Method, apparatus, device and medium for determining standard terms

Abstract

The present disclosure provides a method for determining standard terms, which can be applied to the field of artificial intelligence technology. The method includes: performing word segmentation on the statement information to be matched to obtain M keywords, where the statement information includes text data for describing standard terms; determining N first standard terms that match the M keywords based on a knowledge graph, where the first standard terms include institutional terms for a target user; determining at least one second standard term corresponding to each of the N first standard terms according to the association relationship between the first standard terms and the second standard terms to obtain P second standard terms, where the second standard terms include general standard terms; and displaying the N first standard terms and the P second standard terms that match the statement information. The present disclosure also provides a device, equipment, storage medium, and program product for determining standard terms.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence, and particularly to a method, apparatus, electronic device, medium, and program product for determining standard terms. Background Art

[0002] Currently, internal audits within enterprises still rely on the manual operations of auditors. For example, in the case of obtaining non-standard clause texts, auditors can determine one or more enterprise-related clauses corresponding to the non-standard clause texts based on their own experience. Then, audit work is carried out according to the found enterprise-related clauses.

[0003] In related technologies, for auditors who are not familiar with enterprise-related clauses or lack experience, they cannot quickly and accurately determine the corresponding enterprise-related clauses based on non-standard clause texts, resulting in high training costs and low audit efficiency for auditors. In addition, due to the relatively similar text contents of enterprise-related clauses, the enterprise-related clauses determined based on non-standard clause texts have errors and low credibility. Summary of the Invention

[0004] In view of the above problems, the present disclosure provides a method, apparatus, device, medium, and program product for determining standard terms.

[0005] According to a first aspect of the present disclosure, there is provided a method for determining standard terms, including: performing word segmentation processing on the statement information to be matched to obtain M keywords, where M is greater than or equal to 1, and the statement information includes text data for describing standard terms;

[0006] Based on a knowledge graph, determining N first standard terms that match the M keywords, where the first standard terms include institutional clauses for target users, and N is greater than or equal to 1;

[0007] According to the association relationship between the first standard terms and the second standard terms, determining at least one second standard term corresponding to each of the N first standard terms to obtain P second standard terms, where P is greater than or equal to 1, and P is greater than or equal to N, and the second standard terms include general standard terms; and

[0008] Displaying the N first standard terms and the P second standard terms that match the statement information.

[0009] According to an embodiment of the present disclosure, where determining N first standard terms that match the M keywords based on a knowledge graph includes:

[0010] Processing the M keywords using the knowledge graph to obtain Q first standard terms that match the M keywords, where Q is greater than or equal to 1, and Q is greater than or equal to N;

[0011] Deduplicate Q first standard clauses to obtain L first standard clauses and keyword data. The keyword data includes the number of keywords hit by each of the L first standard clauses. Here, L is greater than or equal to N and less than or equal to Q;

[0012] According to the importance level of the keywords and the keyword data, screen out N first standard clauses from the L first standard clauses.

[0013] According to an embodiment of the present disclosure, wherein screening out N first standard clauses from the L first standard clauses according to the importance level of the keywords and the keyword data includes:

[0014] According to the keyword data, sort the L first standard clauses according to the number of keywords to obtain a first standard clause sequence;

[0015] According to the importance level of the keywords, update the order of the first standard clauses with the same number of keywords to obtain an updated first standard clause sequence; and

[0016] Screen out N first standard clauses from the updated first standard clause sequence.

[0017] According to an embodiment of the present disclosure, wherein the association relationship includes the similarity between the first standard clause and the second standard clause; according to the association relationship between the first standard clause and the second standard clause, determine at least one second standard clause corresponding to each of the N first standard clauses to obtain P second standard clauses, including:

[0018] Determine the similarity between the i-th first standard clause among the N first standard clauses and multiple second standard clauses, where i is greater than or equal to 1 and less than or equal to N; and

[0019] Use the second standard clauses whose similarity meets the preset threshold as the second standard clauses corresponding to the i-th first standard clause to obtain P second standard clauses.

[0020] According to an embodiment of the present disclosure, wherein the similarity includes the Pearson correlation coefficient and the Euclidean distance; determining the similarity between the i-th first standard clause among the N first standard clauses and multiple second standard clauses includes:

[0021] Determine the Pearson correlation coefficient and the Euclidean distance between the i-th first standard clause and multiple second standard clauses; and

[0022] Perform a weighted sum of the Pearson correlation coefficient and the Euclidean distance to obtain the similarity between the i-th first standard clause and multiple second standard clauses.

[0023] According to an embodiment of the present disclosure, presenting N first standard clauses and P second standard clauses that match the statement information includes:

[0024] Presenting N first standard clauses in the order of the updated first standard clause sequence; and

[0025] In response to an operation of a target user on the j-th first standard clause among the N first standard clauses, presenting the second standard clause corresponding to the j-th first standard clause, where j is greater than or equal to 1 and j is less than or equal to N.

[0026] A second aspect of the present disclosure provides an apparatus for determining standard clauses, including: a first determination module for performing word segmentation processing on the statement information to be matched to obtain M keywords, where M is greater than or equal to 1, and the statement information includes text data for describing standard clauses;

[0027] A second determination module for determining N first standard clauses that match the M keywords based on a knowledge graph, where the first standard clauses include institutional clauses preset by the target user, and N is greater than or equal to 1;

[0028] A third determination module for determining at least one second standard clause corresponding to each of the N first standard clauses according to the association relationship between the first standard clauses and the second standard clauses to obtain P second standard clauses, where P is greater than or equal to 1 and P is greater than or equal to N, and the second standard clauses include general standard clauses; and

[0029] A display module for presenting N first standard clauses and P second standard clauses that match the statement information.

