CN111858236A - Knowledge graph monitoring method and device, computer equipment and storage medium - Google Patents
Knowledge graph monitoring method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of knowledge graphs, and provides a knowledge graph monitoring method, a knowledge graph monitoring device, computer equipment and a storage medium, wherein the knowledge graph monitoring method comprises the following steps: responding to a received monitoring request for monitoring the knowledge graph to be detected, and acquiring the record information of the knowledge graph to be detected according to the monitoring request; configuring a plurality of first knowledge bases for the knowledge graph to be tested according to the record information; grading the contents in the plurality of first knowledge bases to obtain a plurality of second knowledge bases; structuring the plurality of second knowledge bases to obtain standard monitoring templates; calling the standard monitoring template to scan the to-be-detected knowledge graph and acquiring a scanning result; and generating a monitoring report according to the scanning result. The invention can accurately and custom monitor the knowledge graph, has high monitoring quality and can assist in improving the service capability of the knowledge graph.
Description
Technical Field
The invention relates to the technical field of knowledge graphs, in particular to a knowledge graph monitoring method, a knowledge graph monitoring device, computer equipment and a storage medium.
Background
With the development of the technology, more and more artificial posts are replaced by a software system taking an artificial intelligence knowledge graph as a core technology or a service robot related to the software system, and the knowledge graph as a semantic network has extremely strong expression capability and modeling flexibility and has very wide application scenes in life.
Although there are many artificial intelligence knowledge graph systems on the market, the corresponding graph service compliance monitoring system and service capability authentication system do not exist, so that it cannot be determined whether the knowledge graph applied in the daily work service has compliance or not, and whether the work service content matches with the service capability expected by the society or not.
Therefore, it is necessary to provide a scheme for monitoring the knowledge graph to improve the service capability of the knowledge graph.
Disclosure of Invention
In view of the above, the present invention provides a knowledge graph monitoring method, an apparatus, a computer device and a storage medium, and aims to solve the technical problem that the knowledge graph cannot be monitored in the prior art.
The invention provides a knowledge graph monitoring method, which is applied to computer equipment and comprises the following steps:
Responding to a received monitoring request for monitoring the knowledge graph to be detected, and acquiring the record information of the knowledge graph to be detected according to the monitoring request;
configuring a plurality of first knowledge bases for the knowledge graph to be tested according to the record information;
grading the contents in the plurality of first knowledge bases to obtain a plurality of second knowledge bases;
structuring the plurality of second knowledge bases to obtain standard monitoring templates;
calling the standard monitoring template to scan the to-be-detected knowledge graph and acquiring a scanning result;
and generating a monitoring report according to the scanning result.
According to an optional embodiment of the present invention, acquiring the docketing information of the to-be-detected knowledge graph according to the monitoring request includes:
analyzing the name of the knowledge graph to be detected in the monitoring request;
sending the name of the knowledge graph to be detected to a preset terminal;
and receiving the record information which is sent by the preset terminal and corresponds to the name of the knowledge graph to be detected.
According to an alternative embodiment of the present invention, the ranking the content in the plurality of first knowledge bases to obtain a plurality of second knowledge bases comprises:
reading each piece of content in each first knowledge base;
Carrying out semantic analysis on the content to obtain an influence degree;
determining an influence grade corresponding to the influence degree;
ranking the content according to the impact level;
and taking the first knowledge base after content grading as a second knowledge base.
According to an alternative embodiment of the present invention, the structured processing of the plurality of second knowledge bases to obtain the standard monitoring template comprises:
for each grade, reading first data corresponding to the grade in the second knowledge base and storing the first data in a preset data format to obtain second data;
acquiring metadata in the first data, and generating a triple conversion rule based on the metadata;
reading the second data, and respectively matching the second data with the entity types defined in the triple conversion rule, the incidence relation among the entity types and the attributes and attribute values corresponding to the entity types to obtain triple data;
and obtaining a standard monitoring template based on the triple data.
According to an alternative embodiment of the present invention, the generating the triplet conversion rule based on the metadata comprises:
and inputting the meaning of each line of data in the metadata and the relation between each line into a preset rule generating template, and analyzing and outputting a triple conversion rule through the rule generating template.
According to an optional embodiment of the present invention, the invoking the standard monitoring template to scan the to-be-detected knowledge graph and obtain the scanning result includes:
comparing the first content in the standard monitoring template with the corresponding second content in the knowledge graph to be detected line by line;
when the first content is consistent with the second content in comparison, determining that the scanning result is successful;
and when the first content is inconsistent with the second content in comparison, determining that the scanning result is scanning failure.
According to an alternative embodiment of the present invention, the analyzing the scanning result and generating the monitoring report according to the analysis result includes:
for each grade, obtaining scanning results of successful scanning and calculating the number of the scanning results of successful scanning;
calculating a ratio between the number and a total number of the scan results;
comparing whether the ratio is within a preset threshold range;
acquiring target grades corresponding to target ratios which are not within the preset threshold range and target scanning results corresponding to the target grades;
and generating a monitoring report according to the target scanning result.
