CN113239177B - Knowledge point query method, device, server, medium and product - Google Patents
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Abstract
The embodiment of the application provides a knowledge point query method, a knowledge point query device, a knowledge point query server, a knowledge point query medium and a knowledge point query product, wherein the knowledge point query method comprises the steps of receiving a first query statement from a client, obtaining keywords from the first query statement, and obtaining target hot search words with the correlation degree with the keywords being greater than or equal to a first threshold value from a hot word library; obtaining a second knowledge point with the correlation degree with the query condition being greater than or equal to a second threshold value from a hot spot knowledge base by taking the target keyword or the keyword and the target hot search term as the query condition; because the keywords contained in the first query statement may be inaccurate, or the keywords contained in the first query statement are too few, the search result is inaccurate, and the target hot search word is used for replacing the keywords or the target hot search word and the keywords are used as the query condition for searching, so that the second knowledge points obtained from the hot point knowledge base can be more fit with the user intention, and the second knowledge points obtained from the hot point knowledge base are more accurate.
Description
Technical Field
The present application relates to the field of database technologies, and in particular, to a knowledge point query method, a device, a server, a medium, and a product.
Background
The knowledge points matched with the query sentences can be searched from the knowledge base based on the query sentences input by the user; at present, the knowledge points obtained by the user from the knowledge base are inaccurate.
Disclosure of Invention
In view of this, the present application provides a knowledge point query method, device, server, medium and product.
The application provides the following technical scheme:
according to a first aspect of an embodiment of the present disclosure, there is provided a knowledge point query method, including:
receiving a first query statement from a client;
obtaining keywords from the first query statement;
obtaining target hot search words with the correlation degree with the keywords being greater than or equal to a first threshold value from a hot word library, wherein the hot word library comprises a plurality of hot search words, the plurality of hot search words are obtained based on keywords contained in a second query statement input by a user with a target user account within a set time period, and the target user account has an association relationship with a user account logging in the client;
obtaining a first knowledge point with the correlation degree with the key words being greater than or equal to a second threshold value from an original knowledge base;
obtaining a second knowledge point with the correlation degree with the query condition being greater than or equal to a second threshold value from the hot spot knowledge base; the hot spot knowledge base comprises hit knowledge points corresponding to the second query statement containing the hot search word, the query condition comprises the key word and the target hot search word, or the query condition comprises the target hot search word;
And sending the links of the first knowledge points and the links of the second knowledge points to the client.
According to a second aspect of the embodiments of the present disclosure, there is provided a knowledge point query device, including:
the first receiving module is used for receiving a first query statement from the client;
the first acquisition module is used for acquiring keywords from the first query statement;
the second acquisition module is used for acquiring target hot search words with the correlation degree with the keywords being greater than or equal to a first threshold value from a hot spot word stock, the hot spot word stock comprises a plurality of hot search words, the plurality of hot search words are obtained based on keywords contained in a second query statement input by a user with a target user account in a set time period, and the target user account has an association relation with a user account logging in the client;
the third acquisition module is used for acquiring a first knowledge point with the correlation degree with the keyword being greater than or equal to a second threshold value from the original knowledge base;
a fourth obtaining module, configured to obtain, from the hotspot knowledge base, a second knowledge point having a correlation with a query condition greater than or equal to a second threshold; the hot spot knowledge base comprises hit knowledge points corresponding to the second query statement containing the hot search word, the query condition comprises the key word and the target hot search word, or the query condition comprises the target hot search word;
And the sending module is used for sending the links of the first knowledge points and the links of the second knowledge points to the client.
According to a third aspect of embodiments of the present disclosure, there is provided a server comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the knowledge point interrogation method as described in the first aspect.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer readable storage medium, which when executed by a processor of a server, causes the server to perform the knowledge point query method as described in the first aspect.
According to a fifth aspect of embodiments of the present disclosure, there is provided a computer program product directly loadable into an internal memory of a computer, for example a memory comprised by a server according to the third aspect, and comprising software code for enabling, after being loaded and executed via the computer, the knowledge point interrogation method according to the first aspect.
According to the technical scheme, in the knowledge point query method provided by the embodiment of the application, a first query sentence from a client is received, a keyword is obtained from the first query sentence, a target hot search word with the degree of correlation with the keyword being greater than or equal to a first threshold value is obtained from a hot word stock, the hot word stock comprises a plurality of hot search words, the plurality of hot search words are obtained based on the keyword contained in a second query sentence input by a user with a target user account in a set time period, and the target user account has an association relationship with a user account logging in the client; taking the keywords and the target hot search words as query conditions, or taking the target hot search words as query conditions, and obtaining second knowledge points with the correlation degree with the query conditions being greater than or equal to a second threshold value from a hot point knowledge base; because the user account number of the login client has an association relation with the target user account number, the queried same knowledge points corresponding to the target user account numbers are obtained, and the probability is that the knowledge points which need to be queried by the user corresponding to the user account number of the login client is high; and because the hot spot knowledge base comprises hit knowledge points corresponding to the second query statement containing the hot search word, the number of the contained knowledge points is small, so that the second knowledge points can be queried quickly and accurately. The situation that the search result is inaccurate due to the fact that the keywords contained in the first query statement are possibly inaccurate or the keywords contained in the first query statement are too few is avoided. It will be appreciated that the hotspot knowledge base may not include the second knowledge points, and to avoid this, the first knowledge points having a degree of relevance to the keyword greater than or equal to the second threshold may be obtained from the original knowledge base; and sending the links of the first knowledge points and the links of the second knowledge points to the client.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings may be obtained according to the provided drawings without inventive effort to a person skilled in the art.