[0030] A third aspect of the present disclosure provides an electronic device, including: one or more processors; a memory for storing one or more programs, where when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the above method for determining standard clauses.

[0031] A fourth aspect of the present disclosure further provides a computer-readable storage medium, on which executable instructions are stored, and when the instructions are executed by a processor, the processor is caused to execute the above method for determining standard clauses.

[0032] A fifth aspect of the present disclosure further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the above method for determining standard clauses is implemented. Description of the Drawings

[0033] Through the following description of the embodiments of the present disclosure with reference to the drawings, the above content and other objects, features, and advantages of the present disclosure will become clearer. In the drawings:

[0034] Figure 1 Schematically shows an application scenario of a method for determining standard terms according to an embodiment of the present disclosure;

[0035] Figure 2 Schematically shows a flowchart of a method for determining standard terms according to an embodiment of the present disclosure;

[0036] Figure 3 Schematically shows a flowchart of a method for determining a first standard term according to an embodiment of the present disclosure;

[0037] Figure 4 Schematically shows a flowchart of a method for determining a second standard term according to an embodiment of the present disclosure;

[0038] Figure 5 Schematically shows a flowchart of a method for determining the similarity between a first standard term and a second standard term according to an embodiment of the present disclosure;

[0039] Figure 6 Schematically shows a flowchart of a method for presenting a first standard term and a second standard term according to an embodiment of the present disclosure;

[0040] Figure 7 Schematically shows a schematic diagram of a display interface according to a specific embodiment of the present disclosure;

[0041] Figure 8 Schematically shows a structural block diagram of an apparatus for determining standard terms according to an embodiment of the present disclosure; and

[0042] Figure 9 Schematically shows a block diagram of an electronic device suitable for a method for determining standard terms according to an embodiment of the present disclosure. Detailed Description of the Embodiment

[0043] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description, for the sake of explanation, many specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments can be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts of the present disclosure.

[0044] The terms used herein are merely for describing specific embodiments and are not intended to limit the present disclosure. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0045] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those of ordinary skill in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0046] In cases where expressions similar to "at least one of A, B, and C, etc." are used, generally, it should be interpreted according to the meaning commonly understood by those of ordinary skill in the art (for example, "a system having at least one of A, B, and C" should include, but not be limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0047] In the technical solutions of this disclosure, the processing of the collection, storage, use, processing, transmission, provision, disclosure, and application, etc. of the user's personal information involved all comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good customs.

[0048] In the technical solutions of this disclosure, before obtaining or collecting the user's personal information, the authorization or consent of the user has been obtained.

[0049] Embodiments of the present disclosure provide a method for determining standard terms, including: performing word segmentation processing on the statement information to be matched to obtain M keywords, where M is greater than or equal to 1, and the statement information includes text data for describing standard terms; based on the knowledge graph, determining N first standard terms that match the M keywords, the first standard terms including institutional terms for target users, where N is greater than or equal to 1; according to the association relationship between the first standard terms and the second standard terms, determining at least one second standard term corresponding to each of the N first standard terms to obtain P second standard terms, where P is greater than or equal to 1, and P is greater than or equal to N, and the second standard terms include general standard terms; and displaying the N first standard terms and the P second standard terms that match the statement information.

[0050] Figure 1 Schematically shows an application scenario for the determination of standard terms according to an embodiment of the present disclosure.

[0051] As Figure 1 shown, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0052] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. An application software for determining standard terms can be installed on terminal devices 101, 102, and 103, or users can log in to a web interface for determining standard terms on terminal devices 101, 102, and 103.

[0053] Terminal devices 101, 102, and 103 can be various electronic devices with a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and so on.

[0054] Server 105 can be a server providing various services, such as a background management server (only for example) that supports the websites browsed by users using terminal devices 101, 102, and 103. The background management server can analyze and process data such as received user requests, and feedback the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0055] It should be noted that the method for determining standard terms provided by the embodiments of the present disclosure can generally be executed by server 105. Correspondingly, the device for determining standard terms provided by the embodiments of the present disclosure can generally be set in server 105. The method for determining standard terms provided by the embodiments of the present disclosure can also be executed by a server or a server cluster different from server 105 and capable of communicating with terminal devices 101, 102, and 103 and / or server 105. Correspondingly, the device for determining standard terms provided by the embodiments of the present disclosure can also be set in a server or a server cluster different from server 105 and capable of communicating with terminal devices 101, 102, and 103 and / or server 105.

[0056] It should be understood that Figure 1 the numbers of terminal devices, networks, and servers in

[0057] are merely illustrative. According to actual needs, there can be any number of terminal devices, networks, and servers. Figure 1 Based on the scenario described below Figures 2 to 7 the method for determining standard terms of the disclosed embodiments will be described in detail.

[0058] Figure 2 Schematically shows a flowchart of the method for determining standard terms according to an embodiment of the present disclosure.

[0059] As Figure 2 shown, the method includes operations S210 to S240.

[0060] In operation S210, word segmentation is performed on the statement information to be matched, and M keywords are obtained.

[0061] According to an embodiment of the present disclosure, the statement information to be matched includes text data for describing standard terms. Specifically, the statement information can be a piece of natural language input by a user, or text data converted from various types of data input by the user. For example, the various types of data input by the user include: picture data, video data, and audio data.

