CN109614467B - Knowledge association and dynamic organization method and system based on fragment similarity - Google Patents
Knowledge association and dynamic organization method and system based on fragment similarity Download PDFInfo
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Abstract
The embodiment of the application provides a knowledge association and dynamic organization method and system based on fragment similarity, wherein the method comprises the following steps: obtaining a partial structured segment of the structured knowledge, and filling the segment based on the application scene; dividing words of the structured fragments based on the professional word stock, acquiring keywords, and determining knowledge information associated with the filled structured fragments by utilizing the similarity of the keywords; and traversing the static knowledge classification system based on the associated knowledge information, and generating a dynamic link for a user to view the knowledge information matched with the application scene. According to the technical scheme, the provided knowledge segments are subjected to similarity calculation, so that knowledge with strong comprehensiveness, which is difficult to simply classify, is effectively associated, and effective fusion of dynamic knowledge organization and a static knowledge classification system is realized.
Description
Technical Field
The application relates to the technical field of big data, in particular to a knowledge association and dynamic organization method and system based on fragment similarity.
Background
For complex product development, knowledge encompasses the development of all system, full life cycle documents (e.g., reports, literature, standards), models, and data. Common knowledge is managed in separate libraries, including archives (e.g., outcome, literature, standard), model, database, etc. In order to effectively manage and utilize knowledge generated in each level of the whole development system and each stage of the whole life cycle and distributed in each library, the knowledge in each library can be conveniently organized and searched according to classification, wherein the knowledge is generally classified according to product composition, professional composition, stage/baseline and the like. Such as Product Data Management (PDM) systems, typically build corresponding tree management views according to product composition and are effectively managed according to phase (split into design, process, manufacturing, etc.) and baseline.
However, for some comprehensive knowledge, such as product quality, maintenance related reports, cases, etc., it is difficult to simply classify the knowledge, so that it is difficult to efficiently organize and search the knowledge, and it is also difficult to correlate the knowledge with knowledge that can be clearly classified under a static knowledge classification system. Taking product quality as an example, quality analysis reports of complex products generally include multiple product composition levels related to quality phenomena, multiple professions, and multiple stages in the development process. It is difficult to classify the quality analysis report into a certain class and recommend or find a quality analysis report of a similar problem under the class, and further retrieve a related design report, design model, calculation analysis/real (test) data, and the like.
Typically such knowledge will be tagged with keywords and can be searched using keyword searches. However, this approach has the problem that the keyword selection is often not very comprehensive; the keywords selected by the person searching for knowledge and the keywords set by the person contributing knowledge are often inconsistent. If the most relevant knowledge cannot be found, it is difficult to find accurate attributes such as the product model of the knowledge from the knowledge, so that relevant documents can be called up for further reference through a static knowledge classification system.
Disclosure of Invention
In order to solve one of the problems, the application provides a knowledge association and dynamic organization method and system based on fragment similarity.
According to a first aspect of the embodiments of the present application, there is provided a method for knowledge association and dynamic organization based on segment similarity, the method comprising the steps of:
obtaining a partial structured segment of the structured knowledge, and filling the segment based on the application scene;
dividing words of the structured fragments based on the professional word stock, acquiring keywords, and determining knowledge information associated with the filled structured fragments by utilizing the similarity of the keywords;
and traversing the static knowledge classification system based on the associated knowledge information, and generating a dynamic link for a user to view the knowledge information matched with the application scene.
Preferably, the step of obtaining a partially structured segment of structured knowledge and filling in the segment comprises:
determining a structured template matched with knowledge information based on an application scene of the knowledge information;
under the structuring template, selecting a structuring fragment for describing the situation under the scene;
and filling in the structured fragments based on the application scene.
Preferably, the step of traversing the pre-stored knowledge information base to determine knowledge information associated with the filled-in segments based on keyword similarity includes:
dividing words of the structured fragments based on the professional word stock, and determining keywords in the whole content of the structured fragments;
searching the keywords and the semantically related keywords thereof, and determining the occurrence frequency of the keywords in other knowledge information related to the filled structured fragments;
and determining relevant knowledge information based on the occurrence frequency of the keywords, and sequencing the similarity of the relevant knowledge information.
Preferably, the step of determining the relevant knowledge information and ordering the similarity thereof based on the frequency of occurrence of the keywords includes: and carrying out weighted summation on the frequency of key occurrence to obtain knowledge information which is ordered according to the similarity and is associated with the filled structured fragments.
Preferably, the step of searching for relevant knowledge information in a static knowledge classification system based on the associated knowledge information, and generating a dynamic link for viewing knowledge information by a user includes:
traversing the static knowledge classification system according to a certain piece of selected associated knowledge information, and determining and displaying the position of the static knowledge classification system;
starting from the position, entering a navigation list of a static knowledge classification system, and viewing knowledge information related to the knowledge information.