A second aspect of the invention provides a knowledge-graph monitoring apparatus, operable in a computer device, the apparatus comprising:
The information acquisition module is used for responding to a received monitoring request for monitoring the knowledge graph to be detected and acquiring the record information of the knowledge graph to be detected according to the monitoring request;
the knowledge base configuration module is used for configuring a plurality of first knowledge bases for the knowledge graph to be tested according to the record information;
the content grading module is used for grading the contents in the first knowledge bases to obtain a plurality of second knowledge bases;
the structural processing module is used for carrying out structural processing on the plurality of second knowledge bases to obtain a standard monitoring template;
the map scanning module is used for calling the standard monitoring template to scan the knowledge map to be detected and acquiring a scanning result;
and the report generating module is used for generating a monitoring report according to the scanning result.
A third aspect of the present invention provides a computer apparatus comprising: a memory to store at least one instruction; and the processor is used for realizing the knowledge graph monitoring method when executing the at least one instruction.
A fourth aspect of the present invention provides a computer-readable storage medium having at least one instruction stored therein, where the at least one instruction, when executed by a processor, implements the knowledge graph monitoring method.
According to the method, the record information of the knowledge graph to be detected is obtained, and the knowledge base is configured for the knowledge graph to be detected according to the record information, so that the individualized and customized data configuration process of the knowledge graph is realized, and the accurate monitoring of the knowledge graph to be detected is realized according to the configured knowledge base; the contents in the first knowledge bases are graded and structured to obtain a standard monitoring template, so that the knowledge graph to be detected can be conveniently scanned through the standard monitoring template, the obtained scanning result is more accurate, and the monitoring accuracy is high; and finally, generating a monitoring report in a grading way, so that related personnel of the knowledge graph to be detected can quickly locate the content which does not meet the requirements in the knowledge graph to be detected, and then adjusting and modifying the content in time to improve the service capability of the knowledge graph
Drawings
Fig. 1 is a schematic structural diagram of a computer device according to a first embodiment of the present invention.
Fig. 2 is a flowchart illustrating a knowledge graph monitoring method according to a second embodiment of the present invention.
FIG. 3 is a diagram of a plurality of first knowledge bases according to an embodiment of the invention.
Fig. 4 is a functional block diagram of a knowledge-graph monitoring apparatus according to a third embodiment of the present invention.
Detailed Description
The following description will more fully describe the present disclosure with reference to the accompanying drawings. There is shown in the drawings exemplary embodiments of the invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. These exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Like reference numerals designate identical or similar components.
The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Further, as used herein, the terms "comprises," "comprising," "includes" and/or "including" or "having" and/or "having," integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, regions, integers, steps, operations, elements, components, and/or groups thereof.
Unless defined otherwise, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Furthermore, unless otherwise defined herein, terms such as those defined in commonly used dictionaries should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present invention, and will not be interpreted in an idealized or overly formal sense.
The following description of exemplary embodiments refers to the accompanying drawings. It should be noted that the components depicted in the referenced drawings are not necessarily shown to scale; and the same or similar components will be given the same or similar reference numerals or similar terms.
Fig. 1 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
The computer device 1 for compliance monitoring of knowledge graphs may comprise at least one memory 10, at least one processor 12, at least one communication bus 14, at least one input interface 16 and at least one output interface 18.
In some embodiments, the at least One Memory 10 includes Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically Erasable rewritable Read-Only Memory (EEPROM), compact-disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage, tape storage, or any other medium readable by a computer that can be used to carry or store data.
In some embodiments, the at least one processor 12 may include one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips, among others.
In some embodiments, the at least one communication bus 14 is configured to enable connective communication between the at least one memory 10, the at least one processor 12, the at least one input interface 16, the at least one output interface 18, and the like.
In some embodiments, the at least one input interface may be a graphical user interface.
In some embodiments, the at least one output result is used to output a monitoring result.
The at least one memory 10 has stored therein a computer program of computer readable instructions. The at least one processor 12 may invoke the computer program stored in the at least one memory 10 to perform the associated functions. For example, the various modules illustrated in fig. 4 are computer programs stored in the at least one memory 10 and executed by the at least one processor 12 to implement the functions of the various modules for purposes of knowledgegraph monitoring.
It will be appreciated by those skilled in the art that the configuration of the computer device 1 shown in fig. 1 does not constitute a limitation of the embodiments of the present invention, and may be a bus-type configuration or a star-type configuration, and that the computer device 1 may also include more or less hardware or software than those shown, or a different arrangement of components.
Fig. 2 is a flowchart of a knowledge-graph monitoring method according to a second embodiment of the present invention.
The knowledge graph monitoring method is applied to computer equipment and specifically comprises the following steps, and the sequence of the steps in the flow chart can be changed and some steps can be omitted according to different requirements.
And S11, responding to the received monitoring request for monitoring the knowledge graph to be detected by the computer equipment, and acquiring the record information of the knowledge graph to be detected according to the monitoring request.