[0062] According to an embodiment of the present disclosure, through part-of-speech analysis, the input statement information is segmented into multiple Chinese words. After determining the required target part of speech, M keywords of the target part of speech are selected from the multiple Chinese words obtained by word segmentation. For example, the target part of speech is set to a noun, and M keywords of the noun part of speech are selected from the multiple Chinese words.

[0063] For example, the statement information is "want to know how to strengthen security management and ensure network availability and reliability". After part-of-speech analysis, the multiple Chinese words obtained are "want to know", "how", "strengthen", "security management", "ensure", "network", "availability", "and", "reliability". After setting the target part of speech to a noun, "security management", "network", "availability", and "reliability" are selected from the multiple Chinese words.

[0064] According to an embodiment of the present disclosure, the keywords obtained after part-of-speech screening can also be screened a second time according to the occurrence frequency of the keywords. Specifically, the occurrence frequency of the keywords can be determined according to a general word library or a user dictionary. After part-of-speech screening, the occurrence frequency of the obtained M keywords is determined. In the case where it is determined that there is a keyword with an occurrence frequency lower than a preset threshold among the M keywords, the keyword is updated according to the general word library or the user dictionary.

[0065] After the present disclosure performs word segmentation on the statement information, based on the part of speech and the occurrence frequency of the keywords, the obtained word segmentation data is screened, which improves the accuracy of word segmentation and helps to improve the accuracy of subsequent matching of the first standard terms.

[0066] For example, still taking the statement information "want to know how to strengthen security management and ensure network availability and reliability" as an example, after obtaining "security management", "network", "availability", and "reliability", according to the general word library or the user dictionary, it is determined that the occurrence frequency of "security management" is less than 5 times, while the occurrence frequency of "security" is higher than that of "security management". Therefore, the keywords "security management", "network", "availability", and "reliability" obtained after part-of-speech screening are updated to "security", "network", "availability", and "reliability".

[0067] In operation S220, based on the knowledge graph, determine N first standard clauses that match the M keywords.

[0068] According to an embodiment of the present disclosure, after determining the M keywords based on the statement information, input the M keywords into a pre-constructed knowledge graph to determine the first standard clauses that match the M keywords.

[0069] The knowledge graph can be constructed according to the clause content of the current first standard clauses. Specifically, the clause content of all first standard clauses can be segmented, and then the data after segmentation can be screened by part of speech to obtain keywords under different parts of speech. Associate the clause numbers of the first standard clauses with the keywords under different parts of speech to form the knowledge graph.

[0070] For example, the nodes of the knowledge graph are clause numbers, and information such as part of speech, keywords, and keyword frequencies can be used as the attribute information of the nodes.

[0071] According to an embodiment of the present disclosure, the first standard clauses include institutional clauses for the target user. The target user can be a certain enterprise, or a department or section within the enterprise that implements different business tasks. Specifically, the first standard clauses can be quality inspection clauses set for multiple departments within the enterprise. For example, "Review the operation logs of the equipment every three months to check for vulnerabilities" and so on.

[0072] In operation S230, according to the association relationship between the first standard clauses and the second standard clauses, determine at least one second standard clause corresponding to each of the N first standard clauses to obtain P second standard clauses.

[0073] According to an embodiment of the present disclosure, after determining the N first standard clauses that match based on the M keywords, the second standard clauses corresponding to each of the N first standard clauses can also be determined according to the association relationship between the first standard clauses and the second standard clauses.

[0074] According to an embodiment of the present disclosure, the second standard clauses include general standard clauses. For example, for the safety production department, the general standard clauses among multiple enterprises are "Review the operation logs of the equipment", "Check for vulnerabilities", "Daily maintenance", etc.

[0075] For the same business type, one or more second standard clauses are common among multiple enterprises. For the second standard clauses for the same business type, each enterprise can set specific first standard clauses. For multiple enterprises, there are multiple association relationships between the first standard clauses and the second standard clauses.

[0076] According to an embodiment of the present disclosure, the target user can pre-establish the association relationship between the first standard clauses and the second standard clauses according to the first standard clauses of the affiliated enterprise.

[0077] In operation S240, N first standard clauses and P second standard clauses that match the statement information are displayed.

[0078] According to an embodiment of the present disclosure, after determining P second standard clauses related to the N first standard clauses according to the association relationship between the first standard clauses and the second standard clauses, the N first standard clauses and the P second standard clauses that match the input statement information can be displayed on a preset interaction interface.

[0079] Specifically, only the clause numbers of the N first standard clauses and the P second standard clauses can be displayed, and a text link corresponding to the clause content can be added after the clause numbers; the clause numbers and the clause content can also be displayed simultaneously for the user to view.

[0080] The present disclosure realizes the rapid search of the first standard clauses by performing word segmentation on the statement information, extracting M keywords in the statement information, and then determining N first standard clauses that match the M keywords based on the knowledge graph, without spending a large amount of labor costs and time costs on the search and matching of the first standard clauses. On the basis of reducing labor costs, the evaluation credibility of the audit process is also improved.

[0081] In addition, after determining the first standard clauses, the present disclosure also recommends P second standard clauses related to the first standard clauses according to the association relationship between the first standard clauses and the second standard clauses, further realizing the recommendation of multiple types of standard clauses related to the statement information, expanding the source of standard clauses that match the statement information, and improving the evaluation credibility. In addition, the present disclosure realizes an intuitive, clear, and comprehensive display of the standard clauses related to the statement information by displaying the N first standard clauses and the P second standard clauses that match the statement information, improving the user experience, and helping to improve the evaluation efficiency of the subsequent audit process.

