CN113342986A - Individualized knowledge service recommendation system based on knowledge graph - Google Patents
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Abstract
The invention discloses an individualized knowledge service recommendation system based on a knowledge graph, which comprises a reading-beating running accompanying service module, wherein the reading-beating running accompanying service module comprises a knowledge graph construction unit, a knowledge query unit, a knowledge learning path planning unit and a knowledge extension unit; the knowledge graph construction unit is used for constructing a knowledge graph system of a user; the knowledge inquiry unit is used for inquiring the content of knowledge points in the existing knowledge graph in the system; the knowledge learning path planning unit is used for recommending new knowledge point learning to the user after the user finishes learning a knowledge point; the knowledge extension unit is used for pushing extended knowledge points to the user, wherein the extended knowledge points are knowledge points which are not in a knowledge graph system of the user. The invention can help the user to obtain the effective transmission and fusion between various learning knowledge information.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence, and particularly relates to an individualized knowledge service recommendation system based on a knowledge graph.
Background
The global data is increased at a speed of 58% per year, and the cardinality of the global data is large, so that people are submerged in a data sea, wherein only 3% of the data has labeled information, and 0.5% of the data is analyzed, so that the information cannot be effectively utilized, in order to solve the information overload, a search engine and a recommendation system with functions of filtering and screening the information are provided, the former can quickly feed back content related to user description, and the latter can personally recommend proper content for a user, so as to assist the user in quickly making a decision. Compared with a search engine and a recommendation system, the search engine mainly aims at the information service with clear and popular requirements, the experience of a user can be improved by inquiring and recommending, the information service with unclear and personalized requirements needs to be solved by the recommendation system, and the recommendation system can actively recommend proper content. The information requirement has a long tail phenomenon, and the recommendation system acquires the potential association between the user and the product by mining the user behavior, so that personalized information service is performed. The recommendation system is ubiquitous in daily life, such as broad bean, known news, hundred-degree news dissemination, internet music recommendation and Jingdong commodity recommendation, and the like, and has become an indispensable service mode in people's life.
The knowledge graph is a complete ecosystem comprising knowledge representation, knowledge construction, knowledge maintenance and knowledge application, and mainly comprises a classical knowledge representation theory (first-order predicate logic, semantic network, framework and script) and a semantic network resource description framework (XML, RDF Schema and OWL).
The reason why the knowledge-graph is chosen is mainly based on the following reasons:
1) knowledge graph is indispensable basic resource for artificial intelligence application;
2) the semantic expression capability is rich, and the current knowledge service can be competent;
3) the description form is uniform, so that integration and fusion of different types of knowledge are facilitated;
4) the representation method is friendly to human beings, and provides convenience for editing and constructing knowledge in modes of crowdsourcing and the like;
5) the description form based on the binary relation is convenient for automatic acquisition of knowledge;
6) the representation method is computer-friendly and supports efficient reasoning;
7) the data format based on the graph structure is convenient for the storage and the retrieval of the computer system.
The knowledge graph contains rich semantic association between entities, and provides a potential auxiliary information source for a recommendation system.
The outstanding problems and shortcomings of doctor education include single educational objective, too narrow and specialized research field, mainly using disciplines as culture units; does not attach importance to the wide and necessary general ability of the cultured students, and has insufficient interdisciplinary cooperation research ability and innovation practice ability; doctor students do not have provisions for employment outside the academia. Due to the fact that various internal knowledge information cannot be effectively transmitted and fused, the ability of doctor graduates is deficient or mismatched due to the mode of 'lone army fight'.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a knowledge graph-based personalized knowledge service recommendation system aiming at the defects in the prior art, wherein a user can pre-construct a knowledge graph system to be learned through a knowledge graph construction unit, and further study and query and extension of the knowledge system are carried out through the knowledge graph system, so that the user is helped to acquire effective transmission and fusion among various learned knowledge information.
In order to solve the technical problems, the invention adopts the technical scheme that: a personalized knowledge service recommendation system based on a knowledge graph comprises a reading and accompanying running service module, wherein the reading and accompanying running service module comprises a knowledge graph construction unit, a knowledge query unit, a knowledge learning path planning unit and a knowledge extension unit;
the knowledge graph construction unit is used for constructing a knowledge graph system of a user;
the knowledge inquiry unit is used for inquiring the content of knowledge points in the existing knowledge graph in the system;
the knowledge learning path planning unit is used for recommending new knowledge point learning to the user after the user finishes learning a knowledge point;
the knowledge extension unit is used for pushing extended knowledge points to the user, wherein the extended knowledge points are knowledge points which are not in a knowledge graph system of the user.