Fig. 1 is a block diagram of a hardware architecture according to an embodiment of the present application;
FIG. 2 is a flowchart of a knowledge point query method according to an embodiment of the present application;
FIG. 3 is a block diagram of a knowledge point query device according to an embodiment of the present application;
fig. 4 is a block diagram illustrating an apparatus for a server according to an exemplary embodiment.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The embodiment of the application provides a knowledge point query method, a knowledge point query device, a knowledge point query server, a knowledge point query medium and a knowledge point query product.
First, a description is given of a related art related to an embodiment of the present application.
In the related art, the knowledge base includes a plurality of knowledge points. Illustratively, the plurality of knowledge points are obtained by splitting the source document. The source documents corresponding to different knowledge points may be the same or may be different.
For example, the source document includes 10 paragraphs, and illustratively, the source text is split into 10 knowledge points, one for each paragraph; illustratively, the source document is split into 5 knowledge points, one knowledge point comprising one or more paragraphs in the source document; illustratively, the source document is split into 20 knowledge points, one knowledge point comprising one or more sentences in a paragraph.
Illustratively, the knowledge points include source documents; illustratively, the knowledge points are derived based on other knowledge points.
Illustratively, knowledge points are represented in a variety of ways, and embodiments of the present application provide, but are not limited to: any one of a linked list, an array, a structure, and a table. The structure of knowledge points is illustrated below using a table as an example.
Illustratively, the structures of knowledge points in the related art are shown in table 1.
TABLE 1 Structure of knowledge points in related Art
Illustratively, the knowledge body includes any one of a knowledge title and knowledge content; illustratively, the knowledge content corresponds to a knowledge title, for example, the knowledge content corresponding to the knowledge title "the deposit term of the deposit is notified by the ag rich person" may be: the individual notice deposit is divided into two varieties, 1 day notice deposit and 7 days notice deposit according to the period length of the depositor notice in advance, no matter how long the actual deposit is. 1 day informing deposit must be advanced by 1 day informing contract to pay deposit, and 7 days informing deposit must be advanced by 7 days informing contract to pay deposit. 1 day notification deposit and 7 days notification deposit, the customer must go to the counter reservation check-in 1 day in advance or 7 days in advance. The transfer is a business where the system can automatically transfer the home to the customer's living account on the expiration date, but the customer is required to transact the contracted transfer at the counter every cycle (7 days).
Illustratively, the service directory included in the knowledge point may include one or more levels of directory, and the service directory including two levels of directory is illustrated in table 1.
Illustratively, the service directory included in the knowledge point is used to indicate a storage path of the knowledge body included in the knowledge point. Illustratively, the service directory included in the knowledge point is the name of the storage device and/or the name of the folder storing the knowledge point.
Illustratively, knowledge titles may be derived from knowledge content based on natural language processing techniques; illustratively, the knowledge title may be obtained from a source document.
Illustratively, the map labels contained in the knowledge points refer to attribute information of the knowledge text. Exemplary, the atlas tag comprises: products (products described by the content of the knowledge body), the affiliated banks (which branches or headquarters the content of the knowledge body is directed to), the customer type. Exemplary client types include, but are not limited to: personal customers, financial management customers, general customers, private banking customers, mid-banking customers, etc.
Illustratively, the personality label included in the knowledge point is added by the artificial agent, the artificial agent may label the knowledge point based on its own understanding of the knowledge point, for example, the artificial agent having the identifier a of the artificial agent in table 1 is labeled with "rich periodic deposit", and the next artificial agent having the identifier a of the artificial agent may accurately search for the knowledge point shown in table 1 based on the query statement "rich periodic deposit".
It should be noted that, sometimes, the human agent queries the required knowledge point when querying, but the human agent has some own understanding to the knowledge point or has own naming habit to the knowledge point, so the human agent can manually add the own understanding to the personality label of the knowledge point. Therefore, the thinking habits of different manual agents can be taken care of, and the labels of the knowledge are enriched, so that the knowledge query efficiency is improved, and the knowledge query accuracy is improved.
For example, for the same knowledge point, the personality tags of different manual agents may be different and may be the same; because the artificial agent needs to log in before searching the knowledge points, the query statement of the artificial agent comprises the identification of the artificial agent, and therefore, the influence of the individual labels marked by other artificial agents can not be caused in the process of searching the knowledge points through the individual labels.
Illustratively, the management attribute included in the knowledge point refers to information of an administrator that manages the knowledge point, and for example, the management attribute includes a department to which the administrator belongs and a user group to which the administrator belongs.
The structure of the knowledge points in table 1 is only an example and is not limited to the structure of the knowledge points, for example, the knowledge points may include: one or more fields in a business catalog, knowledge body, atlas tag, personality tag, and management attribute.
Illustratively, the knowledge point further includes: keywords of the knowledge body.
Illustratively, the knowledge point further comprises a receiving group comprising an identification of the user from which the knowledge point can be queried.