[0082] According to an embodiment of the present disclosure, the information to be matched input by the user can be various types of information, such as text data, picture data, video data, etc. For picture data, before performing word segmentation processing on the statement information to be matched, it further includes: using the method of optical character recognition (OCR) to convert the image data into statement information in text form.

[0083] For video data, the video data can be converted into video frame data. Then, the optical character is used to process the video data under each frame, and the video data is converted into text data to obtain the statement information after data type conversion.

[0084] For audio data, automatic speech recognition (ASR) can be used to extract features, perform pattern matching, and convert text on the input audio data to obtain statement information after data type conversion.

[0085] Figure 3 FIG. schematically shows a flowchart of a method for determining a first standard clause according to an embodiment of the present disclosure.

[0086] As Figure 3 shown, the method of this embodiment includes operations S321 to S323, and can be a specific embodiment of operation S220.

[0087] In operation S321, a knowledge graph is used to process M keywords to obtain Q first standard clauses that match the M keywords.

[0088] According to an embodiment of the present disclosure, each node in the knowledge graph can be the clause number of the first standard clause, or can also be other forms of unique identifiers.

[0089] After obtaining the M keywords, the M keywords are input into the knowledge graph in sequence. By retrieving the input keywords, the knowledge graph outputs multiple first standard clause numbers that match the keywords, and the clause content corresponding to the multiple first standard clause numbers.

[0090] According to an embodiment of the present disclosure, the knowledge graph includes multiple subgraphs corresponding to multiple parts of speech. When using the knowledge graph to process M keywords, first determine the subgraph corresponding to the keywords according to the parts of speech of the M keywords. Then, the keywords are input into the subgraph corresponding to the part of speech to obtain Q first standard clause numbers corresponding to the M keywords and the clause content corresponding to the Q first standard clause numbers.

[0091] Alternatively, the knowledge graph can parallelly retrieve multiple keywords through knowledge graph subgraphs corresponding to multiple parts of speech to obtain Q first standard clauses.

[0092] According to an embodiment of the present disclosure, the knowledge graph can also only output the clause numbers of N first standard clauses corresponding to the M keywords, and the specific clause content can be obtained from the database according to the clause numbers of the first standard clauses.

[0093] In operation S322, duplicate removal processing is performed on the Q first standard clauses to obtain L first standard clauses and keyword data. The keyword data includes the number of keywords hit by each of the L first standard clauses.

[0094] According to an embodiment of the present disclosure, since one keyword can correspond to multiple first standard clauses, there may be duplicate matching of the same first standard clause between two or more keywords. Therefore, after determining Q first standard clauses according to the knowledge graph, deduplication processing is performed on the Q first standard clauses to obtain L first standard clauses and keyword data. The keyword data includes the number of keywords hit by each of the L first standard clauses.

[0095] Specifically, according to the clause numbers of the first standard clauses, multiple first standard clauses with the same clause number can be screened out from the Q first standard clauses, and then a first standard clause is randomly selected from the multiple first standard clauses with the same clause number, and the other first standard clauses with the same clause number are screened out to obtain the final L first standard clauses.

[0096] When randomly selecting a first standard clause from multiple first standard clauses with the same clause number and screening out the other first standard clauses with the same clause number, record the keywords corresponding to the first standard clauses with the same clause number and the number of keywords matched under this clause number to obtain the keyword data under this clause number. For the first standard clauses with different clause numbers, the recorded number of keywords can be 1.

[0097] For example, after performing word segmentation processing on the statement information, the obtained keywords are A, B, and C respectively. For keyword A, the clause numbers of the retrieved first standard clauses are 1.1.1 and 1.1.2; for keyword B, the clause numbers of the retrieved first standard clauses are 1.1.1 and 2.1.2; for keyword C, the clause numbers of the retrieved first standard clauses are 3.1 and 4.1, and a total of 6 first standard clauses are obtained. Since there are two first standard clauses with the same clause number, such as 1.1.1, after deduplication processing, 5 first standard clauses are obtained.

[0098] For the first standard clause with the clause number 1.1.1, the matched keywords are A and B, and the keyword data is 2; for the first standard clause with the clause number 1.1.2, the matched keyword is A, and the keyword data is 1; for the first standard clause with the clause number 2.1.2, the matched keyword is B, and the keyword data is 1; for the first standard clauses with the clause numbers 3.1 and 4.1, the matched keyword is C, and the keyword data is 1.

[0099] In operation S323, according to the importance level of the keywords and the keyword data, N first standard clauses are screened out from the L first standard clauses.

[0100] According to an embodiment of the present disclosure, after screening out L first standard clauses, for common keywords, such keywords will appear in multiple first standard clauses. Correspondingly, this will result in multiple first standard clauses with a low degree of relevance to the statement information being matched, affecting accuracy. Moreover, it will also lead to an excessive number of first standard clauses being matched, wasting computing resources.

[0101] According to an embodiment of the present disclosure, by combining the importance level of keywords and keyword data, screening the obtained L first standard clauses can avoid low matching accuracy caused by common keywords.

[0102] According to an embodiment of the present disclosure, a method for screening out N first standard clauses from L first standard clauses includes: sorting the L first standard clauses according to the number of keywords to obtain a first standard clause sequence. Then, according to the importance level of the keywords, update the order of the first standard clauses with the same number of keywords to obtain an updated first standard clause sequence. Screen out N first standard clauses from the updated first standard clause sequence.

