CN111061750A - Query processing method and device and computer readable storage medium - Google Patents
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Abstract
The application provides a query processing method, a query processing device and a computer readable storage medium, and a query entity input in an information query interface from the outside is obtained; querying a target information entity associated with a query entity according to a knowledge graph comprising a plurality of entities and an association relationship between the plurality of entities; outputting a query result on an information query interface based on the target information entity; and when a knowledge graph updating event is detected, updating the association relation in the knowledge graph. By implementing the scheme of the application, the knowledge graph is adopted to comprehensively and accurately deduce the information result directly or potentially related to the input query data, thereby effectively enhancing the information query capability and improving the effectiveness of the information query result; and the incidence relation in the knowledge graph is updated according to the knowledge graph updating event, so that the accuracy of information query is further improved.
Description
Technical Field
The present application relates to the field of electronic technologies, and in particular, to a query processing method and apparatus, and a computer-readable storage medium.
Background
End users typically obtain desired query results, such as applications, themes, games, songs, etc., by entering query data at an information query interface. At present, a scheme of performing information query based on key fields is generally adopted, that is, a full-library search is performed through key fields in query data input by a user, so that a result matched with the key fields is returned to the user. However, in practical applications, the information query method requires that the query data input by the user should be accurate, otherwise the query data would easily exceed the query range, and the query result cannot be output or the output query result is not the result expected by the user, so that the capability of information query is limited, and the validity of the information query result is poor.
Disclosure of Invention
The embodiment of the application provides a query processing method, a query processing device and a computer readable storage medium, which can at least solve the problems of relatively limited information query capability and relatively poor validity of an information query result caused by information query based on a key field in query data in the related art.
A first aspect of the embodiments of the present application provides a query processing method, including:
acquiring a query entity input on an information query interface;
querying a target information entity associated with the querying entity according to a knowledge graph; wherein the knowledge-graph comprises a plurality of entities and associations between the plurality of entities;
outputting a query result on the information query interface based on the target information entity;
and when a knowledge graph updating event is detected, updating the incidence relation in the knowledge graph.
A second aspect of embodiments of the present application provides a query processing. An apparatus, comprising:
the acquisition module is used for acquiring a query entity input on the information query interface;
the query module is used for querying a target information entity associated with the query entity according to the knowledge graph; wherein the knowledge-graph comprises a plurality of entities and associations between the plurality of entities;
the output module is used for outputting a query result on the information query interface based on the target information entity;
and the updating module is used for updating the incidence relation in the knowledge graph when a knowledge graph updating event is detected.
A third aspect of embodiments of the present application provides an electronic apparatus, including: the query processing method provided by the first aspect of the embodiments of the present application includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the query processing method provided by the first aspect of the embodiments of the present application.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, on which a computer program is stored, where when the computer program is executed by a processor, the computer program implements the steps in the query processing method provided in the first aspect of the embodiments of the present application.
In view of the above, according to the query processing method, device and computer readable storage medium provided by the scheme of the application, the query entity externally input in the information query interface is obtained; querying a target information entity associated with a query entity according to a knowledge graph comprising a plurality of entities and an association relationship between the plurality of entities; outputting a query result on an information query interface based on the target information entity; and when a knowledge graph updating event is detected, updating the association relation in the knowledge graph. By implementing the scheme of the application, the knowledge graph is adopted to comprehensively and accurately deduce the information result directly or potentially related to the input query data, thereby effectively enhancing the information query capability and improving the effectiveness of the information query result; and the incidence relation in the knowledge graph is updated according to the knowledge graph updating event, so that the accuracy of information query is further improved.
Drawings
Fig. 1 is a schematic basic flow chart of a query processing method according to a first embodiment of the present application;
FIG. 2 is a schematic flow chart of a knowledge graph generation method according to a first embodiment of the present application;
FIG. 3 is a schematic view of a knowledge-graph provided in a first embodiment of the present application;
fig. 4 is a schematic flowchart of an information entity query method according to a first embodiment of the present application;
fig. 5 is a schematic flowchart of another information entity query method according to the first embodiment of the present application;
fig. 6 is a schematic flowchart of a query processing method according to a second embodiment of the present application;
fig. 7 is a schematic diagram of program modules of a query processing apparatus according to a third embodiment of the present application;
fig. 8 is a schematic diagram of program modules of another query processing apparatus according to a third embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present application.