By way of example, keywords in a query statement may include keywords belonging to one or more fields of a business catalog, knowledge body, atlas tag, personality tag, management attribute. In the process of retrieving the knowledge points with the relevance of the query statement being greater than or equal to the first threshold value from the knowledge base, the relevance of one or more of a business catalog, a knowledge text, a map label, a personality label and a management attribute contained in the query statement and the knowledge points can be obtained, so that the knowledge points with the relevance of the query statement being greater than or equal to the first threshold value can be obtained, and links with the knowledge points with the relevance of the query statement being greater than or equal to the first threshold value can be displayed.
In the related art, in the process of retrieving knowledge points with the relevance of the query statement being greater than or equal to the first threshold, keywords contained in the first query statement input by the user may not be comprehensive or may not be accurate, so that the obtained query result is inaccurate.
For example, the keywords included in the first query sentence are keyword a and keyword B, and it is assumed that knowledge points to be searched by the user include keyword a, keyword B and keyword C, and because the keywords included in the first query sentence are not comprehensive, the query result is relatively wide, for example, knowledge points including keyword a and keyword B and not including keyword C may exist in the query result, so that the user cannot quickly and accurately find the required knowledge points from the query result.
For example, the keywords included in the first query sentence are a keyword a and a keyword B, and it is assumed that the knowledge points that the user needs to search include the keyword a and the keyword B' and do not include the keyword B; the keyword B and the keyword B' are different but have high relevance, and knowledge points required by the user may not be obtained through the first query sentence.
Next, a hardware architecture according to an embodiment of the present application will be described.
As shown in fig. 1, the architecture diagram of the hardware architecture according to the embodiment of the present application includes: electronic device 11, server 12, hotspot word library 13, original knowledge base 14, and hotspot knowledge base 15.
By way of example, the electronic device 11 may be any electronic product that can interact with a user by one or more of a keyboard, a touchpad, a touch screen, a remote control, a voice interaction, a handwriting device, etc., such as a mobile phone, a notebook computer, a tablet computer, a palm top computer, a personal computer, a wearable device, a smart television, a PAD, etc.
The server 12 may be a server, a server cluster comprising a plurality of servers, or a cloud computing server center, for example. The server 12 may include a processor, memory, a network interface, and the like.
It should be noted that fig. 1 is only an example, and the types of electronic devices may be various, and are not limited to the computer in fig. 1.
The electronic device 11 may illustratively establish a connection and communicate with the server 12 over a wireless network or a wired network.
Illustratively, the hotspot word repository 13, the original knowledge repository 14 and the hotspot knowledge repository 15 may establish a connection and communicate with the server 12 via a wireless network or a wired network.
Illustratively, the user may input a first query statement via the electronic device 11. The electronic device 11 may send the first query statement to the server 12. The electronic device 11 may display the query result fed back by the server 12.
The user may be an artificial agent or customer, for example.
Illustratively, the user may input the first query statement through a user interface of a client, which may be an application client or a web page client, presented by the electronic device 11.
The server 12 is configured to perform the knowledge point query method provided in the embodiments of the present application, and interact with the hotspot word library 13, the original knowledge base 14, and the hotspot knowledge base 15.
Illustratively, the original knowledge base 14 includes all knowledge points.
Illustratively, the hotspots word library 13 includes hotsearches.
Illustratively, the hot-spot knowledge base 15 includes hit knowledge points corresponding to query statements containing hot-search terms.
Illustratively, the hotspots word repository 13, the original knowledge repository 14, and the hotspots knowledge repository 15 may be located in the server 12, or the hotspots word repository 13, the original knowledge repository 14, and the hotspots knowledge repository 15 may be independent of the server 12.
Illustratively, the hot word repository 13 and the hot knowledge repository 15 may be the same knowledge repository or different knowledge repositories.
Those skilled in the art will appreciate that the above-described electronic devices and servers are merely examples, and that other existing or future-occurring electronic devices or servers, as applicable to the present disclosure, are also included within the scope of the present application and are hereby incorporated by reference herein.
The knowledge point query method provided in the embodiments of the present application is described below with reference to a hardware architecture and related technologies.
As shown in fig. 2, a flowchart of a knowledge point query method according to an embodiment of the present application may be applied to the server shown in fig. 1, where the method includes steps S21 to S26 in the implementation process.
Step S21: a first query statement is received from a client.
Illustratively, the query term currently to be queried is referred to as a first query term, and the query term that has been queried is referred to as a second query term.
Step S22: and obtaining keywords from the first query statement.
Illustratively, the number of keywords obtained from the first query statement is one or more.
In an alternative embodiment, the first query term may be speech or text. If the first query term is speech, the speech needs to be converted into text.
Optionally, the embodiment of the present invention provides, but is not limited to, the following method for obtaining the keywords included in the first query sentence.
The first method for obtaining the keywords contained in the first query statement comprises the following steps:
step A1: the first query statement is partitioned to obtain a plurality of vocabularies.
Optionally, if the query statement is "loan contract for purchasing houses by clients", the query statement includes the following words: customer, house purchase, loan contract.
Step A2: and obtaining keywords from the plurality of words according to a preset rule.
Optionally, the preset rule may include: and (3) removing the vocabulary belonging to the stop word from the plurality of vocabularies obtained in the step A1. Assume that the stop words include: is obtained, is not obtained, is in bar, is in middle, and the like. Then, the keywords obtained by step A2 include: customer, house purchase, loan contract.
The second method for obtaining the keywords contained in the first query statement comprises the following steps: keyword extraction method based on statistical characteristics.
The keyword extraction algorithm based on the statistical features extracts keywords of the query sentence by using the statistical information of the words in the first query sentence.