[0103] Specifically, it can be sorted from high to low according to the number of keywords to obtain a first standard clause sequence; it can also be sorted from low to high according to the number of keywords. For first standard clauses with the same clause keyword data, they can be randomly sorted, or sorted according to the clause numbers of the first standard clauses.

[0104] After determining the first standard clause sequence according to the number of keywords, then sort the first standard clauses with the same number of keywords according to the importance level of the keywords to obtain an updated first standard clause sequence. Specifically, for the first standard clause sequence sorted from high to low according to the number of keywords, multiple first standard clauses with the same number of keywords are sorted from high to low according to the importance level of the keywords to obtain an updated first standard clause sequence. Similarly, for the first standard clause sequence sorted from low to high according to the number of keywords, the importance level of the keywords is also sorted from low to high.

[0105] According to an embodiment of the present disclosure, the importance level of keywords is determined in advance according to the clause content of the first standard clause. For example, when the keywords are "security", "network", "availability", and "reliability", the importance levels of "security" and "network" are higher than those of "availability" and "reliability".

[0106] Figure 4 A flowchart showing a method for determining a second standard clause according to an embodiment of the present disclosure is schematically illustrated.

[0107] As Figure 4 shown, the method of this embodiment includes operations S431 to S432, and can be a specific embodiment of operation S230.

[0108] In operation S431, determine the similarity between the i-th first standard clause among the N first standard clauses and the multiple second standard clauses.

[0109] In operation S432, use the second standard clauses whose similarity meets a preset threshold as the second standard clauses corresponding to the i-th first standard clause, obtaining P second standard clauses.

[0110] According to an embodiment of the present disclosure, after determining the N first standard clauses based on the M keywords, the P second standard clauses corresponding to the N first standard clauses can also be determined according to the association relationship between the first standard clauses and the second standard clauses. Each of the N first standard clauses corresponds to at least one second standard clause. The association relationship includes the similarity between the first standard clauses and the second standard clauses.

[0111] According to an embodiment of the present disclosure, before determining the P second standard clauses corresponding to the N first standard clauses according to the association relationship between the first standard clauses and the second standard clauses, calculate the similarity between each of the N first standard clauses and the second standard clauses.

[0112] After calculating the similarity between each first standard clause and the multiple second standard clauses, use the second standard clauses whose similarity meets the preset threshold as the second standard clauses corresponding to the i-th first standard clause. Specifically, there can be multiple second standard clauses that meet the preset threshold, P is greater than or equal to 1, and P is greater than or equal to N.

[0113] According to an embodiment of the present disclosure, the similarity includes the Pearson correlation coefficient and the Euclidean distance. The process of calculating the similarity between each first standard clause and the multiple second standard clauses includes: determining the Pearson correlation coefficient and the Euclidean distance between the i-th first standard clause and the multiple second standard clauses. Perform a weighted sum of the Pearson correlation coefficient and the Euclidean distance to obtain the similarity between the i-th first standard clause and the multiple second standard clauses.

[0114] Specifically, the similarity between the first standard clause and the second standard clause can be calculated by establishing a feature vector similarity model.

[0115] According to an embodiment of the present disclosure, the weights of the Pearson correlation coefficient and the Euclidean distance can be adjusted according to actual needs. Specifically, in the case of emphasizing the linear correlation degree between the first standard clause and the second standard clause, the weight of the Pearson correlation coefficient is higher than the weight of the Euclidean distance; in the case of emphasizing the text difference between the first standard clause and the second standard clause, the weight of the Euclidean distance is higher than the weight of the Pearson correlation coefficient.

[0116] For example, for the first standard clause X, feature extraction is performed, and the feature vector of the first standard clause X is X[1, 1, 1, 1, 1, 1, 1, 1]. Among them, X[1, 1, 1, 1, 1, 1, 1, 1] indicates that keywords 1, 2, 3, 4, 5, 6, 7, and 8 all appear in the first standard clause. The feature vector of the second standard clause Y is Y[1, 1, 0, 0, 0, 1, 1, 1], indicating that keywords 1, 2, 6, 7, and 8 appear in the second standard clause.

[0117] For the N-dimensional feature vector, the Pearson correlation coefficient between the first standard clause X and the second standard clause Y satisfies:

[0118]

[0119] Among them, represents the average value of the N-dimensional feature vector of the first standard clause, represents the average value of the N-dimensional feature vector of the second standard clause, X i represents the i-th dimension feature of the first standard clause, Y i represents the i-th dimension feature of the second standard clause, and P(X, Y) represents the Pearson correlation coefficient between the first standard clause and the second standard clause.

[0120] For the N-dimensional feature vector, the Euclidean distance between the first standard clause X and the second standard clause Y satisfies:

[0121]

[0122] Among them, D(X, Y) represents the Euclidean distance between the first standard clause and the second standard clause, X i and Y i respectively represent the i-th dimension features of the first standard clause and the second standard clause.

[0123] The similarity between the first standard clause and the second standard clause satisfies:

[0124] [[ID=3*]]similarity = W1 * P(X, Y) + W2 * D(X, Y) (3)

[0125] Among them, W1 and W2 respectively represent the weights of the Pearson correlation coefficient and the Euclidean distance.

[0126] Figure 5 Schematically shows a flowchart of a method for determining the similarity between a first standard clause and a second standard clause according to an embodiment of the present disclosure.

