CN111428022B - Information retrieval method, device and storage medium - Google Patents
Information retrieval method, device and storage medium Download PDFInfo
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- CN111428022B CN111428022B CN202010220049.3A CN202010220049A CN111428022B CN 111428022 B CN111428022 B CN 111428022B CN 202010220049 A CN202010220049 A CN 202010220049A CN 111428022 B CN111428022 B CN 111428022B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/335—Filtering based on additional data, e.g. user or group profiles
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/338—Presentation of query results
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9538—Presentation of query results
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
An information retrieval method, apparatus, and storage medium, comprising the operations of: s1, determining a requirement object according to query information of a user, and determining a limiting condition set and limiting conditions of the first round in the set in a preset knowledge graph according to the requirement object; outputting the limiting conditions of the first round to a user; s2, acquiring attribute values input by a user for the limiting conditions, searching according to the attribute value set of each limiting condition in the limiting condition sets, and displaying search results to the user; if the user indicates to continue searching, step S3 is performed; and S3, if the non-output limiting conditions exist in the limiting condition set, calculating the information gain value of each non-output limiting condition according to the acquired attribute values input by the user and the preset knowledge graph, and outputting the limiting condition with the maximum information gain value to the user, and returning to the step S2. The method and the device can improve the retrieval speed and accuracy.
Description
Technical Field
The present invention relates to computer technology, and more particularly, to an information retrieval method, apparatus, and storage medium.
Background
In the year when information explodes, retrieving information meeting its own needs becomes an essential basic requirement for every internet user. However, in many cases, the answer meeting the user's requirement often depends on many conditions, such as the user wants to buy a health insurance meeting the user's requirement, and the judgment logic is complex depending on many conditions such as age, region, occupation, and disease history.
The traditional information retrieval method is that a round of interaction is carried out with a user, the user inputs a query word (for example, health insurance), the system returns an answer list, the user selects correct answers by himself, the method is suitable for simple logic answer retrieval, the accuracy is low when the dependent conditions are many, and the user experience is poor. One solution is to input a constraint into the query word (e.g. "50 years old, beijing, hypertension, health insurance"), which is disadvantageous in that: on the one hand, the user cannot know in advance which limiting conditions need to be input; on the other hand, the set of constraints affecting the final answer may be different, for example, if it is older than 45 years, the user's past medical history needs to be known, and if it is younger than 45 years, the user's occupation information needs to be known to determine whether it is sedentary.
Therefore, how to provide an efficient information retrieval system for users is a new problem to be solved.
Disclosure of Invention
The application provides an information retrieval method, an information retrieval device and a storage medium, which can achieve the purpose of improving retrieval speed and accuracy.
The application provides an information retrieval method which comprises the steps that S1, a demand destination is determined according to query information of a user, and a limiting condition set and limiting conditions of the first round in the set are determined in a preset knowledge graph according to the demand destination; outputting the limiting conditions of the first round to a user; s2, acquiring attribute values input by a user for the limiting conditions, searching according to the attribute value set of each limiting condition in the limiting condition sets, and displaying search results to the user; if the user indicates to continue searching, step S3 is performed; and S3, if the non-output limiting conditions exist in the limiting condition set, calculating the information gain value of each non-output limiting condition according to the acquired attribute values input by the user and the preset knowledge graph, and outputting the limiting condition with the maximum information gain value to the user, and returning to the step S2.
Compared with the related art, in the embodiment of the application, the attribute values input by the user for the limiting conditions are obtained, searching is performed according to the attribute value set of each limiting condition in the limiting condition set, and the limiting condition (the limiting condition with the maximum information gain value) which is most suitable for the user expectation is selected for the user, so that the result can be recommended to the user more accurately, and the searching speed and accuracy can be improved.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. Other advantages of the present application may be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The accompanying drawings are included to provide an understanding of the technical aspects of the present application, and are incorporated in and constitute a part of this specification, illustrate the technical aspects of the present application and together with the examples of the present application, and not constitute a limitation of the technical aspects of the present application.
FIG. 1 is a flow chart of an information retrieval method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of map structure information data based on insurance industry according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a computer process flow of an information retrieval method according to an embodiment of the present application;
fig. 4 is a block diagram of an information retrieval device according to an embodiment of the present application.