Detailed Description
In order to make the objects, features and advantages of the present invention more apparent and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In order to solve the technical problems of limited information query capability and poor validity of an information query result caused by performing information query based on a key field in query data in the related art, a first embodiment of the present application provides a query processing method, for example, fig. 1 is a basic flow chart of the query processing method provided in this embodiment, and the query processing method includes the following steps:
Specifically, in this embodiment, the information query interface may be an information query interface of an information query application on a terminal such as an application store, a theme store, a game store, or a panning function, and the query entity is query data manually or by voice input from the outside on the information query interface. In practical applications, the query entity may be a field directly or potentially associated with the name of the information query result, associated with the user's search intent.
In an optional implementation manner of this embodiment, before obtaining the query entity input in the information query interface, the method may further include: acquiring application attribute information of an information query application to which an information query interface belongs, and judging whether a query processing triggering condition is met or not based on the acquired application attribute information; and if so, executing the step of acquiring the query entity input on the information query interface.
Specifically, in practical applications, in order to improve the rationality of the terminal executing the query processing flow of this embodiment, the query processing flow is triggered only when the application attribute information of the current information query application satisfies a specific trigger condition. It should be noted that the application attribute information may include an application type, an application regulation state, and the like.
Specifically, the knowledge graph of the present embodiment is a knowledge system including a plurality of entities and an association relationship between the plurality of entities. The construction of the knowledge graph is derived from the integrated processing of mass data, and compared with the traditional query engine based on key field matching, the query service constructed based on the knowledge graph can support more natural and complex query input and can more comprehensively and deeply understand the semantics of query information. It should be understood that the target information entities of the present embodiment may be application software, games, themes, etc.
It should be noted that in this embodiment, different knowledge data may be captured from the database, and then subjected to semantic processing, such as entity extraction, cleaning, mapping, disambiguation, and the like, to ensure data quality. The entity extraction is to extract specific fact information from distributed and heterogeneous texts, extract implicit semantics and express the semantics in a more structured and clearer form; semantic cleansing is to filter data that does not meet the requirements, such as filtering duplicate data, error data, incomplete data, and the like; semantic mapping means that words are mapped to a semantic space to obtain vectors; semantic disambiguation can be viewed as a classification problem, where a word W has K meanings, where disambiguation of W is a determination of which meaning W has used in a particular sentence, i.e., classification of W into one of K classes, which may be based on words adjacent to W, i.e., context C of W. After semantic processing is carried out on external knowledge data, the entities are classified and semantically associated according to specific service logic to form a knowledge entity relationship with definite meaning, and thus, a knowledge map is constructed.
It should also be understood that the knowledge graph may exist in a knowledge representation form of an "entity-relationship-entity" triple, and in practical applications, the knowledge graph may be further evolved and updated through knowledge reasoning and further mining implicit knowledge, so as to enrich and expand the knowledge graph.
In an optional implementation manner of this embodiment, before querying a target information entity associated with a query entity according to a knowledge-graph, the method further includes: performing semantic analysis on the query entity; judging whether the query entity is in the query perception range of the information query interface according to the semantic analysis result; when not in the query perception scope, the step of querying a target information entity associated with the query entity according to the knowledge-graph is performed.
Specifically, in this embodiment, the query sensing range includes all query entities whose semantics and the semantics of the information entity are in the same level (or domain), for example, a query entity containing a name of a query result is in one level, and a query entity containing a functional attribute of the query result is in another level, and for a query entity in the query sensing range, the query entity may be completely the same as the information entity, or may be a part of key fields in the information entity. The embodiment determines the hierarchy of the current query entity based on the semantic analysis result of the query entity, then judges whether the information query application can sense the query entity in a traditional way or not based on the determined hierarchy, and triggers the step of querying the target information entity based on the knowledge graph if the information query application is judged to be not, so that the waste of terminal processing performance and the reduction of the overall efficiency of information query caused by blind execution of the knowledge graph query under unnecessary conditions can be avoided.