[0127] As Figure 5As shown, obtain the first standard clause 510 and the second standard clause 520 for which the similarity is to be calculated, and then use the vector feature model 540 to perform text word segmentation and feature extraction at 530. Perform word segmentation processing and feature extraction on the first standard clause 510 and the second standard clause 520 to obtain the feature vector 550 of the first standard clause and the feature vector 560 of the second standard clause. After obtaining the feature vector 550 of the first standard clause and the feature vector 560 of the second standard clause, calculate the Pearson correlation coefficient and Euclidean distance between the two feature vectors to obtain the similarity 570.

[0128] Figure 6 Schematically shows a flowchart of a method for presenting a first standard clause and a second standard clause according to an embodiment of the present disclosure.

[0129] As Figure 6 shown, the method of this embodiment includes operations S610 to S620.

[0130] In operation S610, display N first standard clauses in the order of the updated first standard clause sequence.

[0131] According to an embodiment of the present disclosure, after determining N first standard clauses and P second standard clauses, by displaying N first standard clauses in the order of the updated first standard clause sequence, visualization of the first standard clauses can be achieved.

[0132] Specifically, when displaying N first standard clauses, the clause numbers of the N first standard clauses can be displayed, or the clause content of the clause numbers of the N first standard clauses can be displayed simultaneously.

[0133] In operation S620, in response to an operation of a target user on the j-th first standard clause among the N first standard clauses, display the second standard clause corresponding to the j-th first standard clause.

[0134] According to an embodiment of the present disclosure, after displaying N first standard clauses that match the statement information, in response to an operation of a target user on the j-th first standard clause among the N first standard clauses, display one or more second standard clauses corresponding to the j-th first standard clause.

[0135] Specifically, after determining the first standard clauses, since the number of first standard clauses is too large, the second standard clauses corresponding to the first standard clauses can be hidden. After the target user generates an operation of clicking on the j-th first standard clause, display the second standard clause corresponding to the j-th first standard clause.

[0136] By presenting N first standard terms in the order of an updated first standard term sequence, the present disclosure can intuitively present the matching first standard terms to the target user and display them to the target user in the order of relevance, which helps to improve the user experience. In addition, in response to the operation of the target user, presenting the second standard terms corresponding to the first standard terms can simplify the display interface and improve the aesthetics of the interface; at the same time, it can also show the association relationship between the second standard terms and the first standard terms, which helps to improve the user experience and subsequent evaluation efficiency.

[0137] According to an embodiment of the present disclosure, P second standard terms can also be presented while presenting the N first standard terms; after presenting the N first standard terms, in response to a preset operation of the target user, P second standard terms can be presented.

[0138] Figure 7 Schematically shows a schematic diagram of a display interface according to a specific embodiment of the present disclosure.

[0139] As Figure 7 shown, the display interface 700 includes operation buttons 702-706, 709, and sub-display windows 701, 707, 708, 710-714. Among them, the sub-display window 701 is used to display the "LOGO" logo of the target user. The operation button 702 is used to obtain the first standard term and store the first standard term in response to a click operation of the user. Similarly, the operation button 703 is used to obtain the second standard term and store the second standard term in response to a click operation of the user.

[0140] The operation button 704 is used to calculate the similarity between the first standard term and the second standard term in response to a click operation of the user, and complete the alignment of the first standard term and the second standard term. The operation button 705 is used to separately perform word segmentation processing on the first standard term or the second standard term in response to a click operation of the user. Specifically, it can be implemented through a word segmentation model or the jieba package in Python. The operation button 706 is used to implement standard term recommendation after inputting information in response to a click operation of the user. Specifically, in response to a first preset operation of the target user, it can recommend the first standard term that matches the statement information; in response to a second preset operation of the target user, it can recommend the second standard term that matches the statement information; in response to a third preset operation of the target user, it can recommend the first standard term and the second standard term that match the statement information.

[0141] The sub-display window 707 is used to display the application name. Specifically, the application name can be the name of the evaluation assistance system implementing the above method. The sub-display window 708 is used to display the text information or keywords input by the user. The operation button 709 is used to process the input information in the sub-display window 708 in response to a click operation of the user.

[0142] The sub-display window 710 is used to display the clause content and / or clause number of the obtained first standard clause, as well as the storage location of the first standard clause. The sub-display window 711 is used to display the clause content and / or clause number of the obtained second standard clause, as well as the storage location of the second standard clause. The sub-display window 712 is used to display the word segmentation results of the word segmentation module 705. The sub-display window 713 is used to display the matching results of the standard clause matching module 704. Specifically, information such as specific similarity can be displayed. The sub-display window 714 is used to display the recommendation results of the standard clause recommendation module 706. Specifically, the determined N first standard clauses and P second standard clauses can be displayed.

[0143] Figure 8 A structural block diagram of a device for determining standard clauses according to an embodiment of the present disclosure is schematically shown.

[0144] As Figure 8 shown, the device 800 for determining standard clauses in this embodiment includes a first determination module 810, a second determination module 820, a third determination module 830, and a display module 840.

[0145] The first determination module 810 is configured to perform word segmentation processing on the statement information to be matched to obtain M keywords, where M is greater than or equal to 1, and the statement information includes text data for describing standard clauses. In one embodiment, the first determination module 810 can be used to perform the operation S210 described above, which will not be elaborated here.

[0146] The second determination module 820 is configured to determine N first standard clauses that match the M keywords based on the knowledge graph. The first standard clauses include institutional clauses for target users, where N is greater than or equal to 1. In one embodiment, the second determination module 820 can be used to perform the operation S220 described above, which will not be elaborated here.

