CN115374939A - Expert knowledge base construction method based on multi-label dynamic update - Google Patents
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Abstract
The invention discloses an expert knowledge base construction method based on multi-label dynamic update, which comprises the following steps: the method comprises the steps of S1, firstly determining names of experts in an expert knowledge base, determining the last updating time point, S2, regularly acquiring dynamic expert information from the whole network according to keywords, cleaning information data, S3, classifying the data according to time, and classifying the data according to the experts, wherein through continuous mining and acquisition of the data of the expert knowledge base, historical information of the experts in the expert knowledge base is continuously perfected, the data is corrected, data omission is reduced, newly generated data are continuously increased, the data information in the expert knowledge base is richer, dynamic timeline is set for the experts, important information and tags are displayed, and the tags are continuously updated, so that a complete knowledge base is constructed, more comprehensive and more practical data can be searched when people use the expert knowledge base, and the use efficiency of the expert knowledge base is improved.
Description
Technical Field
The invention relates to the technical field of expert knowledge bases, in particular to an expert knowledge base construction method based on multi-label dynamic updating.
Background
The expert knowledge base is one of the cores of the expert system, has the main function of storing and managing the knowledge in the expert system, and mainly comprises the knowledge from books and the experience knowledge obtained by experts in each field in long-term working practice; however, the existing expert knowledge base is old and cannot be reasonably updated, more repeated data often appears, some more important data are omitted, the knowledge base is incomplete, and the utilization rate is reduced.
Disclosure of Invention
The invention provides a multi-label dynamic update-based expert knowledge base construction method, which can effectively solve the problems that the existing expert knowledge base data in the background technology is old and cannot be reasonably updated, more repeated data often appears, some more important data are omitted, the knowledge base is incomplete, and the utilization rate is reduced.
In order to achieve the purpose, the invention provides the following technical scheme: an expert knowledge base construction method based on multi-label dynamic update comprises the following steps:
s1, firstly, determining a name of a specialist in an expert knowledge base, and determining a last updating time point;
s2, acquiring expert dynamic information from the whole network periodically according to the keywords, and cleaning information data;
s3, classifying the data according to time, and then classifying the data according to experts;
s4, respectively extracting keywords in the data, extracting data relations and events, processing the data, and adding required updated data into an expert knowledge base;
s5, extracting important information of each expert in the knowledge base, and determining the labels of the experts according to the key information;
and S6, generating the dynamic label from the important expert information and the expert label according to a time line.
According to the technical scheme, in the S1, the latest updating time point of the knowledge base is recorded as the last updating time point, and the time point of regularly acquiring the dynamic information is recorded as the current updating time point.
According to the technical scheme, in the S2, the keywords obtained by the information comprise expert names, expert fields, expert areas and expert special titles; the acquired information is all the information of the expert.
According to the technical scheme, in the step S3, the time points in the time point classification include an initial time point, a last update time point, and a current update time point; recording data between the initial time point and the last updating time point as data to be compared;
recording the data before the initial time point as data to be verified; and recording the data between the last updating time point and the current updating time point as new data.
According to the technical scheme, in the S4, the keywords, the relations and the events in the data to be compared are extracted, the corresponding keywords, the relations and the events are searched from the original knowledge base, the data to be compared and the data in the original knowledge base are compared, and the data repetition condition is determined; finding out data with difference between the original data and the data to be compared, wherein the difference data comprises modification, addition and deletion of the original data; and finding out the position of the difference data corresponding to the original data, backing up the original data, and replacing the data at the position with the difference data to update the data in the original data.
According to the technical scheme, in the S4, the data to be verified are analyzed through big data, the authenticity of the data before the time point is determined, and then the data are added; when the authenticity of the data is determined, sequentially determining that experts, time and events corresponding to the data accord with the data of the original knowledge base, searching a data source through big data, determining that the data and the source are both true, and adding the data to be verified into the expert knowledge base; classifying the newly added data according to experts, sequencing data corresponding to each expert according to a time sequence, extracting a label of the data corresponding to each expert, searching in the original knowledge base data through the label, and determining the newly added label in the newly added data; and adding the newly added data into the expert knowledge base.