Different knowledge maps provide different services and functions, for example, a reception knowledge map provides reception services, a translation knowledge map provides translation functions, a legal consultative knowledge map provides legal consultations and answers, an after-sales knowledge map provides after-sales services, a psychological consultative knowledge map provides psychological consultative services, a financial services knowledge map provides services related to financial services, and the like. When the knowledge graph for providing the service to the outside is on line, the related departments of the country need to be put on record.
In an optional embodiment, the obtaining, by the computer device according to the monitoring request, the filing information of the to-be-detected knowledge-graph includes:
analyzing the name of the knowledge graph to be detected in the monitoring request;
sending the name of the knowledge graph to be detected to a preset terminal;
and receiving the record information which is sent by the preset terminal and corresponds to the name of the knowledge graph to be detected.
The computer device may also provide a communication interface through which to communicatively connect with terminals of the country-related department. When computer equipment receives a monitoring request aiming at a knowledge graph, record information of the knowledge graph is obtained from a terminal of a related department of a country.
In an alternative embodiment, the docketing information may include, but is not limited to: name of the knowledge graph, record number, application range (e.g., financial industry), service field (e.g., financial consultation), service country (e.g., china), service area range (e.g., beijing/shanghai), online time, issuing company, server hosting place, legal system followed and legal detailed list in the legal system, ethical criteria and detailed content followed, country/region and specific content of living habits followed, standard system followed and specific standard list, standard content corresponding to each standard list, and professional ability followed by the knowledge graph.
In other embodiments, the docket information may include the content of diversity and fairness followed by the knowledge-graph, the classification distribution of diversity, and specific data for each classification distribution.
And S12, configuring a plurality of first knowledge bases for the knowledge graph to be tested by the computer equipment according to the record information.
Because the knowledge graph plays the role of professional servers in the life of people at present, and the professional servers are socially known, professionally moral and professional competent people in social life, and the socially known, professionally moral and professional competent people not only comply with laws and regulations, comply with international/national/industrial standards of related industries, comply with ethical moral and professional moral, but also have certain professional competence, when the computer equipment monitors the to-be-detected knowledge graph, the computer equipment firstly accurately configures a knowledge base according to the record information of the to-be-detected knowledge graph, so that the purpose of accurate and personalized monitoring is realized.
Illustratively, as shown in fig. 3, the plurality of first repositories of computer device configurations may include: a legal system first knowledge base 301, a standard system first knowledge base 302, an ethical moral first knowledge base 303, a professional ability first knowledge base 304, a living habit first knowledge base 305 and a religious belief first knowledge base 306. Wherein the ethical first knowledge base 303 comprises a combination of one or more of: medical ethics, moral ethics, scientific ethics. The religious belief knowledge base may include the dawn, the astronomical, the islamic, the Buddhist, and the like.
The first knowledge base is configured differently according to different requirements. The computer device may add additional first repositories, or delete portions of the first repository, or make modifications to the configured first repository.
And S13, the computer device grades the contents in the first knowledge bases to obtain a second knowledge base.
And after the computer equipment completes the configuration of the first knowledge bases according to the record information of the knowledge graph to be detected, grading each piece of content in each first knowledge base, and taking the first knowledge base with graded content as a second knowledge base.
In an alternative embodiment, the computer device ranking the content in the first plurality of knowledge bases to a second plurality of knowledge bases comprises:
reading each piece of content in each first knowledge base;
carrying out semantic analysis on the content to obtain an influence degree;
determining an influence grade corresponding to the influence degree;
ranking the content according to the impact level;
and taking the first knowledge base after content grading as a second knowledge base.
And semantic analysis is carried out on the content to obtain the influence degree violating the severity, the influence degree of harmfulness and the influence degree with bad properties.
The grading of each content in the first knowledge base 301 of the legal system, the first knowledge base 302 of the standard system, the first knowledge base 303 of the ethical moral, the first knowledge base 304 of the professional ability, the first knowledge base 305 of the living habits, and the first knowledge base 306 of the religious belief can be semantically analyzed according to the judicial interpretation of the legal regulations, the necessary content of the standard system, the important ability of the professional ability identification, and the like, so as to obtain the degree of influence. The computer device stores the corresponding relationship between the influence degrees and the influence levels in advance, and the influence level corresponding to each influence degree can be determined according to the corresponding relationship. The greater the severity of the violation, the higher the hazard, and the higher the rating of the content for which the adverse impact level is greater; the less severe the violation, the less hazardous, and the lower the rating of the content for less adverse impact. Each piece of content in each of the first repositories may be divided into N levels, where N is a natural number greater than 1, such as 4, 5, 6, 7.
For example, assume that the content is divided into 6 levels: 5, 4, 3, 2, 1, 0, wherein a level 5 represents the greatest severity, criticality and adverse effect of the violation, a level 1 represents the severity, criticality and adverse effect of the violation being relatively weak, and a level 0 represents the absence of severity, criticality and adverse effect of the violation.
And S14, the computer device structurally processes the plurality of second knowledge bases to obtain standard monitoring templates.
And after the computer equipment grades the contents in the plurality of first knowledge bases, carrying out structural processing on each piece of graded content.
