CN114281884A - Method for extracting subject knowledge submodel of knowledge graph - Google Patents
Method for extracting subject knowledge submodel of knowledge graph Download PDFInfo
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- CN114281884A CN114281884A CN202111464293.5A CN202111464293A CN114281884A CN 114281884 A CN114281884 A CN 114281884A CN 202111464293 A CN202111464293 A CN 202111464293A CN 114281884 A CN114281884 A CN 114281884A
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Abstract
The invention discloses a topic knowledge submodel extraction method of a knowledge graph, and relates to the technical field of information processing. The invention relates to a method for extracting a topic knowledge submodel of a knowledge graph, which comprises the following steps: the method comprises the following steps: preprocessing a required text input by a knowledge graph to obtain content data represented by the graph; step two: constructing a standard model according to the processed content data; step three: verifying and matching user login information, completing the entry of a user operation end, and acquiring a standard model by receiving a retrieval request about a target knowledge graph; step four: and searching the target subject knowledge sub-option of the required knowledge graph based on the content data represented by the graph. According to the method, the standard model is established, the operation sub-options corresponding to the target operation option are fused with the standard model to search and select corresponding characteristics, and the extraction efficiency and the extraction precision of the topic knowledge sub-model are improved.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a topic knowledge submodel extraction method of a knowledge graph.
Background
The knowledge map combines theories and methods of applying subjects such as mathematics, graphics, information visualization technology, information science and the like with methods such as metrology citation analysis, co-occurrence analysis and the like, and visually displaying the core structure, development history, frontier field and overall knowledge framework of the disciplines by utilizing the visual map to achieve the modern theory of the multidisciplinary fusion purpose, the knowledge domain visualization or knowledge domain mapping map is a series of different graphs for displaying the relationship between the knowledge development process and the structure in the book information field, the knowledge resources and the carriers thereof are described by using the visualization technology, and the knowledge and the interrelation among the knowledge resources, the carriers, the knowledge resources and the knowledge resources; secondly, giving new meaning to the character string instead of the simple character string; thirdly, all disciplines are fused, so that consistency of the user during searching is facilitated; finding out more accurate information for the user, making more comprehensive summary and providing more deeply related information; systematically displaying a knowledge system related to the keywords to a user; sixthly, the user can acquire information and data reserved on other services only by logging in one of more than 60 online services under the Google flag, and seventhly, the Google draws useful information from the whole internet so that the user can acquire more related public resources;
at present, the difficulty of extracting a topic knowledge sub-model of a knowledge graph is high, and the completeness of information processing is insufficient, so that the obtaining effect of sub-model features is poor, and the operation precision and the operation efficiency of information extraction are influenced; therefore, a topic knowledge submodel extraction method of the knowledge graph is provided.
Disclosure of Invention
The invention aims to provide a method for extracting a topic knowledge submodel of a knowledge graph so as to solve the problems in the background.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a method for extracting a topic knowledge submodel of a knowledge graph, which comprises the following steps:
the method comprises the following steps: preprocessing a required text input by a knowledge graph to obtain content data represented by the graph;
step two: constructing a standard model according to the processed content data;
step three: verifying and matching user login information, completing the entry of a user operation end, and acquiring a standard model by receiving a retrieval request about a target knowledge graph;
step four: searching a target subject knowledge sub-option of a required knowledge graph based on the content data represented by the graph;
step five: rendering operation sub-options corresponding to the target operation options to a target knowledge graph construction page, and fusing a standard model;
step six: searching corresponding characteristics of the target operator options and the standard model, and selecting the corresponding characteristics in the knowledge graph to obtain a topic knowledge submodel;
step seven: and finishing the extraction of the subject knowledge submodel of the knowledge graph.
Preferably, the preprocessing of the requirement text input by the knowledge graph in the first step comprises displaying a complex knowledge domain by utilizing data mining, information processing, knowledge measurement and graph drawing, wherein the knowledge graph comprises a plurality of nodes and attribute values of the nodes.
Preferably, in the second step, a standard model is constructed for the content data, and the strings are rearranged to have a new meaning.
Preferably, in the third step, the verification and matching of the user login information are performed, and a verification mode of a digital account is adopted.