According to the technical scheme, in the S5, when information is extracted for the first time, all data in the knowledge base are extracted as objects; and extracting important data once after the knowledge base data is updated every time, wherein the extracted objects are all updated data after the knowledge base data is updated every time.
According to the technical scheme, in the S5, when a new expert label is determined, the new expert label is compared with all original labels to determine repetition and similar conditions; if the new expert label is repeated with the original label, the label is determined again; and when the new expert label is similar to the original label, analyzing the data difference corresponding to the two labels, and noting the difference of the two similar labels at the position of the label.
According to the technical scheme, in the S6, the dynamic label is updated every time the expert knowledge base is updated;
each expert corresponds to a time line, a corresponding label is arranged above the time line, and important information of the experts is arranged below the time line.
According to the technical scheme, in the S6, expert names, expert photos and expert fields are arranged at the starting end of each time line, the expert time lines in the same field are arranged together, then all the time lines are arranged, a single time line is transversely arranged, and all the time lines are longitudinally arranged; the arrangement order of the expert timelines in the same field is arranged from high to low according to the number of times the expert timelines are viewed.
Compared with the prior art, the invention has the beneficial effects that:
1. by acquiring the expert information, classifying the expert information, dividing the data into data to be verified, data to be compared and newly-added data, extracting keywords, data relations and events in the expert information, performing different processing on different data, determining the data adding condition, adding the updated data into the expert knowledge base, ensuring that the data in the expert knowledge base can be updated regularly, and ensuring that the updated data is not easy to repeat and omit, the data updating degree and accuracy in the expert knowledge base are improved.
2. Through the extraction to expert's information, with the dynamic time line of expert's important information generation to add corresponding label, make things convenient for people to the grasp of expert's information, can pass through the clear understanding expert of label, and when updating expert knowledge base to the label on the time line update, make data more comprehensive, the expert information is introduced to more perfect, make things convenient for people to seek the information that needs.
3. The method has the advantages that data before the last updating time are obtained and classified according to time, a knowledge base can be continuously enriched, the historical data mining efficiency is improved, the method is not limited to data generated by updating, historical information of experts can be continuously perfected, the knowledge base of the data is richer, wrong parts in past historical data can be corrected, and missing parts can be supplemented. Through constantly excavating and obtaining the expert knowledge base data, the historical information of experts in the expert knowledge base is constantly improved, the data is corrected, data omission is reduced, newly generated data are constantly increased, the data information in the expert knowledge base is richer, dynamic time lines are set for the experts, important information and tags are displayed, the tags are constantly updated, a complete knowledge base is built, more comprehensive and more practical data can be looked up when people use the knowledge base, and the use efficiency of the expert knowledge base is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
In the drawings:
FIG. 1 is a flow chart of the steps of expert knowledge base construction of the present invention;
FIG. 2 is a schematic diagram of the time point division of the present invention;
FIG. 3 is a schematic diagram of the structure of a single expert timeline of the present invention;
FIG. 4 is a diagram of an arrangement of an overall expert timeline of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Example (b): as shown in fig. 1 to 4, the present invention provides a technical solution, a method for constructing an expert knowledge base based on multi-label dynamic update, comprising the following steps:
s1, firstly, determining a name of a specialist in an expert knowledge base, and determining a last updating time point;
s2, acquiring expert dynamic information from the whole network regularly according to the keywords, and cleaning information data;
s3, classifying the data according to time, and then classifying the data according to experts;
s4, extracting keywords in the data, extracting data relations and events, processing the data, and adding required updated data into an expert knowledge base;
s5, extracting important information of each expert in the knowledge base, and determining the labels of the experts according to the key information;
and S6, generating the dynamic label from the important expert information and the expert label according to a time line.
According to the technical scheme, in S1, the latest updating time point of the knowledge base is recorded as the last updating time point, and the time point of regularly acquiring the dynamic information is recorded as the current updating time point.