In some embodiments of this embodiment, in order to further improve the comprehensiveness and accuracy of the information query capability and the information query result, after the query entity input on the information query interface is obtained, the query entity may be further split to obtain a plurality of participles, then the plurality of participles are recombined according to a preset field combination rule to obtain a plurality of different query entities, and correspondingly, target information entities respectively associated with the plurality of different query entities are queried according to the knowledge graph.
And 103, outputting a query result on an information query interface based on the target information entity.
Specifically, the knowledge graph is adopted to comprehensively and accurately reason out the query result directly or potentially associated with the query request, so that the information query capability of the information query application is effectively enhanced, and the effectiveness of the information query result is improved. In this embodiment, the application query is taken as an example, for example: in the "game of authority" in the flight video playing, currently, the "game of authority" is searched in the software store, because the query data in the field does not belong to the query perception range of the general software store, if the user searches the "game of authority", the flight video is not necessarily seen in the search result. But by introducing knowledge graph and by real-time reasoning function, the relation between the game of power and the Tencent video can be found out by exploring the potential relation between the user input and the application software. Moreover, by adopting the method, a deeper recommendation relation can be found, for example, if a popular character in the game of the right is named as 'dragon mother', if the user inputs query data 'dragon mother', the vacation video can be found to be related to the query through the knowledge reasoning relation, and surprise is provided for the user.
And 104, when a knowledge graph updating event is detected, updating the association relation in the knowledge graph.
Specifically, in practical applications, the association relationship between the entities in the knowledge graph is not stable for a long time, so that the association relationship between the entities is usually monitored by a manual method at present, and the knowledge graph is updated according to a manual monitoring result, but the efficiency and accuracy of the manual monitoring method are limited. Based on this, the embodiment automatically detects the knowledge graph update event, and updates the association relationship in the knowledge graph in time according to the knowledge graph update event, thereby ensuring the efficiency and accuracy of knowledge graph update and further improving the accuracy of information query. It should be noted that, in the present embodiment, updating the knowledge graph may include creating and removing the association relationship in the knowledge graph. The establishment of the association relationship means establishing an association relationship with respect to existing entities in the knowledge graph, where the established association relationship may be an association relationship established between a plurality of existing entities, or an association relationship established between an existing entity and a newly added entity; and the release of the association relationship refers to the release of the association relationship among the entities existing in the knowledge graph.
In an optional implementation manner of this embodiment, when a knowledge graph update event is detected, updating the association relationship in the knowledge graph includes: acquiring the aging information of all query entities in the knowledge graph in real time; detecting a query entity of which the accumulated existence duration in the knowledge graph exceeds the utility exertion duration indicated by the aging information; and when detecting the query entity of which the accumulated existence duration exceeds the utility exertion duration, releasing the association relationship between the detected query entity and the corresponding associated target information entity.
Specifically, in this embodiment, the utility exertion duration of the query entity is obtained in real time, and when it is monitored that the utility exertion duration of the query entity arrives, the association relationship between the utility exertion duration and the originally associated information entity is released. It should be understood that the utility exertion time length of this embodiment may be a specific validity time length of the query entity or a time length of keeping a specific activity/heat, when the query entity fails or loses the specific activity/heat, continuing to perform information entity association on the query entity will result in erroneous association or meaningless association, so that in this case, the association relationship between the query entity and the target information entity is released, on one hand, useless or erroneous query result output is avoided, and on the other hand, the data volume of the knowledge graph can be further reduced. For example, taking a movie work "at a distant place" as an inquiry entity, when the movie work is about to be shown, taking movie and television APPs "Tengchong video", "Youkou video" and "Aiqiyi" with copyright of the movie and television work as target information entities, and establishing an association relationship between the inquiry entity and the target information entities, however, all episodes of the movie and television works may be shown by the movie and television APPs within a period of time, so that the movie and television APPs will put off the shelf of the movie and television work, and thus the embodiment takes the whole showing period of the movie and television work as its time effect information, and when it is detected that the duration of the movie and television work in the knowledge graph reaches the showing period, the association relationship between the movie and television APPs is released on the knowledge graph.