The third method for obtaining the keywords contained in the first query sentence comprises the following steps: keyword extraction algorithms based on word graph models, such as TextRank algorithm.
The keyword extraction algorithm based on the word graph model firstly builds a language network graph of the first query sentence, then analyzes the language network graph, and searches words or phrases with important functions on the language network graph, wherein the phrases are keywords of the first query sentence.
The fourth method for obtaining the keywords contained in the first query sentence comprises the following steps: keyword extraction algorithms based on topic models, such as LDA algorithms.
The keyword extraction algorithm based on the topic model mainly utilizes the property of topic distribution in the topic model to extract keywords.
Step S23: and obtaining target hot search words with the correlation degree with the keywords being greater than or equal to a first threshold value from a hot word library.
The first threshold may be based on practical situations, and will not be described here in detail.
The hot spot word library comprises a plurality of hot search words, the plurality of hot search words are obtained based on keywords contained in a second query sentence input by a user with a target user account in a set time period, and the target user account has an association relationship with a user account logging in the client.
Illustratively, the number of target hotsearch words is one or more.
Illustratively, the ending time of the set time period is the current time, and the starting time is the current time+the set time period; the ending time and the starting time of the set period of time are constantly changing as time passes.
The number of target user accounts with association relation with the user account logging in the client is multiple; the above association relationship means that the network used by the client logged in by the target user account and the network used by the client logged in by the user account are the same wireless local area network; illustratively, the above association relationship means that the target user account and the user account have the same suffix, for example @ mmmm.com; for example, the above-mentioned association relationship is preset, that is, the above-mentioned association relationship means that the target user account and the user account belong to the same account set.
The plurality of user accounts with the association relationship refer to user accounts applied by the same enterprise.
Step S24: and obtaining a first knowledge point with the correlation degree with the key words being greater than or equal to a second threshold value from the original knowledge base.
Illustratively, knowledge points obtained from the original knowledge base and having a degree of correlation with the keyword greater than or equal to a second threshold value are referred to as first knowledge points. Illustratively, the number of first knowledge points derived from the original knowledge base may be one or more.
Illustratively, the original knowledge base includes all knowledge points.
The second threshold may be based on the actual situation, and will not be described here.
Step S25: obtaining a second knowledge point with the correlation degree with the query condition being greater than or equal to a second threshold value from the hot spot knowledge base; the hot spot knowledge base comprises hit knowledge points corresponding to the second query statement containing the hot search word, the query condition comprises the keyword and the target hot search word, or the query condition comprises the target hot search word.
Illustratively, the knowledge points obtained from the hot-spot knowledge base and having the degree of correlation with the keyword greater than or equal to the second threshold value are referred to as second knowledge points. Illustratively, the number of second knowledge points derived from the hotspot knowledge base may be one or more.
For example, because the user account number of the login client has an association relationship with the target user account number, the queried same knowledge points corresponding to the target user account numbers respectively have a high probability that the user corresponding to the user account number of the login client needs to query the knowledge points; and because the hot spot knowledge base comprises hit knowledge points corresponding to the second query statement containing the hot search word, the number of the contained knowledge points is small, so that the second knowledge points can be queried quickly and accurately.
If the first query sentence contains insufficient keywords, after the target hot search word is supplemented, the range of the obtained second knowledge points can be reduced, and the accuracy of the second knowledge points obtained by searching is improved. If the keywords contained in the first query sentence are not matched, the keywords can be replaced by target hot search words, and the accuracy of the second knowledge points obtained by searching is improved because the target hot search words are accurate (because knowledge points are obtained based on the target hot search words).
Step S26: and sending the links of the first knowledge points and the links of the second knowledge points to the client.
In the knowledge point query method provided by the embodiment of the application, a first query sentence from a client is received, a keyword is obtained from the first query sentence, a target hot search word with the degree of correlation with the keyword being greater than or equal to a first threshold value is obtained from a hot word bank, the hot word bank comprises a plurality of hot search words, the plurality of hot search words are obtained based on the keyword contained in a second query sentence input by a user with a target user account in a set time period, and the target user account has an association relationship with a user account logging in the client; taking the keywords and the target hot search words as query conditions, or taking the target hot search words as query conditions, and obtaining second knowledge points with the correlation degree with the query conditions being greater than or equal to a second threshold value from a hot point knowledge base; because the user account number of the login client has an association relation with the target user account number, the queried same knowledge points corresponding to the target user account numbers are obtained, and the probability is that the knowledge points which need to be queried by the user corresponding to the user account number of the login client is high; and because the hot spot knowledge base comprises hit knowledge points corresponding to the second query statement containing the hot search word, the number of the contained knowledge points is small, so that the second knowledge points can be queried quickly and accurately. The situation that the search result is inaccurate due to the fact that the keywords contained in the first query statement are possibly inaccurate or the keywords contained in the first query statement are too few is avoided. It will be appreciated that the hotspot knowledge base may not include the second knowledge points, and to avoid this, the first knowledge points having a degree of relevance to the keyword greater than or equal to the second threshold may be obtained from the original knowledge base; and sending the links of the first knowledge points and the links of the second knowledge points to the client.
In an alternative implementation, the process of obtaining the hot search word included in the hot word stock includes the following steps B1 to B3.
Step B1: and acquiring a plurality of target user accounts which have an association relationship with the user account logging in the client.