In an alternative embodiment, the computer device structured processing the plurality of second knowledge bases into a standard monitoring template comprises:
for each grade, reading first data corresponding to the grade in the second knowledge base and storing the first data in a preset data format to obtain second data;
acquiring metadata in the first data, and generating a triple conversion rule based on the metadata;
reading the second data, and respectively matching the second data with the entity types defined in the triple conversion rule, the incidence relation among the entity types and the attributes and attribute values corresponding to the entity types to obtain triple data;
and obtaining a standard monitoring template based on the triple data.
In order to understand the data in the second database and the association relationship between the data, the computer device adopts a Resource Description Framework (RDF) to describe the data. The basic idea of RDF is: (1) everything that can be identified on the Web (concrete or abstract, present or not) is collectively called a "resource"; (2) identifying the Resource by a URI (Universal Resource Identifier); (3) a resource is described by a property (property) and a property value. The basic structure of any expression in RDF is a set of triples, each of which consists of a subject, a predicate, and an object. A subject corresponds to a resource, and is anything that can own a URI. The standard monitoring templates generated by the computer device through the RDF technique are actually standardized knowledge maps.
The computer equipment stores the data in a preset data format, so that the subsequent processing of the data in the second database does not need to consider the difference of formats, and the conversion method of the ternary group of data is simplified.
The metadata is data describing data in the second database. The computer device can automatically analyze the data in the second database through a pre-developed data analysis tool to obtain the metadata of the data in the second database.
The computer device may structure each piece of content of the hierarchy from strong to weak, or from weak to strong, according to the severity of the hierarchy. For example, assuming that the computer device divides the content in the first knowledge base into 6 levels, the order of structuring each piece of content of the levels may be: stage 5, stage 4, stage 3, stage 2, stage 1, and stage 0. That is, the 5-level structuring process is completed first, then the 4-level content is structured, and then the 3-level, 2-level, 1-level, and 0-level content are structured, respectively. Or, the order of structuring each piece of content in the hierarchy is: level 0, level 1, level 2, level 3, level 4, level 5. That is, the level 0 structuring process is completed first, then the level 1 content is structured, and then the level 2, level 3, level 4, and level 5 content are structured, respectively.
In an alternative embodiment, the computer device generating a triplet transformation rule based on the metadata comprises:
and inputting the meaning of each line of data in the metadata and the relation between each line into a preset rule generating template, and analyzing and outputting a triple conversion rule through the rule generating template.
The computer device may generate a rule generating template in advance, input the meaning of each line of data in the second database included in the metadata and the relationship between each line to the rule generating template, and obtain the triplet conversion rule after analyzing the input content by the rule generating template.
And S15, the computer equipment calls the standard monitoring template to scan the knowledge graph to be detected and acquire a scanning result.
And after the computer equipment carries out structural processing on the plurality of second knowledge bases, carrying out matching scanning in the knowledge graph to be detected according to the structured content.
In an optional embodiment, the step of the computer device invoking the standard monitoring template to scan the to-be-detected knowledge graph and obtain the scanning result includes:
comparing the first content in the standard monitoring template with the corresponding second content in the knowledge graph to be detected line by line;
When the first content is consistent with the second content in comparison, determining that the scanning result is successful;
and when the first content is inconsistent with the second content in comparison, determining that the scanning result is scanning failure.
In this optional embodiment, the computer device determines whether the to-be-detected knowledge graph is in compliance by comparing the contents of the standard monitoring template and the to-be-detected knowledge graph one by one.
In an optional embodiment, after calling the standard monitoring template to scan the to-be-detected knowledge graph and obtain a scanning result, the computer device performs block marking storage on the scanning result according to a preset table.
And S16, the computer equipment generates a monitoring report according to the scanning result.
The monitoring report mainly comprises: alarm display, statistical analysis and log recording.
In some embodiments, the computer device may generate the monitoring report in a hierarchy of the severity of violation, the harmfulness, and the extent of adverse effects of legal regulations, ethics, standards bodies, occupational abilities, religious beliefs, and living habits, among others, in the first knowledge base.
In some optional embodiments, the generating, by the computer device, a monitoring report according to the scan result comprises:
for each grade, obtaining scanning results of successful scanning and calculating the number of the scanning results of successful scanning;
calculating a ratio between the number and a total number of the scan results;
comparing whether the ratio is within a preset threshold range;
acquiring target grades corresponding to target ratios which are not within the preset threshold range and target scanning results corresponding to the target grades;
and generating a monitoring report according to the target scanning result.
Wherein the preset threshold range may be (0.95, 1).
When the ratio corresponding to the scanning result after a certain classification is within the preset threshold range, the scanning result after the classification is in accordance with the requirement; and when the ratio corresponding to the scanning result after a certain grading is not in the preset threshold range, the scanning result after the grading is not qualified. The closer the ratio corresponding to the scanning result after a certain classification is to the upper limit value of the preset threshold range, the better the scanning result after the classification is.
And listing the target grades corresponding to the target ratios which are not in the preset threshold range and the scanning results corresponding to the target grades, so that the relevant personnel of the knowledge graph to be tested can improve the target grades.