It should be noted that, in practical applications, when there are multiple target information entities at the same time, there may be multiple display manners of the query result, and in one case, the user experience rating of each target information entity may be obtained, then all the target information entities are ranked from high to low according to the user experience rating, and then all the target information entities are sequentially displayed on the information query interface according to the ranking result.
In another case, when there are a plurality of target information entities, outputting the query result on the information query interface based on the target information entities includes: respectively acquiring the use limit level corresponding to each target information entity and the external authority verification level; screening a target information entity with the use limit level matched with the authority verification level; and displaying the screened target information entity as a query result on an information query interface.
Specifically, in practical applications, the acceptance degrees of different information by different user groups are different, and taking an application program as an example, the game application with a bloody smell scene causes discomfort to the old, children and other groups with poor psychological bearing capacity, or the open social application causes the user groups with poor resolving power and self-control capability to be deceived or enthusiastic. Based on this, in this embodiment, after all the target information entities are queried, the rights of the user are verified instead of directly displaying all the target information entities, the obtained rights verification level is matched with the usage restriction level of each target information entity, the target information entity matched with the user rights is subjected to result output, and the target information entities unsuitable for the user are filtered out. Therefore, the embodiment can correspondingly display the result aiming at the users with different permission levels by means of a differentiated result display mode, so that the interaction friendliness is improved, and the risk that the potential query result causes negative influence on the user is avoided.
As shown in fig. 2, which is a schematic flow chart of a method for generating a knowledge graph provided in this embodiment, in an optional implementation manner of this embodiment, before querying a target information entity associated with a query entity according to a knowledge graph, the method specifically includes the following steps:
and 203, indirectly associating the entities spaced by one hierarchy in different sub-knowledge graphs based on the common entities included among the sub-knowledge graphs to generate the knowledge graphs.
Specifically, in this embodiment, the entities in different domains are classified into different levels, for example, all entities including the name of the query result are in the same level, and all entities including the functional attribute of the query result are in the same level, wherein the entities in adjacent levels can be directly associated with each other, and the entities spaced by one level can be indirectly associated with each other, for example, the entity a is directly associated with the entity B, and the entity B is directly associated with the entity C, so that the entity a is indirectly associated with the entity C through the entity B, and thus the entity a is an entity in an adjacent level, and the entity a is an entity spaced by one level. In the embodiment, the different entities are directly associated and indirectly associated to form the overall association of all the entities, so that the knowledge graph is obtained.
The present embodiment describes the generation process of the above knowledge graph by using a specific example, where the information query application is an application store. Firstly, acquiring the relation between a film and television APP and a film and television work, and establishing a sub knowledge map A of the film and television APP and the film and television work; then obtaining the relation between the film and television works and the character roles in the works, and establishing a sub-knowledge graph B of the film and television works and the character roles; and then, the movie and television works are used as connection points, the child knowledge graph A of the movie and television works and the child knowledge graph B of the movie and television works and the character roles are associated, and a knowledge graph C containing the movie and television works, the movie and television APPs and the character roles is manufactured. Fig. 3 is a schematic diagram of a knowledge graph provided in this embodiment, in which entities of three different levels, namely, movie works, movie APPs, and character roles, are associated, and for any query entity, a corresponding movie APP can be found according to an association relationship in the knowledge graph to perform application recommendation. With reference to fig. 3, for example, the user searches for "dragon mother", and through the knowledge reasoning relationship, the user can know that the user is a character role in the "game of right" of the movie and television work, and then further through the knowledge reasoning relationship, the user can obtain the movie and television APP "Tengchong video" with the copyright of the movie and television work.