Detailed Description
The present application describes a number of embodiments, but the description is illustrative and not limiting and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or in place of any other feature or element of any other embodiment unless specifically limited.
The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements of the present disclosure may also be combined with any conventional features or elements to form a unique inventive arrangement as defined in the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive arrangements to form another unique inventive arrangement as defined in the claims. Thus, it should be understood that any of the features shown and/or discussed in this application may be implemented alone or in any suitable combination. Accordingly, the embodiments are not to be restricted except in light of the attached claims and their equivalents. Further, various modifications and changes may be made within the scope of the appended claims.
Furthermore, in describing representative embodiments, the specification may have presented the method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. Other sequences of steps are possible as will be appreciated by those of ordinary skill in the art. Accordingly, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. Furthermore, the claims directed to the method and/or process should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the embodiments of the present application.
As shown in fig. 1, an information retrieval method according to an embodiment of the present application includes the following operations:
s1, determining a requirement object according to query information of a user, and determining a limiting condition set and limiting conditions of the first round in the set in a preset knowledge graph according to the requirement object; outputting the limiting conditions of the first round to a user;
as shown in fig. 2, an example of the preset knowledge graph is insurance industry graph structure information data, and the demand purposes include: "find agent", "find site address", "find product", "price between existing products", "after-market problem", etc. Each requirement object corresponds to a limiting condition set, for example, the limiting condition set corresponding to the requirement target of 'product finding' comprises the following limiting conditions: "age", "occupation", "medical history", "marital status", "location", "education level", etc. When the user determines that the requirement is to find a product, the corresponding first round of limiting conditions are as follows: the "age" is directly output to the user.
In one exemplary implementation, the query information prompts the user to enter query keywords, etc., in a system interactive interface, either by parsing the user's desired purpose for the user's entered keywords, or prompting the user for a selected desired purpose.
S2, acquiring attribute values input by a user for the limiting conditions, searching according to the attribute value set of each limiting condition in the limiting condition sets, and displaying search results to the user; if the user indicates to continue searching, step S3 is performed;
in this step, some of the defined conditions in the defined condition set have been output to the user, i.e. the attribute values have been obtained, some of the defined conditions have not been output, and the attribute values may default to null or other default attribute values; the attribute value set of each limiting condition according to the searching can be regarded as a union of the attribute value set of the limiting condition which is outputted and the default attribute value set of the limiting condition which is not outputted in the limiting condition set; when the default attribute values of the non-outputted limiting conditions are all empty, the attribute value set of each limiting condition in the limiting condition set is the attribute value set of the outputted limiting condition.
For example, in the above example, assuming that default attribute values of the limitation conditions which are not output are all null, for the limitation condition "age" of the first round, assuming that the attribute value input by the user is "27", searching is performed according to age 27 in the first round of searching; assuming that the limiting condition of the second round of output is 'education level', and the attribute value input by the user is 'Gramineae', searching according to the age 27 during the second round of searching, wherein the education level is the Gramineae; and so on until the user no longer instructs to continue the search, or the defined conditions are all output.
In an exemplary embodiment, the knowledge graph may include a set of correspondence relationships corresponding to each requirement objective; the set of correspondence relationships includes: the corresponding relation of each limiting condition in the limiting condition set corresponding to the requirement purpose; wherein, the corresponding relation of a limiting condition comprises: when the limiting conditions have different attribute values, the scores of other limiting conditions in the limiting condition set; wherein, each attribute value can be respectively corresponding to different scores, or different ranges or intervals of the attribute value can be corresponding to different scores.
When calculating the information gain value of a limiting condition, the score corresponding to the attribute value input in this round can be directly used as the information gain value, or the information gain value can be obtained by accumulating the scores corresponding to the attribute values which have been input.
Where one qualifier has a different attribute value, the score for the other qualifier may be predetermined based on expert experience (e.g., the impact of the different qualifier on the search result) or otherwise.