In the knowledge graph monitoring method in this embodiment, the individualized and customized data configuration process of the knowledge graph is realized by acquiring the filing information of the to-be-detected knowledge graph and configuring the knowledge base for the to-be-detected knowledge graph according to the filing information, so that the accurate monitoring of the to-be-detected knowledge graph is realized according to the configured knowledge base; the contents in the first knowledge bases are graded and structured to obtain a standard monitoring template, so that the knowledge graph to be detected can be conveniently scanned through the standard monitoring template, the obtained scanning result is more accurate, and the monitoring accuracy is high; and finally, generating a monitoring report in a grading manner, so that related personnel of the knowledge graph to be detected can quickly locate the content which does not meet the requirement in the knowledge graph to be detected, and then adjusting and modifying the content in time to improve the service capability of the knowledge graph.
Fig. 4 is a functional block diagram of a knowledge-graph monitoring apparatus according to a third embodiment of the present invention.
The knowledge-graph monitoring apparatus 40 may include a plurality of functional modules comprised of computer program segments. The computer program of each program segment in the knowledge-graph monitoring apparatus 40 may be stored in a memory of a computer device and executed by at least one processor of the computer device to perform the functions of the knowledge-graph monitoring method.
In this embodiment, the knowledge-graph monitoring apparatus 40 may be divided into a plurality of functional modules according to the functions performed by the apparatus. The functional module may include: an information acquisition module 401, a knowledge base configuration module 402, a content rating module 403, a structuring processing module 404, a map scanning module 405, and a report generation module 406. The module referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in memory. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.
The information obtaining module 401 is configured to, in response to a received monitoring request for monitoring a to-be-detected knowledge graph, a computer device obtain filing information of the to-be-detected knowledge graph according to the monitoring request.
Different knowledge maps provide different services and functions, for example, a reception knowledge map provides reception services, a translation knowledge map provides translation functions, a legal consultative knowledge map provides legal consultations and answers, an after-sales knowledge map provides after-sales services, a psychological consultative knowledge map provides psychological consultative services, a financial services knowledge map provides services related to financial services, and the like. When the knowledge graph for providing the service to the outside is on line, the related departments of the country need to be put on record.
In an optional embodiment, the obtaining, by the information obtaining module 401, the filing information of the to-be-detected knowledge graph according to the monitoring request includes:
analyzing the name of the knowledge graph to be detected in the monitoring request;
sending the name of the knowledge graph to be detected to a preset terminal;
and receiving the record information which is sent by the preset terminal and corresponds to the name of the knowledge graph to be detected.
The computer device may also provide a communication interface through which to communicatively connect with terminals of the country-related department. When computer equipment receives a monitoring request aiming at a knowledge graph, record information of the knowledge graph is obtained from a terminal of a related department of a country.
In an alternative embodiment, the docketing information may include, but is not limited to: name of the knowledge graph, record number, application range (e.g., financial industry), service field (e.g., financial consultation), service country (e.g., china), service area range (e.g., beijing/shanghai), online time, issuing company, server hosting place, legal system followed and legal detailed list in the legal system, ethical criteria and detailed content followed, country/region and specific content of living habits followed, standard system followed and specific standard list, standard content corresponding to each standard list, and professional ability followed by the knowledge graph.
In other embodiments, the docket information may include the content of diversity and fairness followed by the knowledge-graph, the classification distribution of diversity, and specific data for each classification distribution.
The knowledge base configuring module 402 is configured to configure a plurality of first knowledge bases for the to-be-detected knowledge graph according to the record information.
Because the knowledge graph plays the role of professional servers in the life of people at present, and the professional servers are socially known, professionally moral and professional competent people in social life, and the socially known, professionally moral and professional competent people not only comply with laws and regulations, comply with international/national/industrial standards of related industries, comply with ethical moral and professional moral, but also have certain professional competence, when the computer equipment monitors the to-be-detected knowledge graph, the computer equipment firstly accurately configures a knowledge base according to the record information of the to-be-detected knowledge graph, so that the purpose of accurate and personalized monitoring is realized.
Illustratively, as shown in fig. 3, the plurality of first repositories of computer device configurations may include: a legal system first knowledge base 301, a standard system first knowledge base 302, an ethical moral first knowledge base 303, a professional ability first knowledge base 304, a living habit first knowledge base 305 and a religious belief first knowledge base 306. Wherein the ethical first knowledge base 303 comprises a combination of one or more of: medical ethics, moral ethics, scientific ethics. The religious belief knowledge base may include the dawn, the astronomical, the islamic, the Buddhist, and the like.
The first knowledge base is configured differently according to different requirements. The computer device may add additional first repositories, or delete portions of the first repository, or make modifications to the configured first repository.
The content grading module 403 is configured to grade the content in the multiple first knowledge bases to obtain multiple second knowledge bases.
And after the computer equipment completes the configuration of the first knowledge bases according to the record information of the knowledge graph to be detected, grading each piece of content in each first knowledge base, and taking the first knowledge base with graded content as a second knowledge base.
In an alternative embodiment, the content ranking module 403 ranks the content in the first knowledge bases to obtain a second knowledge base, including:
reading each piece of content in each first knowledge base;
carrying out semantic analysis on the content to obtain an influence degree;
determining an influence grade corresponding to the influence degree;
ranking the content according to the impact level;
and taking the first knowledge base after content grading as a second knowledge base.