Step B2: and for each target user account, acquiring the second query statement input by the user with the target user account in a preset time period.
For example, the association relationship of the users can be constructed according to the organization architecture of the enterprise. Illustratively, users belonging to the same enterprise have an association relationship, and illustratively, users belonging to the same department of the same enterprise have an association relationship.
By way of example, information such as mails, instant messaging, access histories, search histories and the like sent by a user in daily work can be collected, enterprise information and department information of the user can be extracted, and a hot spot word stock can be established.
Step B3: and determining keywords with the occurrence frequency greater than or equal to a third threshold value in second query sentences corresponding to the target user accounts as the hot search words.
Illustratively, the frequency of occurrence of a keyword = the number of second query terms containing the keyword/the total number of second query terms.
In an alternative implementation, the process of obtaining hit knowledge points included in the hotspot knowledge base includes the following steps C1 to C2.
Step C1: and obtaining hit knowledge points corresponding to the second query sentences containing the hot search words from hit knowledge points of the second query sentences corresponding to the target user accounts respectively.
Illustratively, the step of obtaining hit knowledge points of the second query statement includes: acquiring a query result corresponding to the second query statement, wherein the query result comprises links of one or more knowledge points; and determining the knowledge points of the clicked links or the knowledge points with browsing time length longer than or equal to the preset time length in the query result as the hit knowledge points.
For example, the second query term is an accumulation fund application flow, and if the query result includes: the method comprises the steps of introducing a housing deposit loan, linking the housing deposit loan application flow, linking the housing deposit loan guarantee, linking housing deposit loan approval, linking housing deposit loan release and linking housing deposit loan inquiry, wherein if a user clicks the link of the housing deposit application flow, the knowledge point 'housing deposit application flow' is a hit knowledge point, and if the user does not click the link of other knowledge points, the other knowledge points are not hit knowledge points.
For example, for the same second query term, the number of hit knowledge points in the query result corresponding to the second query term may be one or more.
Step C2: and storing hit results corresponding to the second query statement containing the hot search word into the hot spot knowledge base.
In an alternative implementation, the implementation of step S26 includes the following steps D1 to D2.
Step D1: and sequencing the first knowledge points and the second knowledge points based on the correlation degree of the first knowledge points and the keywords and the correlation degree of the second knowledge points and the keywords and the target keywords to obtain a sequencing result.
Step D2: and sending the sequencing result to the client.
Illustratively, the number of first knowledge points derived from the original knowledge base may be one or more; the number of second knowledge points derived from the hotspot knowledge base may be one or more.
The first knowledge point from the original knowledge base and the second knowledge point from the hot knowledge base may have the same knowledge point or the first knowledge point from the original knowledge base and the second knowledge point from the hot knowledge base may be completely different.
If the first knowledge point obtained from the original knowledge base and the second knowledge point obtained from the hot knowledge base have the same knowledge point, and the same knowledge point is the target knowledge point, and supposing that the correlation degree between the target knowledge point and the keyword is the first correlation degree and the correlation degree between the target knowledge point and the query condition is the second correlation degree, then the final correlation degree=max { first correlation degree, second correlation degree }, or the final correlation degree=min { first correlation degree, second correlation degree }, or the final correlation degree=first correlation degree, or the final correlation degree=second correlation degree, or the final correlation degree=1×first correlation degree+2×second correlation degree, of the target knowledge point; the weight 1 and the weight 2 may be set in advance.
If the first knowledge point obtained from the original knowledge base and the second knowledge point obtained from the hot-spot knowledge base have different knowledge points, for the knowledge points which are contained in the original knowledge base but not in the hot-spot knowledge base, the final correlation degree of the knowledge points is the correlation degree of the knowledge points and the keywords; for the knowledge points contained in the hot knowledge base but not in the original knowledge base, the final relevance of the knowledge points is the relevance of the knowledge points and the query condition.
When the ranking is performed in the step D1, the ranking is performed based on the final relevance of each knowledge point.
Knowledge points are described below.
In an alternative implementation, the knowledge point includes a knowledge body, a location of the knowledge body in the source document, a first identifier, and a second identifier; the first identifier is an identifier of a knowledge point corresponding to a previous knowledge text positioned in the knowledge text in the source document; the second identifier is an identifier of a knowledge point corresponding to a next knowledge text positioned in the knowledge text in the source document; the source document is split into a plurality of knowledge bodies.
The structure of knowledge points in the embodiment of the present application is different from that in the related art.
Illustratively, knowledge points are represented in a variety of ways, and embodiments of the present application provide, but are not limited to: any one of a linked list, an array, a structure, and a table. The structure of knowledge points is illustrated below using a table as an example. Illustratively, the structures of the knowledge points in the embodiments of the present application are shown in table 2.
TABLE 2 Structure of knowledge points in embodiments of the present application
Illustratively, the knowledge points are identified, for example, by a plurality of ways, for example, by one or more of letters, numbers, or special symbols, and the first and second identifications are illustrated in table 2 by numerical representations.
For example, the identities of the knowledge points may be randomly assigned, with the identities of the different knowledge points being different; illustratively, the identification of the knowledge point is related to the location of the knowledge body contained by the knowledge point in the source document.
For example, the 3 rd paragraph, the 4 th paragraph and the 5 th paragraph contained in the source document correspond to one knowledge point respectively, and the knowledge point shown in table 2 corresponds to the 4 th paragraph contained in the source document, so "at the source document position" is 4; illustratively, the source document context index includes a first identifier, as in Table 2, 3, and a second identifier, as in Table 5.