In the example where the above demand aims at "finding a product", assuming that the attribute values of the constraint "age" include "18-35", "35-50", "50-65", "65" or more "four sections, the correspondence relationship of the constraint" age "includes:
when the attribute values are 18-35, the scores of the conditions of occupation, medical history, marital status, region and education level are limited;
when the attribute value is 35-50, the respective scores of the conditions of occupation, medical history, marital status, region and education degree are limited;
when the attribute value is 50-65, the respective scores of the conditions of occupation, medical history, marital status, region and education level are defined
When the attribute value is "65 or more", the respective scores of the conditions "occupation", "medical history", "marital status", "region where the user is located" and "education level" are defined.
Then, after the first round of output of the "age", assuming that the user inputs "27", respective scores of other limiting conditions in the limiting condition set can be obtained according to the corresponding relation, and the limiting condition with the maximum information gain value can be selected according to the scores for output.
Assuming that the second round outputs "education level", similarly, the knowledge graph may contain respective scores of other defined conditions when the education level has different attribute values; thus, based on the attribute values entered by the user for "education level", scores for other defined conditions may be determined.
At this time, when determining an information gain value of a constraint condition which is not output, a score corresponding to an attribute value of the education level inputted by the user may be used as the information gain value of the constraint condition, that is, the information gain value is determined only according to the attribute value inputted by the user in the present round; or the score corresponding to the attribute value of the education degree input by the user and the score corresponding to the attribute value of the age input by the user can be added to obtain the information gain value of the limiting condition, namely the information gain value is determined according to the round and the attribute value input before.
And S3, if the non-output limiting conditions exist in the limiting condition set, calculating the information gain value of each non-output limiting condition according to the acquired attribute values input by the user and the preset knowledge graph, and outputting the limiting condition with the maximum information gain value to the user, and returning to the step S2.
In an exemplary embodiment, the S3 further includes: if there are no more restrictions in the set of restrictions that have not been output, the search is terminated.
For example, in the example of the above requirement for "finding a product", the limiting condition determined by the first round of the user is "age", and the limiting condition which is still not outputted is "education degree", "occupation", "medical history", "marital status", "area", "marital status", "medical history", etc. and is not displayed to the user, so that the limiting condition which is confirmed by the next round of the user needs to be determined according to the attribute value of the limiting condition "age". When the user selects the attribute value "18-35", the definition condition that the information gain value corresponding to the attribute value is the maximum is "education level", and the next round of "education level" provides user confirmation. If there are no non-outputted qualifiers in the qualifier set, the search is terminated and the target result is outputted to the user. The target results are: one of the most suitable products, such as "health No. 1", "health No. 2", "health No. 3", "financial No. 1", "financial No. 2", "major medical No. 1", "Yijian Saint No. 1", etc., is recommended to the user.
According to the method and the device for recommending the results to the user, the attribute value set of each limiting condition in the limiting condition sets is searched, and the limiting condition (the limiting condition with the largest information gain value) which is most suitable for the user expectation is selected for the user, so that the results can be recommended to the user more accurately.
As shown in fig. 3, the computer processing flow example of the present application includes the following operations:
m1, prompting a user to input a limiting condition;
m2, receiving user input;
m3, calculating information gain of the condition according to the limiting condition input by the user, predicting the result, judging whether the confidence coefficient is larger than a preset threshold value, if so, entering a step M4, and if not, carrying out information gain sequencing on the rest limiting condition, and entering a limiting condition with the maximum information gain;
m4, displaying a result;
m5, receiving user input, judging whether the user finds an answer, and stopping searching if the answer is found; if no answer is found, the information gain ordering is carried out on the remaining limiting conditions, the limiting condition with the maximum information gain is selected, and the step M1 is carried out.
As shown in fig. 4, an information retrieval apparatus is characterized by comprising the following modules:
the first round condition output module 10 is configured to determine a requirement objective according to query information of a user, and determine a set of limiting conditions and limiting conditions of a first round in the set in a predetermined knowledge graph according to the requirement objective; outputting the limiting conditions of the first round to a user;
the searching module 20 is configured to obtain attribute values input by the user for the limiting conditions, search according to the attribute value set of each limiting condition in the limiting condition sets, and display the search result to the user; if the user indicates to continue searching, step S3 is performed;
the back-wheel condition output module 30 is configured to calculate an information gain value of each non-output limiting condition according to the obtained attribute value input by the user and a predetermined knowledge graph, and obtain the limiting condition with the maximum information gain value from the information gain value, output the limiting condition to the user, and return to the search module for operation if the non-output limiting condition exists in the limiting condition set.