And semantic analysis is carried out on the content to obtain the influence degree violating the severity, the influence degree of harmfulness and the influence degree with bad properties.
The grading of each content in the first knowledge base 301 of the legal system, the first knowledge base 302 of the standard system, the first knowledge base 303 of the ethical moral, the first knowledge base 304 of the professional ability, the first knowledge base 305 of the living habits, and the first knowledge base 306 of the religious belief can be semantically analyzed according to the judicial interpretation of the legal regulations, the necessary content of the standard system, the important ability of the professional ability identification, and the like, so as to obtain the degree of influence. The computer device stores the corresponding relationship between the influence degrees and the influence levels in advance, and the influence level corresponding to each influence degree can be determined according to the corresponding relationship. The greater the severity of the violation, the higher the hazard, and the higher the rating of the content for which the adverse impact level is greater; the less severe the violation, the less hazardous, and the lower the rating of the content for less adverse impact. Each piece of content in each of the first repositories may be divided into N levels, where N is a natural number greater than 1, such as 4, 5, 6, 7.
For example, assume that the content is divided into 6 levels: 5, 4, 3, 2, 1, 0, wherein a level 5 represents the greatest severity, criticality and adverse effect of the violation, a level 1 represents the severity, criticality and adverse effect of the violation being relatively weak, and a level 0 represents the absence of severity, criticality and adverse effect of the violation.
The structural processing module 404 is configured to structurally process the plurality of second knowledge bases to obtain a standard monitoring template.
And after the computer equipment grades the contents in the plurality of first knowledge bases, carrying out structural processing on each piece of graded content.
In an alternative embodiment, the structural processing module 404 structural processing the plurality of second knowledge bases to obtain the standard monitoring template includes:
for each grade, reading first data corresponding to the grade in the second knowledge base and storing the first data in a preset data format to obtain second data;
acquiring metadata in the first data, and generating a triple conversion rule based on the metadata;
reading the second data, and respectively matching the second data with the entity types defined in the triple conversion rule, the incidence relation among the entity types and the attributes and attribute values corresponding to the entity types to obtain triple data;
and obtaining a standard monitoring template based on the triple data.
In order to understand the data in the second database and the association relationship between the data, the computer device adopts a Resource Description Framework (RDF) to describe the data. The basic idea of RDF is: (1) everything that can be identified on the Web (concrete or abstract, present or not) is collectively called a "resource"; (2) identifying the Resource by a URI (Universal Resource Identifier); (3) a resource is described by a property (property) and a property value. The basic structure of any expression in RDF is a set of triples, each of which consists of a subject, a predicate, and an object. A subject corresponds to a resource, and is anything that can own a URI. The standard monitoring templates generated by the computer device through the RDF technique are actually standardized knowledge maps.
The computer equipment stores the data in a preset data format, so that the subsequent processing of the data in the second database does not need to consider the difference of formats, and the conversion method of the ternary group of data is simplified.
The metadata is data describing data in the second database. The computer device can automatically analyze the data in the second database through a pre-developed data analysis tool to obtain the metadata of the data in the second database.
The computer device may structure each piece of content of the hierarchy from strong to weak, or from weak to strong, according to the severity of the hierarchy. For example, assuming that the computer device divides the content in the first knowledge base into 6 levels, the order of structuring each piece of content of the levels may be: stage 5, stage 4, stage 3, stage 2, stage 1, and stage 0. That is, the 5-level structuring process is completed first, then the 4-level content is structured, and then the 3-level, 2-level, 1-level, and 0-level content are structured, respectively. Or, the order of structuring each piece of content in the hierarchy is: level 0, level 1, level 2, level 3, level 4, level 5. That is, the level 0 structuring process is completed first, then the level 1 content is structured, and then the level 2, level 3, level 4, and level 5 content are structured, respectively.
In an alternative embodiment, the computer device generating a triplet transformation rule based on the metadata comprises:
and inputting the meaning of each line of data in the metadata and the relation between each line into a preset rule generating template, and analyzing and outputting a triple conversion rule through the rule generating template.
The computer device may generate a rule generating template in advance, input the meaning of each line of data in the second database included in the metadata and the relationship between each line to the rule generating template, and obtain the triplet conversion rule after analyzing the input content by the rule generating template.
The map scanning module 405 is configured to invoke the standard monitoring template to scan the to-be-detected knowledge map and obtain a scanning result.
And after the computer equipment carries out structural processing on the plurality of second knowledge bases, carrying out matching scanning in the knowledge graph to be detected according to the structured content.
In an optional embodiment, the invoking of the standard monitoring template by the atlas scanning module 405 to scan the to-be-detected knowledge atlas and obtain the scanning result includes:
comparing the first content in the standard monitoring template with the corresponding second content in the knowledge graph to be detected line by line;
When the first content is consistent with the second content in comparison, determining that the scanning result is successful;
and when the first content is inconsistent with the second content in comparison, determining that the scanning result is scanning failure.