In an optional implementation manner of this embodiment, querying, according to the knowledge-graph, a target information entity associated with the querying entity includes: inquiring the inquiring entity according to the knowledge graph; when the result is not inquired, the inquiry entity is converted; and inquiring the target information entity associated with the inquired entity after the conversion processing according to the knowledge graph.
Specifically, in practical application, the query entity input from the outside may be incorrect and result in unsuccessful query, so that the embodiment converts the query entity based on the preset conversion rule to perform effective query. In this embodiment, the conversion process performed on the query entity includes, but is not limited to, the following ways: carrying out synonymy expansion processing on the query entity, carrying out character error correction processing on the query entity, and carrying out language translation processing on the query entity. The synonymy expansion processing refers to obtaining synonymy query entities which can be equivalently replaced based on the original query entities, the word error correction processing is used for correcting error fields in the original query entities, and the language translation processing is used for translating the original query entities from one language into another language.
In an optional implementation manner of this embodiment, querying, according to the knowledge-graph, a target information entity associated with the querying entity includes: performing keyword division on query entities to obtain a plurality of sub-query entities; a target information entity associated with a query entity is queried on a knowledge-graph based on a plurality of sub-query entities.
Specifically, the complexity of the query entity input by the user is different, and in practical application, the query entity may be composed of a plurality of keywords.
As shown in fig. 4, which is a schematic flow chart of an information entity query method provided in this embodiment, further, in an optional implementation manner of this embodiment, querying, on a knowledge graph, a target information entity associated with a query entity based on a plurality of sub-query entities specifically includes the following steps:
Specifically, in this embodiment, the information validity degrees provided by different keywords in the query entity are different, the query priority ranking is performed on each sub-query entity based on the information validity degrees in this embodiment, the higher the information validity degree is, the higher the query priority of the sub-query entity is, and then the information entity query is performed on the knowledge graph only for the sub-query entity with the highest query priority, so as to improve the accuracy of the information entity query and avoid recommending irrelevant information to the user.
As shown in fig. 5, which is a flowchart of another information entity query method provided in this embodiment, further, in another optional implementation manner of this embodiment, querying, on a knowledge graph, a target information entity associated with a query entity based on a plurality of sub-query entities specifically includes the following steps:
502, performing recommendation priority ordering on all information entities obtained by query;
Specifically, in this embodiment, the information entities may be queried on the knowledge graph for the plurality of partitioned sub-query entities, so that a plurality of information entities can be obtained through querying. However, the degree of engagement between different information entities and the actual needs of the user is different, so that the present embodiment performs recommendation priority ranking on the information entities, and selects an information entity to be determined as a target information entity associated with the query entity based on the ranking result, and in practical applications, the information entity with the highest recommendation priority may be preferably determined as the target information entity.
Based on the technical scheme of the embodiment of the application, a query entity input from the outside on an information query interface is obtained; querying a target information entity associated with a query entity according to a knowledge graph comprising a plurality of entities and an association relationship between the plurality of entities; outputting a query result on an information query interface based on the target information entity; and when a knowledge graph updating event is detected, updating the association relation in the knowledge graph. By implementing the scheme of the application, the knowledge graph is adopted to comprehensively and accurately deduce the information result directly or potentially related to the query data, thereby effectively enhancing the information query capability and improving the effectiveness of the information query result; and the incidence relation in the knowledge graph is updated according to the knowledge graph updating event, so that the accuracy of information query is further improved.
The method in fig. 6 is a refined query processing method provided in the second embodiment of the present application, where the query processing method includes:
In this embodiment, the externally input query information corresponds to a query entity, and may be a field directly associated or potentially associated with the application name.
In this embodiment, the query sensing range includes all query entities with semantics at the same level as those of the application entity, and the query entities in the query sensing range may be completely the same as the application entity or some key fields in the application entity.
And 603, when the query entity is not in the query perception range, performing keyword division on the query entity to obtain a plurality of sub-query entities.