For example, the knowledge point may include one or more first identifications. If the knowledge point includes a first identifier, the first identifier is, for example, an identifier of a knowledge point corresponding to any one of the previous knowledge texts in the knowledge text included in the knowledge point in the source document. Taking a case that each section included in the source document corresponds to one knowledge point as an example, if the knowledge point a corresponds to the 1 st section of the source document, the knowledge point B corresponds to the 2 nd section of the source document, the knowledge point C corresponds to the 3 rd section of the source document, the knowledge point D corresponds to the 4 th section of the source document, and the knowledge point E corresponds to the 5 th section of the source document, the first identifier included in the knowledge point C may be the identifier of the knowledge point B or the identifier of the knowledge point a. Illustratively, the first identifier is an identifier of a knowledge point corresponding to an adjacent previous knowledge body located in the knowledge body contained in the knowledge point in the source document. For example, the first identity contained by knowledge point C is the identity of knowledge point B.
If the knowledge point E comprises a plurality of first identifications, knowledge texts contained in the knowledge point with the plurality of first identifications are adjacent to the source document and adjacent to the knowledge texts contained in the knowledge point E; for example, the knowledge point E includes a plurality of first identifiers respectively: identification of a knowledge point D and identification of a knowledge point C.
If the knowledge point E includes a plurality of first identifiers, knowledge texts included in the knowledge point with the plurality of first identifiers may not be adjacent to each other at the location of the source document, for example, the plurality of first identifiers included in the knowledge point E are respectively: identification of knowledge point C and identification of knowledge point a.
Illustratively, the knowledge point may include one or more second identifiers. For the second identifier, reference may be made to the first identifier, which is not described herein.
Illustratively, the knowledge point further includes: at least one of an identification ID of the source document and a source document name.
In summary, the knowledge points provided in the embodiments of the present application include the context index of the source document (i.e., the first knowledge point and the second knowledge point), so that multiple knowledge points derived from the same source document have an association relationship.
In an alternative implementation manner, if the user obtains the query result based on the query statement in the query process, the client can display the link of the knowledge points contained in the query result; the method comprises the steps that a server responds to the operation of clicking a link of a certain knowledge point to obtain a first identifier and a second identifier contained in the knowledge point; sending a knowledge text contained in the knowledge point, a knowledge text contained in the knowledge point with the first identifier, and a knowledge text contained in the knowledge point with the second identifier to the client; the client side displays the knowledge text contained in the knowledge point and the knowledge text contained in the knowledge point with the first identifier and the knowledge text contained in the knowledge point with the second identifier when displaying, so that if the user also has the requirement of checking the previous knowledge text or the next knowledge text of the knowledge text contained in the knowledge point, the user does not need to search again, the search times of the user are reduced, the search complexity is reduced, and the search speed of the user is higher.
Illustratively, a document is generated based on the knowledge body contained in the knowledge point, the knowledge body contained in the knowledge point with the first identifier, and the knowledge body contained in the knowledge point with the second identifier, and the document is sent to the client.
Knowledge texts contained in the knowledge points, knowledge texts contained in the knowledge points with the first identifier and knowledge texts contained in the knowledge points with the second identifier are all derived from the same source document, and as an example, different knowledge texts belonging to the same source document may have a logic sequence association, wherein the logic sequence association is an order in which a user browses the knowledge texts. For example, 6 knowledge points are obtained by splitting from the source document, wherein the positions of knowledge texts contained in the 6 knowledge points in the source document are sequentially as follows: knowledge 1, knowledge point 2, knowledge point 3, knowledge point 4, knowledge point 5, knowledge point 6; knowledge 1, knowledge point 2, knowledge point 3, knowledge point 4, knowledge point 5, knowledge point 6 respectively contain knowledge texts which are in turn: introduction of house deposit loans, application flow of house deposit loans, guarantee of house deposit loans, approval of house deposit loans, release of house deposit loans and inquiry of house deposit loans. I.e. the knowledge bodies contained by the 6 knowledge points are semantically related.
Illustratively, the knowledge texts of the knowledge points in the document containing the knowledge text of the knowledge points, the knowledge text of the knowledge points with the first identifier, and the knowledge text of the knowledge points with the second identifier are ordered in a logical order.
In an alternative implementation, the knowledge points further comprise an association identifier that associates the knowledge points. The number of associated identities of associated knowledge points that the knowledge points contain may be one or more.
In this embodiment, for any knowledge point, a knowledge point that has a higher degree of correlation with the knowledge point and that includes a knowledge body and that includes the knowledge point that does not belong to the same source document is referred to as a correlation knowledge point. The step of obtaining the first association identifier of the association knowledge point specifically comprises the following steps: and for each knowledge point, obtaining an associated knowledge point with the degree of correlation with the knowledge point being greater than or equal to a fourth threshold value, wherein a knowledge text contained in the associated knowledge point and a knowledge text contained in the knowledge point belong to different source documents. Wherein the knowledge points include associated identifications of the associated knowledge points.
The fourth threshold may be based on actual conditions, for example, and is not limited herein.
The method is described in detail in the embodiments disclosed in the application, and the method can be implemented by using various devices, so that the application also discloses a device, and a specific embodiment is given in the following detailed description.