In this optional embodiment, the computer device determines whether the to-be-detected knowledge graph is in compliance by comparing the contents of the standard monitoring template and the to-be-detected knowledge graph one by one.
In an optional embodiment, after calling the standard monitoring template to scan the to-be-detected knowledge graph and obtain a scanning result, the computer device performs block marking storage on the scanning result according to a preset table.
The report generating module 406 is configured to generate a monitoring report according to the scanning result.
The monitoring report mainly comprises: alarm display, statistical analysis and log recording.
In some embodiments, the computer device may generate the monitoring report in a hierarchy of the severity of violation, the harmfulness, and the extent of adverse effects of legal regulations, ethics, standards bodies, occupational abilities, religious beliefs, and living habits, among others, in the first knowledge base.
In some optional embodiments, the report generating module 406 generates the monitoring report according to the scanning result, including:
for each grade, obtaining scanning results of successful scanning and calculating the number of the scanning results of successful scanning;
calculating a ratio between the number and a total number of the scan results;
comparing whether the ratio is within a preset threshold range;
acquiring target grades corresponding to target ratios which are not within the preset threshold range and target scanning results corresponding to the target grades;
and generating a monitoring report according to the target scanning result.
Wherein the preset threshold range may be (0.95, 1).
When the ratio corresponding to the scanning result after a certain classification is within the preset threshold range, the scanning result after the classification is in accordance with the requirement; and when the ratio corresponding to the scanning result after a certain grading is not in the preset threshold range, the scanning result after the grading is not qualified. The closer the ratio corresponding to the scanning result after a certain classification is to the upper limit value of the preset threshold range, the better the scanning result after the classification is.
And listing the target grades corresponding to the target ratios which are not in the preset threshold range and the scanning results corresponding to the target grades, so that the relevant personnel of the knowledge graph to be tested can improve the target grades.
The knowledge graph monitoring device in the embodiment realizes the individualized and customized data configuration process of the knowledge graph by acquiring the record information of the knowledge graph to be detected and configuring the knowledge base for the knowledge graph to be detected according to the record information, so that the precise monitoring of the knowledge graph to be detected is realized according to the configured knowledge base; the contents in the first knowledge bases are graded and structured to obtain a standard monitoring template, so that the knowledge graph to be detected can be conveniently scanned through the standard monitoring template, the obtained scanning result is more accurate, and the monitoring accuracy is high; and finally, generating a monitoring report in a grading manner, so that related personnel of the knowledge graph to be detected can quickly locate the content which does not meet the requirement in the knowledge graph to be detected, and then adjusting and modifying the content in time to improve the service capability of the knowledge graph.
Embodiments of the present invention further provide a computer-readable storage medium, where at least one instruction is stored, and when executed by a processor, the at least one instruction implements all or part of the steps of the knowledge graph monitoring method, for example, the steps S11-S16 described above. Alternatively, the at least one instruction, when executed by the processor, implements all or part of the functions of the apparatus for monitoring a knowledge graph, for example, the module 401 and 406.
In the embodiments provided in the present invention, 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, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. 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 invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a hardware mode, and can also be realized in a mode of hardware and a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the apparatus claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A knowledge graph monitoring method applied to computer equipment is characterized by comprising the following steps:
responding to a received monitoring request for monitoring the knowledge graph to be detected, and acquiring the record information of the knowledge graph to be detected according to the monitoring request;
configuring a plurality of first knowledge bases for the knowledge graph to be tested according to the record information;
grading the contents in the plurality of first knowledge bases to obtain a plurality of second knowledge bases;
structuring the plurality of second knowledge bases to obtain standard monitoring templates;
calling the standard monitoring template to scan the to-be-detected knowledge graph and acquiring a scanning result;
and generating a monitoring report according to the scanning result.
2. The knowledge graph monitoring method of claim 1, wherein obtaining docketing information of the knowledge graph to be tested according to the monitoring request comprises:
Analyzing the name of the knowledge graph to be detected in the monitoring request;
sending the name of the knowledge graph to be detected to a preset terminal;
and receiving the record information which is sent by the preset terminal and corresponds to the name of the knowledge graph to be detected.
3. The knowledge graph monitoring method of claim 1, wherein ranking the content in the plurality of first knowledge bases to obtain a plurality of second knowledge bases comprises:
reading each piece of content in each first knowledge base;
carrying out semantic analysis on the content to obtain an influence degree;
determining an influence grade corresponding to the influence degree;
ranking the content according to the impact level;
and taking the first knowledge base after content grading as a second knowledge base.
4. The knowledge graph monitoring method of claim 3, wherein the structured processing of the plurality of second knowledge bases to obtain a standard monitoring template comprises:
for each grade, reading first data corresponding to the grade in the second knowledge base and storing the first data in a preset data format to obtain second data;
acquiring metadata in the first data, and generating a triple conversion rule based on the metadata;
Reading the second data, and respectively matching the second data with the entity types defined in the triple conversion rule, the incidence relation among the entity types and the attributes and attribute values corresponding to the entity types to obtain triple data;
and obtaining a standard monitoring template based on the triple data.