Specifically, in practical application, there may be a query entity composed of a plurality of keywords, and in order to ensure accuracy of application query, the present embodiment performs keyword division on the query entity, and queries a target application entity on a knowledge graph based on the divided sub-query entities.
And step 604, performing query priority ordering on the plurality of sub-query entities, and determining the sub-query entity with the highest query priority ordering.
In the embodiment, after semantic processing is performed on knowledge data acquired from the outside, the entities are classified and semantically associated according to specific service logic to form a knowledge entity relationship with a definite meaning, so that a knowledge graph is constructed and completed, wherein the knowledge graph comprises a plurality of entities and association relationships among the plurality of entities.
In this embodiment, the information validity degrees provided by different keywords in the query entity are different, the query priority ranking is performed on each sub-query entity based on the information validity degrees in this embodiment, the higher the information validity degree is, the higher the query priority of the sub-query entity is, and then the application entity query is performed on the knowledge graph only for the sub-query entity with the highest query priority, so as to improve the accuracy of the application entity query and avoid recommending irrelevant applications to the user.
And step 607, performing application display on the application query interface based on the target application entity.
In the embodiment, a knowledge graph reasoning mode is adopted, a potential association relation which is difficult to perceive between a query and an APP input by a user can be found, and the deduced application is displayed on an application query interface of an application store and recommended to the user.
It should be understood that, the size of the serial number of each step in this embodiment does not mean the execution sequence of the step, and the execution sequence of each step should be determined by its function and inherent logic, and should not be limited uniquely to the implementation process of the embodiment of the present application.
According to the query processing method disclosed by the embodiment of the application, a query entity input by an application query interface of an external application store is acquired; when the query entity is not in the query perception range of the application store, performing keyword division on the query entity to obtain a plurality of sub-query entities; the method comprises the steps of carrying out query priority ordering on a plurality of sub-query entities, and querying an application entity associated with the sub-query entity with the highest query priority ordering according to a knowledge graph comprising a plurality of entities and incidence relations among the entities; determining the application entity obtained by querying the sub-query entities with the highest priority as a target application entity associated with the query entity; and performing application display on an application query interface based on the target application entity. By implementing the scheme of the application, the application directly or potentially associated with the query information is comprehensively and accurately inferred by adopting the knowledge graph, so that the application query capability of an application store is effectively enhanced, and the effectiveness of an application query result is improved; and only aiming at the sub-query entity with the highest query priority, the application entity query is carried out on the knowledge graph so as to improve the accuracy of the application entity query and avoid recommending irrelevant applications to the user.
Fig. 7 is a query processing apparatus according to a third embodiment of the present application. The query processing device can be used for realizing the query processing method in the foregoing embodiments. As shown in fig. 7, the query processing apparatus mainly includes:
an obtaining module 701, configured to obtain a query entity input on an information query interface;
a query module 702 for querying a target information entity associated with a query entity according to a knowledge graph; the knowledge graph comprises a plurality of entities and incidence relations among the entities;
an output module 703, configured to output a query result on an information query interface based on the target information entity;
and an updating module 704, configured to update the association relationship in the knowledge graph when a knowledge graph update event is detected.
As shown in fig. 8, another query processing apparatus provided in this embodiment is an optional implementation manner of this embodiment, where the query processing apparatus further includes: the judging module 705 is used for performing semantic analysis on the query entity before querying the target information entity associated with the query entity according to the knowledge graph; and judging whether the query entity is in a query sensing range of the information query interface according to the semantic analysis result, wherein the query sensing range comprises all query entities with the semantics being in the same level as the semantics of the information entity. Correspondingly, when not in the query perception scope, the query module 702 performs the function of querying the target information entity associated with the query entity according to the knowledge-graph.
Referring to fig. 8, in an alternative implementation manner of this embodiment, the query processing apparatus further includes: a generating module 706, configured to respectively summarize a plurality of entities into entity sets of different levels before querying a target information entity associated with a query entity according to a knowledge graph; directly associating entities in the entity sets of adjacent levels to obtain a sub-knowledge graph; and indirectly associating entities separated by a hierarchy in different sub-knowledge graphs based on common entities included among the sub-knowledge graphs to generate the knowledge graphs.