As shown in fig. 3, the structure diagram of the knowledge point query device provided in the embodiment of the present application may include: a first receiving module 31, a first acquiring module 32, a second acquiring module 33, a third acquiring module 34, a fourth acquiring module 35 and a transmitting module 36, wherein:
a first receiving module 31, configured to receive a first query statement from a client;
a first obtaining module 32, configured to obtain a keyword from the first query sentence;
a second obtaining module 33, configured to obtain, from a hot word library, a target hot search word having a relevance greater than or equal to a first threshold with respect to the keyword, where the hot word library includes a plurality of hot search words, where the plurality of hot search words are obtained based on keywords included in a second query sentence input by a user having a target user account in a set period of time, and the target user account has an association relationship with a user account logged in the client;
a third obtaining module 34, configured to obtain, from an original knowledge base, a first knowledge point having a degree of correlation with the keyword that is greater than or equal to a second threshold;
A fourth obtaining module 35, configured to obtain, from the hotspot knowledge base, a second knowledge point having a correlation with the query condition greater than or equal to a second threshold; the hot spot knowledge base comprises hit knowledge points corresponding to the second query statement containing the hot search word, the query condition comprises the key word and the target hot search word, or the query condition comprises the target hot search word;
a sending module 36, configured to send the link of the first knowledge point and the link of the second knowledge point to the client.
In an alternative implementation, the method further includes:
a fourth obtaining module, configured to obtain a plurality of target user accounts having an association relationship with a user account logging in the client;
a fifth obtaining module, configured to obtain, for each target user account, the second query sentence input by the user having the target user account in a preset time period;
and the first determining module is used for determining keywords with the occurrence frequency larger than or equal to a third threshold value in the second query sentences corresponding to the target user accounts as the hot search words.
In an alternative implementation, the method further includes:
A sixth obtaining module, configured to obtain hit knowledge points corresponding to a second query sentence including a hot search word from hit knowledge points of second query sentences corresponding to a plurality of target user accounts respectively;
and the storage module is used for storing hit results corresponding to the second query statement containing the hot search word into the hot spot knowledge base.
In an alternative implementation, the method further includes:
a sixth obtaining module, configured to obtain hit knowledge points corresponding to a second query sentence including a hot search word from hit knowledge points of second query sentences corresponding to a plurality of target user accounts respectively;
and the storage module is used for storing hit results corresponding to the second query statement containing the hot search word into the hot spot knowledge base.
In an alternative implementation, the method further includes:
a seventh obtaining module, configured to obtain a query result corresponding to the second query statement, where the query result includes links of one or more knowledge points;
and the second determining module is used for determining the knowledge points of the clicked links or the knowledge points with browsing time length longer than or equal to the preset time length in the query result as the hit knowledge points.
In an alternative implementation, the sending module includes:
The ranking unit is used for ranking the first knowledge points and the second knowledge points based on the correlation degree of the first knowledge points and the keywords and the correlation degree of the second knowledge points and the query conditions to obtain ranking results;
and the sending unit is used for sending the sequencing result to the client.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 4 is a block diagram illustrating an apparatus for a server according to an exemplary embodiment.
Servers include, but are not limited to: a processor 41, a memory 42, a network interface 43, an I/O controller 44, and a communication bus 45.
It should be noted that the structure of the server shown in fig. 4 is not limited to the server, and the server may include more or less components than those shown in fig. 4, or may combine some components, or may be arranged with different components, as will be understood by those skilled in the art.
The following describes the respective constituent elements of the server in detail with reference to fig. 4:
the processor 41 is a control center of the server, connects respective portions of the entire server using various interfaces and lines, and performs various functions of the server and processes data by running or executing software programs and/or modules stored in the memory 42, and calling data stored in the memory 42, thereby performing overall monitoring of the server. Processor 41 may include one or more processing units; by way of example, processor 41 may integrate an application processor that primarily handles operating systems, user interfaces, applications, etc., with a modem processor that primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 41.
Processor 41 may be a central processing unit (CentralProcessing Unit, CPU), or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention, etc.;
the Memory 42 may include a Memory such as a Random-Access Memory (RAM) 421 and a Read-Only Memory (ROM) 422, and may further include a mass storage device 423, such as at least 1 disk Memory, and the like. Of course, the server may also include hardware required for other services.
The memory 42 is used for storing instructions executable by the processor 41. The processor 41 has the following functions: receiving a first query statement from a client;
obtaining keywords from the first query statement;
obtaining target hot search words with the correlation degree with the keywords being greater than or equal to a first threshold value from a hot word library, wherein the hot word library comprises a plurality of hot search words, the plurality of hot search words are obtained based on keywords contained in a second query statement input by a user with a target user account within a set time period, and the target user account has an association relationship with a user account logging in the client;
Obtaining a first knowledge point with the correlation degree with the key words being greater than or equal to a second threshold value from an original knowledge base;
obtaining a second knowledge point with the correlation degree with the query condition being greater than or equal to a second threshold value from the hot spot knowledge base; the hot spot knowledge base comprises hit knowledge points corresponding to the second query statement containing the hot search word, the query condition comprises the key word and the target hot search word, or the query condition comprises the target hot search word;
and sending the links of the first knowledge points and the links of the second knowledge points to the client.
The processor 41, memory 42, network interface 43, and I/O controller 44 may be interconnected by a communication bus 45, which may be an ISA (Industry Standard Architecture ) bus, PCI (PeripheralComponent Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc.