According to a second aspect of embodiments of the present application, there is provided a knowledge association and dynamic organization system based on segment similarity, the system comprising:
the acquisition module acquires a part of the structuring fragment of the structuring knowledge and fills in the fragment based on the application scene;
the association module is used for segmenting the structured fragments based on the professional word stock, acquiring keywords and determining knowledge information associated with the filled structured fragments by utilizing the similarity of the keywords;
and the generation module traverses the static knowledge classification system based on the associated knowledge information and generates a dynamic link for a user to view the knowledge information matched with the application scene.
Preferably, the acquiring module specifically performs the following steps:
determining a structured template matched with knowledge information based on an application scene of the knowledge information;
under the structuring template, selecting a structuring fragment for describing the situation under the scene;
and filling in the structured fragments based on the application scene.
Preferably, the association module specifically performs the following steps:
dividing words of the structured fragments based on the professional word stock, and determining keywords in the whole content of the structured fragments;
searching the keywords and the semantically related keywords thereof, and determining the occurrence frequency of the keywords in other knowledge information related to the filled structured fragments;
and carrying out weighted summation on the frequency of key occurrence to obtain knowledge information which is ordered according to the similarity and is associated with the filled structured fragments.
Preferably, the generating module specifically performs the following steps:
traversing the static knowledge classification system according to a certain piece of selected associated knowledge information, and determining and displaying the position of the static knowledge classification system;
starting from the position, entering a navigation list of a static knowledge classification system, and viewing knowledge information related to the knowledge information.
Preferably, the static classification system comprises: design report, design model, computational analysis/real (test) data taxonomies.
According to the technical scheme, the provided knowledge segments are subjected to similarity calculation, so that knowledge with strong comprehensiveness, which is difficult to simply classify, is effectively associated, and effective fusion of dynamic knowledge organization and a static knowledge classification system is realized.
According to the technical scheme, the knowledge such as similar reports and cases related to product quality and maintenance can be conveniently and rapidly found, and the design report, the design model, the calculation analysis/real (test) data and the like related to the knowledge can be further retrieved through a traditional static classification system, so that similar problems can be avoided or existing processing methods can be referred, and the method has important significance for developing complex products related to complex product compositions, professional compositions and phase compositions.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 shows a schematic diagram of the knowledge association and dynamic organization method described in this application.
Detailed Description
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following detailed description of exemplary embodiments of the present application is given with reference to the accompanying drawings, and it is apparent that the described embodiments are only some of the embodiments of the present application and not exhaustive of all the embodiments. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
The method and the system have the advantages that the provided knowledge segments are subjected to similarity calculation, so that knowledge with strong comprehensiveness, which is difficult to simply classify, is effectively associated, and effective fusion of dynamic knowledge organization and a static knowledge classification system is realized, and the problems of association of knowledge with strong comprehensiveness (such as reports, cases and the like related to product quality and maintenance) and effective fusion of knowledge organization and the static knowledge classification system are solved.
Example 1
As shown in fig. 1, the present example discloses a knowledge association and dynamic organization method based on fragment similarity, and the steps of the method include:
step 1, obtaining a partial structured fragment of the structured knowledge, and filling the fragment based on an application scene. Firstly, determining a structured template matched with knowledge information based on an application scene of the knowledge information; then, under the structuring template, selecting a structuring fragment for describing the situation under the scene; finally, filling in the structured fragments based on the application scene.
And 2, word segmentation is carried out on the structured fragments based on the professional word stock, keywords are obtained, and knowledge information associated with the filled structured fragments is determined by utilizing the similarity of the keywords. Firstly, word segmentation is carried out on the structured fragments based on a professional word stock, and keywords in the whole content of the structured fragments are determined; then, searching the keywords and the keywords related to the semantics thereof, and determining the occurrence frequency of the keywords in other knowledge information related to the filled structured fragments; and finally, determining relevant knowledge information based on the occurrence frequency of the keywords, and sequencing the similarity. In this example, the step of determining the relevant knowledge information based on the occurrence frequency of the keywords and sorting the similarity is to weight and sum the occurrence frequency of the keywords to obtain knowledge information associated with the filled structured segments sorted according to the similarity.
And step 3, traversing the static knowledge classification system based on the associated knowledge information, and generating a dynamic link for a user to view the knowledge information matched with the application scene. Firstly, traversing a static knowledge classification system according to a certain piece of selected associated knowledge information, and determining and displaying the position of the static knowledge classification system; then, starting from the position, a navigation list of the static knowledge classification system is entered to view knowledge information related to the knowledge information.