The knowledge-graph-based personalized knowledge service recommendation system further comprises an online project system scientific research academic tutoring service module, wherein the online project system scientific research academic tutoring service module comprises a project receiving unit, a project decomposition unit and a result evaluation unit;
the project receiving unit is used for receiving a project research and development request; the project research and development request comprises a project name, a project requirement and a project finishing time point;
the project decomposition unit is used for decomposing a plurality of project tasks and task finishing time points corresponding to each task according to the project requirements by a user;
and the result evaluation unit is used for evaluating the completion result of each project task by the user.
The system for recommending the personalized knowledge service based on the knowledge map further comprises an innovation and entrepreneurship training service module, wherein the innovation and entrepreneurship training service module comprises an innovation and entrepreneurship learning unit, a wage registration unit and a financing loan unit;
the innovation entrepreneurship learning unit is used for uploading, downloading and playing an innovation entrepreneurship video;
the business registration unit is used for the user to fill in the information required by the business registration and submit the information required by the business registration to the business system;
the financing and loan unit is used for the user to initiate a financing request and a loan request, and for the user to sign a financing agreement or a loan agreement after the request passes.
The knowledge-graph-based personalized knowledge service recommendation system further comprises a question-answer community service module, and the question-answer community service module is used for users to issue questions and answer questions in a community.
The system for recommending the personalized knowledge service based on the knowledge graph comprises the following steps when a knowledge graph system is constructed by the knowledge graph construction unit:
acquiring a knowledge point adding request initiated by a user; the knowledge point adding request comprises a knowledge point name, knowledge point description and knowledge point mastering degree;
and inquiring whether the current knowledge map has knowledge point information in the knowledge point adding request, if so, returning that the current knowledge point exists, if not, storing the current knowledge point into a knowledge map system, acquiring the mapping relation between the current knowledge point and other knowledge points in the knowledge map, and if so, establishing the association relation between the two knowledge points.
In the system for recommending personalized knowledge service based on the knowledge graph, the knowledge learning path planning unit, when recommending new knowledge point learning to the user, comprises the following steps:
acquiring the current knowledge points which are just learned, screening out the knowledge points which are associated with the knowledge points which are just learned, and exiting if no associated knowledge points exist; and if the associated knowledge points are associated, further screening the associated knowledge points which are not learned, and learning the knowledge point with the lowest level in the screened associated knowledge points, wherein the level of the knowledge point refers to the number of the preposed knowledge points of the knowledge point.
The system for recommending the personalized knowledge service based on the knowledge graph comprises the following steps that when the knowledge extension unit pushes the extended knowledge points to the user:
acquiring a current knowledge graph of a user;
inquiring a cloud map with the highest similarity to the current knowledge map;
acquiring a knowledge point which is different from the current knowledge graph in the inquired cloud graph;
and taking the knowledge point with the lowest level from the different knowledge points to push the user.
Compared with the prior art, the invention has the following advantages: according to the invention, a user can pre-construct a knowledge map system to be learned through a knowledge map construction unit, and further study and query and extension of the knowledge system are carried out through the knowledge map system, so that the user is helped to acquire effective transmission and fusion among various learned knowledge information.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is an architectural diagram of the present invention.
Fig. 2 is a diagram illustrating the construction of the reading and pacing running service module according to the present invention.
FIG. 3 is an architecture diagram of an on-line project scientific research tutoring service module according to the present invention.
Detailed Description
As shown in fig. 1 to fig. 3, a knowledge-graph-based personalized knowledge service recommendation system includes a pace-reading running service module 100, an online project-system scientific research academic tutoring service module 200, an innovation startup training service module 300, and a question-answering community service module 400.