In an exemplary embodiment, the server may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for performing the above-described knowledge point interrogation method.
In an exemplary embodiment, the disclosed embodiments provide a storage medium including instructions, such as a memory 42 including instructions, executable by a processor 41 of a server to perform the above-described method. Alternatively, the storage medium may be a non-transitory computer readable storage medium, which may be, for example, ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.
In an exemplary embodiment, a computer readable storage medium is also provided, which can be directly loaded into an internal memory of a computer, such as the memory 42 described above, and contains software code, and the computer program can implement the steps shown in any embodiment of the knowledge point query method described above after being loaded and executed by the computer.
In an exemplary embodiment, a computer program product is also provided, which can be directly loaded into an internal memory of a computer, for example, a memory contained in the server, and contains software codes, and the computer program can implement the steps shown in any embodiment of the knowledge point query method after being loaded and executed by the computer.
The features described in the respective embodiments in the present specification may be replaced with each other or combined with each other. For device or system class embodiments, the description is relatively simple as it is substantially similar to method embodiments, with reference to the description of method embodiments in part.
It is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A knowledge point querying method, comprising:
receiving a first query statement from a client;
obtaining keywords from the first query statement;
Obtaining target hot search words with the correlation degree with the keywords being greater than or equal to a first threshold value from a hot word library, wherein the hot word library comprises a plurality of hot search words, the plurality of hot search words are obtained based on keywords contained in a second query statement input by a user with a target user account within a set time period, and the target user account has an association relationship with a user account logging in the client;
obtaining a first knowledge point with the correlation degree with the key words being greater than or equal to a second threshold value from an original knowledge base;
obtaining a second knowledge point with the correlation degree with the query condition being greater than or equal to a second threshold value from the hot spot knowledge base; the hot spot knowledge base comprises hit knowledge points corresponding to the second query statement containing the hot search word, the query condition comprises the key word and the target hot search word, or the query condition comprises the target hot search word;
and sending the links of the first knowledge points and the links of the second knowledge points to the client.
2. The knowledge point query method of claim 1, wherein the step of obtaining hot search words contained in the hot spot word stock comprises:
Acquiring a plurality of target user accounts with association relation with the user account logging in the client;
for each target user account, acquiring the second query statement input by the user with the target user account in a preset time period;
and determining keywords with the occurrence frequency greater than or equal to a third threshold value in second query sentences corresponding to the target user accounts as the hot search words.
3. The knowledge point querying method according to claim 1 or 2, wherein the step of obtaining hit knowledge points contained in the hotspot knowledge base comprises:
obtaining hit knowledge points corresponding to the second query sentences containing the hot search words from hit knowledge points of the second query sentences corresponding to the target user accounts respectively;
and storing hit results corresponding to the second query statement containing the hot search word into the hot spot knowledge base.
4. The knowledge point query method of claim 3, wherein the step of obtaining hit knowledge points for the second query statement comprises:
acquiring a query result corresponding to the second query statement, wherein the query result comprises links of one or more knowledge points;
And determining the knowledge points of the clicked links or the knowledge points with browsing time length longer than or equal to the preset time length in the query result as the hit knowledge points.
5. The knowledge point querying method according to claim 1 or 2, wherein the step of sending the link of the first knowledge point and the link of the second knowledge point to the client comprises:
based on the relativity of the first knowledge points and the keywords and the relativity of the second knowledge points and the query conditions, sequencing the first knowledge points and the second knowledge points to obtain sequencing results;
and sending the sequencing result to the client.
6. A knowledge point querying device, comprising:
the first receiving module is used for receiving a first query statement from the client;
the first acquisition module is used for acquiring keywords from the first query statement;
the second acquisition module is used for acquiring target hot search words with the correlation degree with the keywords being greater than or equal to a first threshold value from a hot spot word stock, the hot spot word stock comprises a plurality of hot search words, the plurality of hot search words are obtained based on keywords contained in a second query statement input by a user with a target user account in a set time period, and the target user account has an association relation with a user account logging in the client;
The third acquisition module is used for acquiring a first knowledge point with the correlation degree with the keyword being greater than or equal to a second threshold value from the original knowledge base;
the fourth acquisition module is used for acquiring a second knowledge point with the correlation degree with the query condition being greater than or equal to a second threshold value from the hot spot knowledge base; the hot spot knowledge base comprises hit knowledge points corresponding to the second query statement containing the hot search word, the query condition comprises the key word and the target hot search word, or the query condition comprises the target hot search word;
and the sending module is used for sending the links of the first knowledge points and the links of the second knowledge points to the client.
7. The knowledge point querying device as in claim 6, further comprising:
a fourth obtaining module, configured to obtain a plurality of target user accounts having an association relationship with a user account logging in the client;
a fifth obtaining module, configured to obtain, for each target user account, the second query sentence input by the user having the target user account in a preset time period;
and the first determining module is used for determining keywords with the occurrence frequency larger than or equal to a third threshold value in the second query sentences corresponding to the target user accounts as the hot search words.
8. A server, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the knowledge point interrogation method of any one of claims 1 to 5.
9. A computer readable storage medium, which when executed by a processor of a server, causes the server to perform the knowledge point interrogation method of any of claims 1-5.
10. A computer program product directly loadable into the internal memory of a computer and containing software code, which, when loaded and executed via the computer, is able to implement the knowledge point interrogation method of any of claims 1 to 5.
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