequency-inverse text frequency index model, extract the keyword information in the word segmentation result before the preset second ranking value as the target keyword set;

Acquiring a target word vector corresponding to each keyword information in the target keyword set;

According to each target word vector and the weight corresponding to each target word vector, a semantic network vector corresponding to the consultation question to be answered is obtained.
The computer-readable storage medium according to claim 19, wherein the similarity calculation is performed on the semantic network vector and the semantic vector included in the pre-built reply answer database to obtain the semantics in the reply answer database A semantic vector whose similarity between network vectors is greater than a preset similarity threshold is used as the target semantic vector, including:

Acquiring the target keyword set corresponding to the semantic network vector;

Comparing the target keyword set with the keyword combination in the reply answer library, and acquiring the keyword combination including the target keyword set in the reply answer library to obtain a keyword matching result;

Calculating the cosine of the angle between the semantic vector corresponding to each keyword combination in the keyword matching result and the semantic network vector to obtain the relationship between each keyword combination and the semantic network vector in the keyword matching result Between the similarities, as a set of similarities;

Obtaining a similarity in the similarity set that is greater than the similarity threshold to obtain a target similarity set;

Acquire a semantic vector corresponding to each similarity in the target similarity set as a target semantic vector.