According to the technical scheme, in S2, the keywords obtained by the information comprise the names of the experts, the fields to which the experts belong, the areas to which the experts belong and the special titles of the experts; the acquired information is all the information of the expert.
According to the technical scheme, in S3, the time points in the time point classification comprise an initial time point, a last updating time point and a current updating time point; recording data between the initial time point and the last updating time point as data to be compared;
recording data before the initial time point as data to be verified; and recording the data between the last updating time point and the current updating time point as new data.
By acquiring the expert information, classifying the expert information, dividing the data into data to be confirmed, data to be compared and newly-added data, extracting keywords, data relations and events in the expert information, performing different processing on different data, determining the data adding condition, adding the updated data into the expert knowledge base, ensuring that the data in the expert knowledge base can be updated regularly, and the updated data is not easy to repeat and omit.
According to the technical scheme, in S4, extracting keywords, relations and events in the data to be compared, searching corresponding keywords, relations and events from an original knowledge base, comparing the data to be compared with the data in the original knowledge base, and determining the data repetition condition;
finding out data with difference between the original data and the data to be compared, wherein the difference data comprises modification, addition and deletion of the original data;
and finding out the position of the difference data corresponding to the original data, backing up the original data, and replacing the data at the position with the difference data to update the data in the original data.
According to the technical scheme, in S4, the data to be confirmed are analyzed through big data, the authenticity of the data before the time point is determined, and then the data are added;
when the authenticity of the data is determined, sequentially determining that experts, time and events corresponding to the data accord with the data of the original knowledge base, searching a data source through big data, determining that the data and the source are both true, and adding the data to be verified into the expert knowledge base; the method has the advantages that the data before the last updating time are obtained and are classified according to time, so that the knowledge base can be continuously enriched, the historical data mining efficiency is improved, the method is not limited to the data generated by updating, the historical information of experts can be continuously improved, and the knowledge base of the data is richer.
Classifying the newly added data according to experts, sequencing data corresponding to each expert according to a time sequence, extracting a label of the data corresponding to each expert, searching in the original knowledge base data through the label, and determining the newly added label in the newly added data; and adding the newly added data into the expert knowledge base.
According to the technical scheme, in S5, when information is extracted for the first time, all data in the knowledge base are extracted as objects;
and extracting important data once after the knowledge base data is updated every time, wherein the extracted objects are all updated data after the knowledge base data is updated every time.
According to the technical scheme, in S5, when a new expert label is determined, the new expert label is compared with all original labels to determine repetition and similar conditions;
if the new expert label is repeated with the original label, the label is determined again;
and when the new expert label is similar to the original label, analyzing the data difference corresponding to the two labels, and noting the difference of the two similar labels at the position of the label.
According to the technical scheme, in S6, the dynamic label is updated when the expert knowledge base is updated each time;
each expert corresponds to a time line, a corresponding label is arranged above the time line, and important information of the experts is arranged below the time line.
According to the technical scheme, in S6, expert names, expert photos and expert fields are arranged at the starting end of each time line, the expert time lines in the same field are arranged together, then all the time lines are arranged, a single time line is arranged transversely, and all the time lines are arranged longitudinally;
the arrangement order of the expert timelines in the same field is arranged from high to low according to the number of times the expert timelines are viewed.
Through the extraction to expert's information, with the dynamic time line of expert's important information generation to add corresponding label, make things convenient for people to the grasp of expert's information, can pass through the clear understanding expert of label, and when updating expert's knowledge base the label on the time line is updated, make data more comprehensive, the expert's information is introduced more completely.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described above, or equivalents may be substituted for elements thereof. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A multi-label dynamic update based expert knowledge base construction method is characterized by comprising the following steps: the method comprises the following steps:
s1, firstly, determining a name of a specialist in an expert knowledge base, and determining a last updating time point;
s2, acquiring expert dynamic information from the whole network regularly according to the keywords, and cleaning information data;
s3, classifying the data according to time, and then classifying the data according to experts;
s4, respectively extracting keywords in the data, extracting data relations and events, processing the data, and adding required updated data into an expert knowledge base;
s5, extracting important information of each expert in the knowledge base, and determining the labels of the experts according to the key information;
and S6, generating the dynamic label from the important expert information and the expert label according to a time line.