In an optional implementation manner of this embodiment, the query module 702 is specifically configured to: inquiring the inquiring entity according to the knowledge graph; when the result is not inquired, the inquiry entity is converted; and inquiring the target information entity associated with the inquired entity after the conversion processing according to the knowledge graph.
Further, in an optional implementation manner of this embodiment, when the query module 702 performs the conversion processing on the query entity, specifically, it is configured to: carrying out synonymy expansion processing on the query entity; or, the word error correction processing is carried out on the query entity; or, performing language translation processing on the query entity.
In an optional implementation manner of this embodiment, the query module 702 is specifically configured to: performing keyword division on query entities to obtain a plurality of sub-query entities; a target information entity associated with a query entity is queried on a knowledge-graph based on a plurality of sub-query entities.
Further, in an optional implementation manner of this embodiment, when querying the target information entity associated with the query entity on the knowledge-graph based on a plurality of sub-query entities, the query module 702 is specifically configured to: performing query prioritization on the plurality of sub-query entities; querying the information entities associated with the sub-query entities with the highest query priority order on the knowledge graph; determining the information entity obtained by querying the sub-query entity with the highest priority as a target information entity associated with the query entity; or, respectively querying information entities associated with a plurality of sub-query entities on the knowledge-graph; performing recommendation priority ordering on all the information entities obtained by query; and determining the information entity with the highest recommendation priority as a target information entity associated with the query entity.
In an optional implementation manner of this embodiment, the updating module 704 is specifically configured to: acquiring the aging information of all query entities in the knowledge graph in real time; detecting a query entity of which the accumulated existence duration in the knowledge graph exceeds the utility exertion duration indicated by the aging information; and when detecting the query entity of which the accumulated existence duration exceeds the utility exertion duration, releasing the association relationship between the detected query entity and the corresponding associated target information entity.
In an optional implementation manner of this embodiment, when there are multiple target information entities, the output module 703 is specifically configured to: respectively acquiring the use limit level corresponding to each target information entity and the external authority verification level; screening a target information entity with the use limit level matched with the authority verification level; and displaying the screened target information entity as a query result on an information query interface.
It should be noted that, the query processing methods in the first and second embodiments can be implemented based on the query processing device provided in this embodiment, and it can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working process of the query processing device described in this embodiment may refer to the corresponding process in the foregoing method embodiment, and details are not described here.
According to the query processing device provided by the embodiment, a query entity input in an information query interface from the outside is obtained; querying a target information entity associated with a query entity according to a knowledge graph comprising a plurality of entities and an association relationship between the plurality of entities; outputting a query result on an information query interface based on the target information entity; and when a knowledge graph updating event is detected, updating the association relation in the knowledge graph. By implementing the scheme of the application, the knowledge graph is adopted to comprehensively and accurately deduce the information result directly or potentially related to the query data, thereby effectively enhancing the information query capability and improving the effectiveness of the information query result; and the incidence relation in the knowledge graph is updated according to the knowledge graph updating event, so that the accuracy of information query is further improved.
Referring to fig. 9, fig. 9 is an electronic device according to a fourth embodiment of the present disclosure. The electronic device can be used for realizing the query processing method in the foregoing embodiment. As shown in fig. 9, the electronic device mainly includes:
The Memory 901 may be a high-speed Random Access Memory (RAM) Memory or a non-volatile Memory (non-volatile Memory), such as a magnetic disk Memory. The memory 901 is used for storing executable program code, and the processor 902 is coupled to the memory 901.
Further, an embodiment of the present application also provides a computer-readable storage medium, where the computer-readable storage medium may be provided in an electronic device in the foregoing embodiments, and the computer-readable storage medium may be the memory in the foregoing embodiment shown in fig. 9.
The computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the query processing method in the foregoing embodiments. Further, the computer-readable storage medium may be various media that can store program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a RAM, a magnetic disk, or an optical disk.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of modules is merely a division of logical functions, and an actual implementation may have another division, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a readable storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned readable storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
It should be noted that, for the sake of simplicity, the above-mentioned method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present application is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In view of the above description of the query processing method, apparatus and computer-readable storage medium provided by the present application, those skilled in the art will recognize that the scope of the present application can be modified according to the following claims.
Claims (10)
1. A query processing method, comprising:
acquiring a query entity input on an information query interface;
querying a target information entity associated with the querying entity according to a knowledge graph; wherein the knowledge-graph comprises a plurality of entities and associations between the plurality of entities;
outputting a query result on the information query interface based on the target information entity;
and when a knowledge graph updating event is detected, updating the incidence relation in the knowledge graph.
2. The query processing method of claim 1, wherein prior to querying the target information entity associated with the querying entity according to the knowledge-graph, further comprising:
performing semantic analysis on the query entity;
judging whether the query entity is in the query perception range of the information query interface according to the semantic analysis result; wherein the query perception range comprises all query entities with the semantics being in the same level as the semantics of the information entities;
when the target information entity is not in the query perception scope, the step of querying the target information entity associated with the query entity according to the knowledge-graph is executed.
3. The query processing method of claim 1, wherein prior to querying the target information entity associated with the querying entity according to the knowledge-graph, further comprising:
respectively summarizing the entities into entity sets of different levels;
directly associating entities in the entity sets of adjacent levels to obtain a sub-knowledge graph;
and indirectly associating entities separated by a hierarchy in different sub-knowledge graphs based on common entities included among the sub-knowledge graphs to generate the knowledge graphs.
4. The query processing method of claim 1, wherein the updating the associations in the knowledge-graph upon detection of a knowledge-graph update event comprises:
acquiring the aging information of all query entities in the knowledge graph in real time;
detecting a query entity having an accumulated existence duration in the knowledge graph exceeding a utility exertion duration indicated by the aging information;
and when detecting the query entity of which the accumulated existence duration exceeds the utility exertion duration, releasing the association relationship between the detected query entity and the corresponding associated target information entity.
5. The query processing method of any one of claims 1 to 4, wherein the querying a target information entity associated with the querying entity according to a knowledge-graph comprises:
performing keyword division on the query entity to obtain a plurality of sub-query entities;
querying, on a knowledge-graph, a target information entity associated with the querying entity based on the plurality of sub-querying entities.
6. The query processing method of claim 5, wherein querying a target information entity associated with the querying entity on a knowledge-graph based on the plurality of sub-querying entities comprises:
performing query prioritization on the plurality of sub-query entities;
querying the information entities associated with the sub-query entities with the highest query priority order on a knowledge graph;
determining the information entity obtained by querying the sub-query entity with the highest priority as a target information entity associated with the query entity;
or querying information entities associated with the plurality of sub-query entities on the knowledge-graph respectively;
performing recommendation priority ordering on all the information entities obtained by query;
and determining the information entity with the highest recommendation priority order as a target information entity associated with the query entity.
7. The query processing method according to any one of claims 1 to 4, wherein, when there are a plurality of the target information entities, the outputting the query result on the information query interface based on the target information entities includes:
respectively acquiring the use restriction level corresponding to each target information entity and the external authority verification level;
screening target information entities with usage restriction levels matched with the authority verification levels;
and displaying the screened target information entity as a query result on the information query interface.
8. A query processing apparatus, comprising:
the acquisition module is used for acquiring a query entity input on the information query interface;
the query module is used for querying a target information entity associated with the query entity according to the knowledge graph; wherein the knowledge-graph comprises a plurality of entities and associations between the plurality of entities;
the output module is used for outputting a query result on the information query interface based on the target information entity;
and the updating module is used for updating the incidence relation in the knowledge graph when a knowledge graph updating event is detected.
9. An electronic device, comprising: the system comprises a memory, a processor and a bus, wherein the bus is used for realizing connection communication between the memory and the processor; the processor is configured to execute a computer program stored on the memory, and when the processor executes the computer program, the processor implements the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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