The application also provides a device for directing the content, which comprises a processor and a memory, wherein the memory stores a program for directing the content; the processor is configured to read the program for targeting delivery of content, and execute the method of any one of the above.
The present application also provides a computer storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the method of any of the above.
In an exemplary embodiment, the specified business may be an insurance industry, a banking industry, a medical industry, or an e-commerce industry, etc.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
Claims (6)
1. An information retrieval method, comprising the operations of:
s1, determining a requirement object according to query information of a user, and determining a limiting condition set and limiting conditions of the first round in the set in a preset knowledge graph according to the requirement object; outputting the limiting conditions of the first round to a user;
s2, acquiring attribute values input by a user for the limiting conditions, searching according to the attribute value set of each limiting condition in the limiting condition sets, and displaying search results to the user; if the user indicates to continue searching, step S3 is performed;
s3, if the non-output limiting conditions exist in the limiting condition set, calculating the information gain value of each non-output limiting condition according to the acquired attribute values input by the user and the preset knowledge graph, and outputting the limiting condition with the maximum information gain value to the user from the information gain value, and returning to the step S2;
the knowledge graph comprises a group of corresponding relations corresponding to each requirement; the set of correspondence relationships includes: the corresponding relation of each limiting condition in the limiting condition set corresponding to the requirement purpose;
the knowledge graph also comprises a corresponding relation of a limiting condition; the correspondence of the one limiting condition includes: when the limiting condition has different attribute values, the corresponding relation between the different attribute values of the limiting condition and the information gain scores of other limiting conditions in the limiting condition set;
wherein the information gain value for each of the defined conditions is calculated by:
and directly taking the score corresponding to the attribute value input in the round as the information gain value of the limiting condition, or accumulating the score corresponding to each attribute value input in the round to obtain the information gain value of the limiting condition.
2. The information retrieval method as recited in claim 1, wherein S3 further comprises: if there are no more restrictions in the set of restrictions that have not been output, the search is terminated.
3. The information retrieval method as recited in claim 1, wherein the determining the demand destination based on the query information of the user comprises: and receiving voice input, text input or clicking operation of a user on the predetermined knowledge graph query interface to acquire the query information.
4. An information retrieval apparatus, comprising:
the first round condition output module is used for determining a requirement purpose according to query information of a user, and determining a limiting condition set and limiting conditions of a first round in the set in a preset knowledge graph according to the requirement purpose; outputting the limiting conditions of the first round to a user;
the search module is used for acquiring attribute values input by the user for the limiting conditions, searching according to the attribute value set of each limiting condition in the limiting condition sets, and displaying search results to the user; if the user indicates to continue searching, step S3 is performed;
the back wheel condition output module is used for calculating the information gain value of each non-output limiting condition according to the acquired attribute value input by the user and a preset knowledge graph if the non-output limiting condition exists in the limiting condition set, and outputting the limiting condition with the maximum information gain value to the user from the information gain value, and returning to the search module for operation;
the knowledge graph comprises a group of corresponding relations corresponding to each requirement; the set of correspondence relationships includes: the corresponding relation of each limiting condition in the limiting condition set corresponding to the requirement purpose;
the knowledge graph also comprises a corresponding relation of a limiting condition; the correspondence of the one limiting condition includes: when the limiting condition has different attribute values, the corresponding relation between the different attribute values of the limiting condition and the information gain scores of other limiting conditions in the limiting condition set;
wherein the information gain value for each of the defined conditions is calculated by:
and directly taking the score corresponding to the attribute value input in the round as the information gain value of the limiting condition, or accumulating the score corresponding to each attribute value input in the round to obtain the information gain value of the limiting condition.
5. An apparatus for directing delivery of content, comprising a processor and a memory, wherein the memory stores a program for directing delivery of content; the processor is configured to read the program for targeting content and perform the method of any of claims 1-3.
6. A computer storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1-3.
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