Based on the above steps, in this example, an application scenario may be set as a fault analysis scenario, and materials such as a fault analysis report are used as a structural template to select a structural fragment describing a situation in the scenario; finally, in the context of fault analysis, the structured fragments, such as fault descriptions, are filled in.
According to the technical scheme, the provided knowledge segments are subjected to similarity calculation, so that knowledge with strong comprehensiveness, which is difficult to simply classify, is effectively associated, and effective fusion of dynamic knowledge organization and a static knowledge classification system is realized.
According to the technical scheme, the knowledge such as similar reports and cases related to product quality and maintenance can be conveniently and rapidly found, and the design report, the design model, the calculation analysis/real (test) data and the like related to the knowledge can be further retrieved through a traditional static classification system, so that similar problems can be avoided or existing processing methods can be referred, and the method has important significance for developing complex products related to complex product compositions, professional compositions and phase compositions.
In this example, further disclosed is a knowledge association and dynamic organization system based on segment similarity, the system comprising:
the acquisition module acquires a partial structured fragment of the structured knowledge, and fills in the fragment based on the application scene. The acquisition module specifically executes the following steps: determining a structured template matched with knowledge information based on an application scene of the knowledge information; under the structuring template, selecting a structuring fragment for describing the situation under the scene; and filling in the structured fragments based on the application scene.
And the association module is used for segmenting the structured fragments based on the professional word stock, acquiring keywords and determining knowledge information associated with the filled structured fragments by utilizing the similarity of the keywords. The association module specifically executes the following steps: dividing words of the structured fragments based on the professional word stock, and determining keywords in the whole content of the structured fragments; searching the keywords and the semantically related keywords thereof, and determining the occurrence frequency of the keywords in other knowledge information related to the filled structured fragments; and carrying out weighted summation on the frequency of key occurrence to obtain knowledge information which is ordered according to the similarity and is associated with the filled structured fragments.
And the generation module traverses the static knowledge classification system based on the associated knowledge information and generates a dynamic link for a user to view the knowledge information matched with the application scene. The generation module specifically executes the following steps: traversing a static knowledge classification system, and searching the content containing the associated knowledge information in a certain classification; and generating a link which can directly enter the knowledge content of the classification under the knowledge background attribute. Wherein the static classification system comprises: design report, design model, computational analysis/real (test) data.
Example 2
The embodiment provides a knowledge association and dynamic organization method based on fragment similarity, which can realize corresponding functions through electronic equipment, and comprises the following steps: a memory, one or more processors; the memory is connected with the processor through a communication bus; the processor is configured to execute the instructions in the memory; the storage medium has stored therein instructions for carrying out the steps of the method as described above. The method may also implement the corresponding functions by a computer storage medium having stored thereon a computer program which when executed by a processor implements the steps of the method as described above. Specifically, the method comprises the following steps:
the first step is to select a part of the structured fragments of a certain type of structured knowledge and fill in the content of the fragments.
Providing a structured template of a certain type of knowledge for a requirement when the requirement is a scene using the knowledge; based on the structured template, selecting a structured fragment describing the situation in the scene, and filling the structured fragment as comprehensively as possible by a user. The content of the filled-in fragment is submitted and calculated.
And secondly, calculating the similarity between the content of the submitted fragment and other knowledge of the class, and recommending the associated knowledge to the user according to the similarity ranking.
And (3) searching keywords in the whole content of the structured segment from the professional word stock, searching each keyword and related keywords of the semantic meaning one by one, finding the occurrence frequency of each keyword in other knowledge, carrying out weighted summation to obtain similarity sequencing, correlating related knowledge, and recommending sequencing result knowledge to a user.
Thirdly, the user checks the recommended knowledge and links into a static knowledge classification system to search other kinds of associated knowledge according to the background attribute of the knowledge
And the user uses a knowledge navigation bar provided in the electronic equipment or the program to start with the background attribute of the knowledge, browse the static knowledge classification system, search and enter other related knowledge. If the knowledge text contains a word of a certain classification in the static knowledge classification system, a link which can directly enter the knowledge of the classification under the knowledge background attribute is generated for the user to click and view.
According to the scheme, the provided knowledge segments are subjected to similarity calculation, so that knowledge with strong comprehensiveness, which is difficult to simply classify, is effectively associated, and effective fusion of dynamic knowledge organization and static knowledge classification system is realized. The method can conveniently and rapidly find out the knowledge of similar reports, cases and the like related to product quality and maintenance, and further retrieve the design report, design model, calculation analysis/real (test) data and the like related to the knowledge through the traditional static classification system, thereby helping to avoid similar problems or referencing the existing processing method, and having important significance for developing complex products related to complex product composition, professional composition and stage composition.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof, but rather as providing for the use of additional embodiments and advantages of all such modifications, equivalents, improvements and similar to the present invention are intended to be included within the scope of the present invention as defined by the appended claims.