In this embodiment, the pace-reading running-accompanying service module 100 includes a knowledge graph constructing unit 101, a knowledge querying unit 102, a knowledge learning path planning unit 103, and a knowledge extending unit 104;
the knowledge graph construction unit 101 is used for a user to construct a knowledge graph system of the user;
specifically, the knowledge graph constructing unit 101, when constructing the knowledge graph system, includes the following steps:
step 1, acquiring a knowledge point adding request initiated by a user; the knowledge point adding request comprises a knowledge point name, knowledge point description and knowledge point mastering degree; for example, the "blockchain technique-concept includes Transaction (Transaction) that is a one-time operation that results in one change in the state of the ledger, such as adding a record; block (Block) for recording transaction and status results occurring over a period of time, which is a consensus on the current ledger status; chain (Chain) is formed by connecting blocks in series according to the occurrence order, and is a log record of the whole state change. If the blockchain is used as a state machine, each transaction is attempted to change state, and each consensus generated block is that the participant confirms the result of all transaction contents in the block that caused the state change. The block chain is explained in popular terms, if the database is assumed to be a book, the database can be read and written to be regarded as an accounting behavior, the principle of the block chain technology is that the person with the fastest accounting speed is found out in a period of time, the person accounts, and then the page of information of the book is sent to other owners in the whole system. This is equivalent to changing all records in the database and sending to every other node in the whole network, so the block chain technique is also called distributed ledger (distributed ledger). The mastery degree is as follows: 20%) "
And 2, inquiring whether the current knowledge graph has knowledge point information in the knowledge point adding request, if so, returning that the current knowledge point exists, if not, storing the current knowledge point into a knowledge graph system, acquiring the mapping relation between the current knowledge point and other knowledge points in the knowledge graph, and if so, establishing the association relation between the two knowledge points.
It should be noted that the mapping relationship between the knowledge points and the knowledge points includes forward mapping and reverse mapping, where the forward mapping refers to mapping from a forward knowledge point to a backward knowledge point, for example, a block chain technique includes a distribution setting technique, and to master the block chain technique, it is necessary to master the distributed technique first, that is, the distributed technique is the forward knowledge point of the block chain technique, and mapping from the distributed technique to the block chain technique is the forward mapping, whereas mapping from the block chain technique to the distributed technique is the reverse mapping.
Through the knowledge map construction unit 101, a user constructs a knowledge map system by himself, which can help the user systematically comb the knowledge points of the user and describe and master the knowledge points, and the user can deepen the knowledge master in the combing process.
In this embodiment, the knowledge query unit 102 is configured to query the content of a knowledge point in an existing knowledge graph in the system;
it should be noted that, when querying the content of the knowledge point, the knowledge querying unit 102 specifically returns the description part of the knowledge point by the user and the situations of all the pre-knowledge points of the knowledge point to the user, for example, when the user queries the blockchain technology, the user returns the spectrum situations of the pre-knowledge points of all the blockchains to the user in addition to the description of the blockchain to the user, so that the user can conveniently locate the place that the user does not understand.
In this embodiment, the knowledge learning path planning unit 103 is configured to recommend new knowledge point learning to the user after the user finishes learning a knowledge point;
the knowledge learning path planning unit 103 includes the following steps when recommending new knowledge point learning to a user:
acquiring the current knowledge points which are just learned, screening out the knowledge points which are associated with the knowledge points which are just learned, and exiting if no associated knowledge points exist; and if the associated knowledge points are associated, further screening the associated knowledge points which are not learned, and learning the knowledge point with the lowest level in the screened associated knowledge points, wherein the level of the knowledge point refers to the number of the preposed knowledge points of the knowledge point.
It should be understood by those skilled in the art that the level of the knowledge points may also be measured by using other dimensions, for example, classifying the technical field, subject field or application field of the knowledge points, assigning different level weights to the knowledge points in each field, and using the number of layer levels of the knowledge points in the neighborhood, the number of knowledge points in the current layer, and the number of knowledge points directly related to the knowledge points; e.g. knowledge point level
K1 represents the number of the preposed knowledge points of the knowledge points, K2 represents the number of the direct associations (direct mapping relationships) of the knowledge points, and K3 represents the number of the knowledge points in the domain hierarchy where the knowledge points are located.
It should be noted that, through the operation of the knowledge learning path planning unit 103, the user can be helped to make knowledge point recommendation from two angles, which are the most adjacent and easiest, so that the user can learn more efficiently.
In this embodiment, the knowledge extension unit 104 is configured to push an extended knowledge point to the user, where the extended knowledge point is a knowledge point that is not in the knowledge graph system of the user.
When the knowledge extension unit 104 pushes the extended knowledge points to the user, the method includes the following steps:
step 1, acquiring a current knowledge graph of a user;
step 2, inquiring a cloud map with the highest similarity to the current knowledge map;
step 3, acquiring the difference knowledge points between the inquired cloud atlas and the current knowledge atlas;
and 4, taking the knowledge point with the lowest level from the different knowledge points to push the user.