[0147] The third determination module 830 is configured to determine at least one second standard clause corresponding to each of the N first standard clauses according to the association relationship between the first standard clauses and the second standard clauses, to obtain P second standard clauses, where P is greater than or equal to 1 and P is greater than or equal to N, and the second standard clauses include general standard clauses. In one embodiment, the third determination module 830 can be used to perform the operation S230 described above, which will not be elaborated here.

[0148] The display module 840 is configured to display the N first standard clauses and P second standard clauses that match the statement information. In one embodiment, the display module 840 can be used to perform the operation S240 described above, which will not be elaborated here.

[0149] According to an embodiment of the present disclosure, the second determination module 820 includes a first determination unit, a second determination unit, and a third determination unit.

[0150] The first determination unit is configured to process M keywords by using a knowledge graph to obtain Q first standard clauses that match the M keywords, where Q is greater than or equal to 1 and Q is greater than or equal to N. In one embodiment, the first determination unit may be configured to perform the operation S321 described above, which will not be elaborated here.

[0151] The second determination unit is configured to perform a duplicate removal process on the Q first standard clauses to obtain L first standard clauses and keyword data, where the keyword data includes the number of keywords hit by each of the L first standard clauses. Here, L is greater than or equal to N and less than or equal to Q. In one embodiment, the second determination unit may be configured to perform the operation S322 described above, which will not be elaborated here.

[0152] The third determination unit is configured to screen out N first standard clauses from the L first standard clauses according to the importance level of the keywords and the keyword data. In one embodiment, the third determination unit may be configured to perform the operation S323 described above, which will not be elaborated here.

[0153] According to an embodiment of the present disclosure, the third determination module 830 includes a fourth determination unit and a fifth determination unit.

[0154] The fourth determination unit is configured to determine the similarity between the i-th first standard clause among the N first standard clauses and multiple second standard clauses, where i is greater than or equal to 1 and i is less than or equal to N. In one embodiment, the fourth determination unit may be configured to perform the operation S431 described above, which will not be elaborated here.

[0155] The fifth determination unit is configured to use the second standard clauses whose similarity meets a preset threshold as the second standard clauses corresponding to the i-th first standard clause to obtain P second standard clauses. In one embodiment, the fifth determination unit may be configured to perform the operation S432 described above, which will not be elaborated here.

[0156] According to an embodiment of the present disclosure, the display module 840 includes a first display unit and a second display unit.

[0157] The first display unit is configured to display the N first standard clauses in the order of the updated first standard clause sequence. In one embodiment, the first display unit may be configured to perform the operation S641 described above, which will not be elaborated here.

[0158] The second display unit is configured to display a second standard clause corresponding to the j-th first standard clause in the N first standard clauses in response to an operation of a target user on the j-th first standard clause, where j is greater than or equal to 1 and j is less than or equal to N. In one embodiment, the second display unit may be configured to perform the operation S642 described above, which will not be elaborated herein.

[0159] Figure 9 Schematically shows a block diagram of an electronic device suitable for a method of determining standard clauses according to an embodiment of the present disclosure.

[0160] As Figure 9 shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage section 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general microprocessor (e.g., CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (e.g., an application specific integrated circuit (ASIC)), etc. The processor 901 may also include on-board memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0161] In the RAM 903, various programs and data required for the operation of the electronic device 900 are stored. The processor 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. The processor 901 performs various operations of the method flow according to an embodiment of the present disclosure by executing the program in the ROM 902 and / or the RAM 903. It should be noted that the program may also be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 may also perform various operations of the method flow according to an embodiment of the present disclosure by executing the program stored in the one or more memories.

[0162] According to an embodiment of the present disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, and the input / output (I / O) interface 905 is also connected to the bus 904. The electronic device 900 may further include one or more of the following components connected to the I / O interface 905: an input portion 906 including a keyboard, a mouse, etc.; an output portion 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage portion 908 including a hard disk, etc.; and a communication portion 909 including a network interface card such as a LAN card, a modem, etc. The communication portion 909 performs communication processing via a network such as the Internet. The drive 910 is also connected to the I / O interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed so that a computer program read therefrom can be installed into the storage portion 908 as needed.

[0163] The present disclosure also provides a computer-readable storage medium, which may be included in the device / device / system described in the above embodiments; or may exist separately without being assembled into the device / device / system. The above computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiments of the present disclosure is implemented.

[0164] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the above-described ROM 902 and / or RAM 903 and / or one or more memories other than ROM 902 and RAM 903.

[0165] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program includes program codes for executing the method shown in the flowchart. When the computer program product runs in a computer system, the program codes are used to cause the computer system to implement the method for determining standard terms provided by the embodiments of the present disclosure.

[0166] When the computer program is executed by the processor 901, the above functions defined in the system / apparatus of the embodiments of the present disclosure are executed. According to the embodiments of the present disclosure, the above-described systems, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0167] In one embodiment, the computer program can rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program can also be transmitted and distributed in the form of signals on a network medium, and be downloaded and installed through the communication part 909, and / or be installed from the removable medium 911. The program code included in the computer program can be transmitted by any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0168] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 909, and / or be installed from the removable medium 911. When the computer program is executed by the processor 901, the above functions defined in the system of the embodiments of the present disclosure are executed. According to the embodiments of the present disclosure, the above-described systems, devices, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0169] According to the embodiments of the present disclosure, the program code for executing the computer program provided by the embodiments of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedures and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include but are not limited to, such as Java, C++, python, the "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0170] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as combinations of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or by a combination of dedicated hardware and computer instructions.