5. The knowledge graph monitoring method of claim 4, wherein the generating a triplet transformation rule based on the metadata comprises:
and inputting the meaning of each line of data in the metadata and the relation between each line into a preset rule generating template, and analyzing and outputting a triple conversion rule through the rule generating template.
6. The knowledge graph monitoring method according to any one of claims 3 to 5, wherein the calling the standard monitoring template to scan the knowledge graph to be tested and obtain the scanning result comprises:
comparing the first content in the standard monitoring template with the corresponding second content in the knowledge graph to be detected line by line;
when the first content is consistent with the second content in comparison, determining that the scanning result is successful;
and when the first content is inconsistent with the second content in comparison, determining that the scanning result is scanning failure.
7. The knowledge profile monitoring method of claim 6, wherein the analyzing the scan results and generating a monitoring report according to the analysis results comprises:
for each grade, obtaining scanning results of successful scanning and calculating the number of the scanning results of successful scanning;
calculating a ratio between the number and a total number of the scan results;
comparing whether the ratio is within a preset threshold range;
acquiring target grades corresponding to target ratios which are not within the preset threshold range and target scanning results corresponding to the target grades;
and generating a monitoring report according to the target scanning result.
8. A knowledge-graph monitoring apparatus, operable in a computer device, the apparatus comprising:
the information acquisition module is used for responding to a received monitoring request for monitoring the knowledge graph to be detected and acquiring the record information of the knowledge graph to be detected according to the monitoring request;
the knowledge base configuration module is used for configuring a plurality of first knowledge bases for the knowledge graph to be tested according to the record information;
the content grading module is used for grading the contents in the first knowledge bases to obtain a plurality of second knowledge bases;
The structural processing module is used for carrying out structural processing on the plurality of second knowledge bases to obtain a standard monitoring template;
the map scanning module is used for calling the standard monitoring template to scan the knowledge map to be detected and acquiring a scanning result;
and the report generating module is used for generating a monitoring report according to the scanning result.
9. A computer device, characterized in that the computer device comprises:
a memory to store at least one instruction;
a processor configured to implement the knowledge graph monitoring method of any one of claims 1 to 7 when executing the at least one instruction.
10. A computer-readable storage medium having stored therein at least one instruction which, when executed by a processor, implements a knowledge graph monitoring method as claimed in any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113408738A (en) * | 2021-05-17 | 2021-09-17 | 桂林电子科技大学 | Method for designing ethical agent based on reinforcement learning |
| WO2024027071A1 (en) * | 2022-08-01 | 2024-02-08 | 中电科大数据研究院有限公司 | Data monitoring method and system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150379414A1 (en) * | 2014-06-27 | 2015-12-31 | Nuance Communications, Inc. | Utilizing large-scale knowledge graphs to support inference at scale and explanation generation |
| CN110019824A (en) * | 2017-07-20 | 2019-07-16 | 苏州狗尾草智能科技有限公司 | Man-machine interaction method, the apparatus and system of knowledge based map |
| CN110704634A (en) * | 2019-09-06 | 2020-01-17 | 平安科技（深圳）有限公司 | Method and device for checking and repairing knowledge graph link errors and storage medium |
| CN110704635A (en) * | 2019-09-16 | 2020-01-17 | 金色熊猫有限公司 | Conversion method and device for ternary group data in knowledge graph |
| CN110727804A (en) * | 2019-10-11 | 2020-01-24 | 北京明略软件系统有限公司 | Method and device for processing maintenance case by using knowledge graph and electronic equipment |
| CN111275277A (en) * | 2018-12-05 | 2020-06-12 | 北京国双科技有限公司 | Method and device for monitoring administrative element structure |
-
2020
- 2020-06-23 CN CN202010584068.4A patent/CN111858236B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150379414A1 (en) * | 2014-06-27 | 2015-12-31 | Nuance Communications, Inc. | Utilizing large-scale knowledge graphs to support inference at scale and explanation generation |
| CN110019824A (en) * | 2017-07-20 | 2019-07-16 | 苏州狗尾草智能科技有限公司 | Man-machine interaction method, the apparatus and system of knowledge based map |
| CN111275277A (en) * | 2018-12-05 | 2020-06-12 | 北京国双科技有限公司 | Method and device for monitoring administrative element structure |
| CN110704634A (en) * | 2019-09-06 | 2020-01-17 | 平安科技（深圳）有限公司 | Method and device for checking and repairing knowledge graph link errors and storage medium |
| CN110704635A (en) * | 2019-09-16 | 2020-01-17 | 金色熊猫有限公司 | Conversion method and device for ternary group data in knowledge graph |
| CN110727804A (en) * | 2019-10-11 | 2020-01-24 | 北京明略软件系统有限公司 | Method and device for processing maintenance case by using knowledge graph and electronic equipment |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113408738A (en) * | 2021-05-17 | 2021-09-17 | 桂林电子科技大学 | Method for designing ethical agent based on reinforcement learning |
| WO2024027071A1 (en) * | 2022-08-01 | 2024-02-08 | 中电科大数据研究院有限公司 | Data monitoring method and system |
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|---|---|
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