Preferably, the target subject knowledge sub-option of the required knowledge graph is searched in the fourth step, multiple disciplines are fused, and a knowledge system related to key words is systematically displayed to the user, so that more accurate information is found for the user, more comprehensive summary is made, and more deeply related information is provided.
Preferably, in the fifth step, the operation sub-options corresponding to the target operation options are rendered to the target knowledge graph construction page, and the operation is performed based on a computer operation terminal, wherein the computer operation terminal comprises a computer processor and a computer memory, and the computer processor is electrically connected with the computer memory.
Preferably, the sixth step selects corresponding features in the knowledge-graph, and further includes a recognition and determination module of the system, so as to determine the features of the subject knowledge sub-model, wherein the recognition and determination module of the system includes an entry-ready terminal and an entry-disabled terminal.
The invention has the following beneficial effects:
according to the method for extracting the topic knowledge submodel of the knowledge graph, the standard model is established, the operation submodel corresponding to the target operation option is fused with the standard model to search and select corresponding characteristics, and the method is beneficial to improving the extraction efficiency and the extraction precision of the topic knowledge submodel.
According to the method for extracting the subject knowledge submodel of the knowledge graph, the safety of information output and acquisition is effectively improved by verifying and matching the user login information.
The method for extracting the subject knowledge submodel of the knowledge graph is simple and rapid to operate, low in operation difficulty, high in operation efficiency and high in popularization value.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the method for extracting the subject knowledge submodel of the knowledge graph of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Please refer to fig. 1: the invention relates to a method for extracting a topic knowledge submodel of a knowledge graph, which comprises the following steps:
the method comprises the following steps: preprocessing a required text input by a knowledge graph to obtain content data represented by the graph; the knowledge graph comprises a plurality of nodes and attribute values of the nodes;
step two: constructing a standard model according to the processed content data; and the character strings are recombined and arranged;
step three: verifying and matching user login information, completing the entry of a user operation end, and acquiring a standard model by receiving a retrieval request about a target knowledge graph; the user login adopts a digital password verification mode, and the safety in the information processing process is enhanced;
step four: searching a target subject knowledge sub-option of a required knowledge graph based on the content data represented by the graph; make a more comprehensive summary and provide more depth-related information;
step five: rendering operation sub-options corresponding to the target operation options to a target knowledge graph construction page, and fusing a standard model; operating based on a computer operation terminal;
step six: searching corresponding characteristics of the target operator options and the standard model, and selecting the corresponding characteristics in the knowledge graph to obtain a topic knowledge submodel;
step seven: and finishing the extraction of the subject knowledge submodel of the knowledge graph.
The method comprises the following steps of firstly, preprocessing a required text input by a knowledge graph, wherein the required text input by the knowledge graph comprises displaying a complex knowledge field by utilizing data mining, information processing, knowledge measurement and graph drawing, and the knowledge graph comprises a plurality of nodes and attribute values of the nodes.
And in the second step, a standard model is constructed for the content data, and the character strings are recombined and arranged to have new significance.
And in the third step, the verification and matching of the user login information are carried out, and the security of information acquisition is effectively improved by adopting a digital account verification mode.
The target subject knowledge sub-options of the required knowledge graph are searched in the fourth step, multiple disciplines are fused, and a knowledge system related to key words is systematically displayed to the user, so that more accurate information is found for the user, more comprehensive summary is made, more deeply related information is provided, and the operation completion degree is higher.
And in the fifth step, the operation sub-options corresponding to the target operation options are rendered to the target knowledge graph construction page and run based on the computer operation terminal, the computer operation terminal comprises a computer processor and a computer memory, the computer processor is electrically connected with the computer memory, and the operation efficiency is effectively improved by using the computer to run.
And the identification and judgment module of the system comprises an access terminal and a non-access terminal, and improves the operation precision by perfecting corresponding ports.