[0171] Those skilled in the art will appreciate that the features recited in the various embodiments and / or claims of the present disclosure may be combined or combined in various ways, even if such combinations or combinations are not explicitly recited in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features recited in the various embodiments and / or claims of the present disclosure may be combined and combined in various ways. All such combinations and / or combinations fall within the scope of the present disclosure.

[0172] The specific embodiments described above further elaborate on the objectives, technical solutions, and beneficial effects of the present disclosure. It should be understood that the above description is only specific embodiments of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for determining standard terms, comprising: Performing word segmentation on the statement information to be matched to obtain M keywords, where M is greater than or equal to 1, and the statement information includes text data for describing standard terms; Based on the knowledge graph, determining N first standard terms that match the M keywords, where the first standard terms include institutional terms for target users, and N is greater than or equal to 1; According to the association relationship between the first standard terms and the second standard terms, determining at least one second standard term corresponding to each of the N first standard terms to obtain P second standard terms, where P is greater than or equal to 1 and P is greater than or equal to N, and the second standard terms include general standard terms, and the association relationship includes the similarity between the first standard terms and the second standard terms; Displaying the N first standard terms and the P second standard terms that match the statement information; The determining N first standard terms that match the M keywords based on the knowledge graph includes: Processing the M keywords using the knowledge graph to obtain Q first standard terms that match the M keywords, where Q is greater than or equal to 1 and Q is greater than or equal to N; Performing duplicate removal processing on the Q first standard terms to obtain L first standard terms and keyword data, where the keyword data includes the number of keywords hit by each of the L first standard terms, and L is greater than or equal to N and less than or equal to Q; According to the importance level of the keywords and the keyword data, screening out the N first standard terms from the L first standard terms; The determining at least one second standard term corresponding to each of the N first standard terms according to the association relationship between the first standard terms and the second standard terms to obtain P second standard terms includes: Determining the similarity between the i-th first standard term among the N first standard terms and multiple second standard terms, where i is greater than or equal to 1 and i is less than or equal to N; and Taking the second standard terms whose similarity meets the preset threshold as the second standard terms corresponding to the i-th first standard term to obtain P second standard terms.

2. The method according to claim 1, wherein, The screening out the N first standard terms from the L first standard terms according to the importance level of the keywords and the keyword data includes: Sorting the L first standard terms according to the number of keywords according to the keyword data to obtain a first standard term sequence; Updating the order of the first standard terms with the same number of keywords according to the importance level of the keywords to obtain an updated first standard term sequence; and Screening out N first standard terms from the updated first standard term sequence.

3. The method according to claim 1, wherein, The similarity includes Pearson correlation coefficient and Euclidean distance; Determining the similarity between the i-th first standard term among the N first standard terms and multiple second standard terms includes: Determining the Pearson correlation coefficient and the Euclidean distance between the i-th first standard term and multiple second standard terms; And Sum the Pearson correlation coefficient and the Euclidean distance with weights to obtain the similarity between the \(i\)-th first standard clause and multiple second standard clauses.

4. The method according to claim 2, wherein The displaying of the \(N\) first standard clauses and the \(P\) second standard clauses that match the statement information includes: Display the \(N\) first standard clauses in the order of the updated first standard clause sequence; and In response to an operation by the target user on the \(j\)-th first standard clause among the \(N\) first standard clauses, display the second standard clauses corresponding to the \(j\)-th first standard clause, where \(j\geq1\) and \(j\leq N\).

5. An apparatus for determining standard clauses, comprising: A first determination module, configured to perform word segmentation on the statement information to be matched to obtain \(M\) keywords, where \(M\geq1\), and the statement information includes text data for describing standard clauses; A second determination module, configured to determine, based on a knowledge graph, \(N\) first standard clauses that match the \(M\) keywords, where the first standard clauses include institutional clauses preset by a target user, and \(N\geq1\); A third determination module, configured to determine, according to the association relationship between the first standard clauses and the second standard clauses, at least one second standard clause corresponding to each of the \(N\) first standard clauses to obtain \(P\) second standard clauses, where \(P\geq1\) and \(P\geq N\), and the second standard clauses include general standard clauses, and the association relationship includes the similarity between the first standard clauses and the second standard clauses; and A display module, configured to display the \(N\) first standard clauses and the \(P\) second standard clauses that match the statement information; The second determination module is further configured to process the \(M\) keywords using the knowledge graph to obtain \(Q\) first standard clauses that match the \(M\) keywords, where \(Q\geq1\) and \(Q\geq N\); perform deduplication processing on the \(Q\) first standard clauses to obtain \(L\) first standard clauses and keyword data, where the keyword data includes the number of keywords hit by each of the \(L\) first standard clauses; and screen out the \(N\) first standard clauses from the \(L\) first standard clauses according to the importance level of the keywords and the keyword data; The third determination module is further configured to determine the similarity between the \(i\)-th first standard clause among the \(N\) first standard clauses and multiple second standard clauses, where \(i\geq1\) and \(i\leq N\); and use the second standard clauses whose similarity meets a preset threshold as the second standard clauses corresponding to the \(i\)-th first standard clause to obtain \(P\) second standard clauses.

6. An electronic device, comprising: One or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having executable instructions stored thereon, which when executed by a processor cause the processor to execute the method according to any one of claims 1 to 4.

8. A computer program product comprising a computer program, which when executed by a processor implements the method according to any one of claims 1 to 4.

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