Claims (8)
1. A knowledge association and dynamic organization method based on segment similarity, comprising the steps of:
obtaining a partial structured segment of the structured knowledge, and filling the segment based on the application scene;
dividing words of the structured fragments based on the professional word stock, acquiring keywords, and determining knowledge information associated with the filled structured fragments by utilizing the similarity of the keywords;
traversing a static knowledge classification system based on the associated knowledge information, and generating a dynamic link for a user to view knowledge information matched with an application scene;
the step of obtaining the partial structured fragment of the structured knowledge and filling the fragment based on the application scene comprises the following steps:
determining a structured template matched with knowledge information based on an application scene of the knowledge information;
under the structuring template, selecting a structuring fragment for describing the situation under the scene;
and filling in the structured fragments based on the application scene.
2. The knowledge association and dynamic organization method according to claim 1, wherein the step of determining knowledge information associated with the filled-in structured snippets using keyword similarity by segmenting the structured snippets based on a professional lexicon and obtaining keywords comprises:
dividing words of the structured fragments based on the professional word stock, and determining keywords in the whole content of the structured fragments;
searching the keywords and the semantically related keywords thereof, and determining the occurrence frequency of the keywords in other knowledge information related to the filled structured fragments;
and determining relevant knowledge information based on the occurrence frequency of the keywords, and sequencing the similarity of the relevant knowledge information.
3. The knowledge association and dynamic organization method according to claim 2, wherein the step of determining relevant knowledge information based on the frequency of occurrence of keywords and ordering the similarity thereof comprises: and carrying out weighted summation on the occurrence frequency of the keywords to obtain knowledge information which is ordered according to the similarity and is associated with the filled structured fragments.
4. The knowledge association and dynamic organization method of claim 2, wherein the step of traversing a static knowledge classification hierarchy based on the associated knowledge information to generate a dynamic link for a user to view knowledge information matching an application scenario comprises:
traversing the static knowledge classification system according to a certain piece of selected associated knowledge information, and determining and displaying the position of the static knowledge classification system;
starting from the position, entering a navigation list of a static knowledge classification system, and viewing knowledge information related to the knowledge information.
5. A knowledge association and dynamic organization system based on segment similarity, the system comprising:
the acquisition module acquires a part of the structuring fragment of the structuring knowledge and fills in the fragment based on the application scene;
the association module is used for segmenting the structured fragments based on the professional word stock, acquiring keywords and determining knowledge information associated with the filled structured fragments by utilizing the similarity of the keywords;
the generation module traverses a static knowledge classification system based on the associated knowledge information and generates a dynamic link for a user to view knowledge information matched with an application scene;
the acquisition module specifically executes the following steps:
determining a structured template matched with knowledge information based on an application scene of the knowledge information;
under the structuring template, selecting a structuring fragment for describing the situation under the scene;
and filling in the structured fragments based on the application scene.
6. The knowledge association and dynamic organization system of claim 5, wherein the association module specifically performs the steps of:
dividing words of the structured fragments based on the professional word stock, and determining keywords in the whole content of the structured fragments;
searching the keywords and the semantically related keywords thereof, and determining the occurrence frequency of the keywords in other knowledge information related to the filled structured fragments;
and carrying out weighted summation on the frequency of key occurrence to obtain knowledge information which is ordered according to the similarity and is associated with the filled structured fragments.
7. The knowledge association and dynamic organization system of claim 6, wherein the generation module specifically performs the steps of:
traversing the static knowledge classification system according to a certain piece of selected associated knowledge information, and determining and displaying the position of the static knowledge classification system;
starting from the position, entering a navigation list of a static knowledge classification system, and viewing knowledge information related to the knowledge information.
8. The knowledge correlation and dynamic organization system of claim 7, wherein the static knowledge classification hierarchy comprises: design report, design model, calculation and analysis of experimental data classification system.
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| CN103593336B (en) * | 2013-10-30 | 2017-05-10 | 中国运载火箭技术研究院 | Knowledge pushing system and method based on semantic analysis |
| US10049102B2 (en) * | 2014-06-26 | 2018-08-14 | Hcl Technologies Limited | Method and system for providing semantics based technical support |
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| CN107784166A (en) * | 2017-09-29 | 2018-03-09 | 北京卫星制造厂 | A kind of spacecraft process knowledge supplying system and method based on the aspect of model |
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