The nodes contained in the knowledge graph comprise entities, wherein the entities refer to certain things which have distinguishability and exist independently, such as a certain person, a certain city, a certain plant and the like, a certain commodity and the like, the entities are the most basic elements in the knowledge graph, and different relationships exist among different entities; semantic classes, which refer to a set of entities with the same kind of characteristics, including a set, a category, an object type, a kind of things, such as a person, a geography, and the like; content, typically as names, descriptions, interpretations, etc. of entities and semantic classes, may be expressed by text, images, audio-video, etc.; attributes (values), which point to its attribute value from an entity, different attribute types corresponding to edges of different types of attributes, attribute values mainly referring to values of object-specific attributes; relations, formalized as a function, which maps k k points to a boolean value, on a knowledge graph, relations are a function that maps k k graph nodes (entities, semantic classes, attribute values) to boolean values;
in the scheme, the architecture of the knowledge graph comprises a self logic structure and a technical (system) architecture adopted for constructing the knowledge graph, the knowledge graph can be logically divided into a mode layer and a data layer, the data layer mainly comprises a series of facts, knowledge is stored by taking the facts as a unit, the mode layer is constructed on the data layer and is the core of the knowledge graph, a body base is generally adopted to manage the mode layer of the knowledge graph, the body is a concept template of a structured knowledge base, and the knowledge base formed by the body base has a strong hierarchical structure and a small redundancy degree; the architecture of the knowledge graph refers to a mode structure, the knowledge graph construction is based on most original data (including structured, semi-structured and unstructured data), a series of automatic or semi-automatic technical means are adopted to extract knowledge facts from an original database and a third-party database and store the knowledge facts into a data layer and a mode layer of the knowledge base, and the process comprises the following steps: the method comprises four processes of information extraction, knowledge representation, knowledge fusion and knowledge reasoning, and each updating iteration comprises four stages.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (7)
1. The method for extracting the topic knowledge submodel of the knowledge graph is characterized by comprising the following steps of:
the method comprises the following steps: preprocessing a required text input by a knowledge graph to obtain content data represented by the graph;
step two: constructing a standard model according to the processed content data;
step three: verifying and matching user login information, completing the entry of a user operation end, and acquiring a standard model by receiving a retrieval request about a target knowledge graph;
step four: searching a target subject knowledge sub-option of a required knowledge graph based on the content data represented by the graph;
step five: rendering operation sub-options corresponding to the target operation options to a target knowledge graph construction page, and fusing a standard model;
step six: searching corresponding characteristics of the target operator options and the standard model, and selecting the corresponding characteristics in the knowledge graph to obtain a topic knowledge submodel;
step seven: and finishing the extraction of the subject knowledge submodel of the knowledge graph.
2. The method for extracting the subject knowledge submodel of the knowledge-graph according to claim 1, wherein the step one is to preprocess the required text input by the knowledge-graph, including displaying the complex knowledge domain by using data mining, information processing, knowledge measurement and graph drawing, and the knowledge-graph includes a plurality of nodes and attribute values of the nodes.
3. The method for extracting the subject knowledge submodel of the knowledge-graph according to claim 1, wherein in the second step, a standard model is constructed for the content data, and the strings are recombined and arranged to have new significance.
4. The method for extracting the subject knowledge submodel of the knowledge-graph according to claim 1, wherein the verification and matching of the user login information are performed in the third step by adopting a digital account verification mode.
5. The method for extracting the topic knowledge submodel of the knowledge graph according to claim 1, wherein the step four is to search the target topic knowledge submodel of the required knowledge graph, integrate multiple disciplines, and systematically display the knowledge system related to the key words to the user, thereby finding more accurate information for the user, making more comprehensive summary and providing more deeply related information.
6. The method for extracting the subject knowledge submodel of the knowledge-graph according to claim 1, wherein in the fifth step, the operation sub-option corresponding to the target operation option is rendered to the target knowledge-graph construction page, and the operation is performed based on a computer operation terminal, wherein the computer operation terminal comprises a computer processor and a computer memory, and the computer processor is electrically connected with the computer memory.
7. The method for extracting the subject knowledge submodel of the knowledge-graph according to claim 1, wherein the corresponding features in the knowledge-graph are selected in the sixth step, and further comprising a system identification and determination module for determining the features of the subject knowledge submodel, wherein the system identification and determination module comprises an entry-ready terminal and an entry-disabled terminal.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115544626A (en) * | 2022-10-21 | 2022-12-30 | 清华大学 | Submodel extraction method, submodel extraction device, computer equipment and medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115544626A (en) * | 2022-10-21 | 2022-12-30 | 清华大学 | Submodel extraction method, submodel extraction device, computer equipment and medium |
| CN115544626B (en) * | 2022-10-21 | 2023-10-20 | 清华大学 | Sub-model extraction method, device, computer equipment and medium |
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