2. The expert knowledge base construction method based on multi-label dynamic update as claimed in claim 1, wherein in S1, the latest update time point of the knowledge base is recorded as the last update time point, and the time point at which the dynamic information is periodically acquired is recorded as the current update time point.
3. The expert knowledge base construction method based on multi-label dynamic update as claimed in claim 1, wherein in S2, the keywords obtained by information include expert names, expert fields, expert regions and expert special titles; the acquired information is all the information of the expert.
4. The expert knowledge base construction method based on multi-label dynamic update of claim 3, wherein in S3, the time points in the time point classification include an initial time point, a last update time point and a current update time point;
recording data between the initial time point and the last updating time point as data to be compared;
recording the data before the initial time point as data to be verified;
and recording the data between the last updating time point and the current updating time point as new data.
5. The expert knowledge base construction method based on multi-label dynamic update as claimed in claim 4, wherein in S4, the keywords, relationships and events in the data to be compared are extracted, the corresponding keywords, relationships and events are searched from the original knowledge base, the data to be compared is compared with the data in the original knowledge base, and the data repetition condition is determined;
finding out data with difference between the original data and the data to be compared, wherein the difference data comprises modification, addition and deletion of the original data;
and finding out the position of the difference data corresponding to the original data, backing up the original data, and replacing the data at the position with the difference data to update the data in the original data.
6. The expert knowledge base construction method based on multi-label dynamic update of claim 4, wherein in S4, the data to be verified is analyzed through big data, the authenticity of the data before the time point is determined, and then the data is added; when the authenticity of the data is determined, sequentially determining that experts, time and events corresponding to the data accord with the data of the original knowledge base, searching a data source through big data, determining that the data and the source are both true, and adding the data to be verified into the expert knowledge base; classifying the newly added data according to experts, sequencing data corresponding to each expert according to a time sequence, extracting a label of the data corresponding to each expert, searching in the original knowledge base data through the label, and determining the newly added label in the newly added data; and adding the newly added data into the expert knowledge base.
7. The expert knowledge base construction method based on multi-label dynamic update as claimed in claim 1, wherein in S5, when information is extracted for the first time, all data in the knowledge base are extracted; and extracting important data once after the knowledge base data is updated every time, wherein the extracted objects are all updated data after the knowledge base data is updated every time.
8. The expert knowledge base construction method based on multi-label dynamic update of claim 7, wherein in S5, when a new expert label is determined, the new expert label is compared with all original labels to determine repetition and similarity; if the new expert label is repeated with the original label, the label is determined again; and when the new expert label is similar to the original label, analyzing the data difference corresponding to the two labels, and noting the difference of the two similar labels at the position of the label.
9. The expert knowledge base construction method based on multi-label dynamic update of claim 1, wherein in S6, the dynamic label is updated each time the expert knowledge base is updated;
each expert corresponds to a time line, a corresponding label is arranged above the time line, and important information of the experts is arranged below the time line.
10. The expert knowledge base construction method based on multi-label dynamic update of claim 9, wherein in S6, an expert name, an expert photo and an expert field are set at the start of each timeline, expert timelines in the same field are arranged together, then all timelines are arranged, a single timeline is set horizontally, and all timelines are arranged vertically; the arrangement order of the expert timelines in the same field is arranged from high to low according to the number of times the expert timelines are viewed.
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| CN117725229A (en) * | 2024-01-08 | 2024-03-19 | 中国科学技术信息研究所 | Knowledge organization system auxiliary updating method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117725229A (en) * | 2024-01-08 | 2024-03-19 | 中国科学技术信息研究所 | Knowledge organization system auxiliary updating method |
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