It should be noted that the knowledge graph system constructed by each user is uniformly stored in a storage constructed by a distributed system, and the knowledge graph system of each user is perfect and is a valuable wealth, when the knowledge graph of one user is perfect, the user can be considered to reach the limit of the user to a certain extent, and at the moment, the knowledge graphs of other users are obtained through the knowledge extension unit 104, so that the personal limit can be broken, and the user can be promoted. In addition, the similarity between the two knowledge maps can be measured by counting the number of similar knowledge points by a statistical method. The statistics of the similar knowledge points adopts the similarity calculation of description between the two knowledge points, and the similarity between the knowledge points can be measured by multiple dimensions such as the field, the field hierarchy of the knowledge points, the knowledge point content related to the knowledge points, the description of the knowledge points and the like during calculation.
The invention also comprises an online project system scientific research academic tutoring service module 200, wherein the online project system scientific research academic tutoring service module 200 comprises a project receiving unit 201, a project decomposition unit 202 and an achievement evaluation unit 203;
the project receiving unit 201 is configured to receive a project research and development request; the project research and development request comprises a project name, a project requirement and a project finishing time point;
the project decomposition unit 202 is configured to decompose, according to the project requirement, a plurality of project tasks and task ending time points corresponding to each task for a user;
the achievement evaluation unit 203 is configured to evaluate a completion result of each project task for the user.
It should be noted that the idea of the project decomposition unit 202 is similar to that of the knowledge graph construction unit 101, and by decomposing the project into a plurality of tasks, the user can complete each task (knowledge point) as a knowledge graph to be completed, and can correspondingly make a corresponding knowledge graph after completing the task, so as to enrich the knowledge graph system of the user.
The invention also comprises an innovation entrepreneur training service module 300, wherein the innovation entrepreneur training service module 300 comprises an innovation entrepreneur learning unit, a trader registration unit and a financing loan unit;
the innovation entrepreneurship learning unit is used for uploading, downloading and playing an innovation entrepreneurship video;
the business registration unit is used for the user to fill in the information required by the business registration and submit the information required by the business registration to the business system;
the financing and loan unit is used for the user to initiate a financing request and a loan request, and for the user to sign a financing agreement or a loan agreement after the request passes.
It should be noted that the design purpose of the innovative business training service module 300 is mainly to facilitate landing of the research and development results of the user, and in practice, after a large number of users use the system of the present invention, initiation, research and development and landing of each project will be streamlined, so that on one hand, the capital support of landing of the project can be evaluated by initiating a financing request and a loan request at the beginning of the project, on the other hand, the repeated research and development problems can be avoided by the online process of the project research and development, and on the other hand, the retention of the research and development process data can protect the research and development results from being continuously reused.
In this embodiment, the social question-answering service module 400 is further included, and the social question-answering service module 400 is used for users to issue questions and answer questions in a community.
It should be noted that the social question-answering service module 400 is used for providing mutual communication between users, so as to facilitate academic communication between users.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and all simple modifications, changes and equivalent structural changes made to the above embodiment according to the technical spirit of the present invention still fall within the protection scope of the technical solution of the present invention.
Claims (7)
1. A personalized knowledge service recommendation system based on knowledge graph is characterized in that: the system comprises a reading and accompanying running service module, wherein the reading and accompanying running service module comprises a knowledge map construction unit, a knowledge query unit, a knowledge learning path planning unit and a knowledge extension unit;
the knowledge graph construction unit is used for constructing a knowledge graph system of a user;
the knowledge inquiry unit is used for inquiring the content of knowledge points in the existing knowledge graph in the system;
the knowledge learning path planning unit is used for recommending new knowledge point learning to the user after the user finishes learning a knowledge point;
the knowledge extension unit is used for pushing extended knowledge points to the user, wherein the extended knowledge points are knowledge points which are not in a knowledge graph system of the user.
2. A knowledge-graph based personalized knowledge service recommendation system according to claim 1, characterized in that: the online project system scientific research academic tutoring service module comprises a project receiving unit, a project decomposition unit and a result evaluation unit;
the project receiving unit is used for receiving a project research and development request; the project research and development request comprises a project name, a project requirement and a project finishing time point;
the project decomposition unit is used for decomposing a plurality of project tasks and task finishing time points corresponding to each task according to the project requirements by a user;
and the result evaluation unit is used for evaluating the completion result of each project task by the user.
3. A knowledge-graph based personalized knowledge service recommendation system according to claim 1, characterized in that: the system also comprises an innovation entrepreneur training service module, wherein the innovation entrepreneur training service module comprises an innovation entrepreneur learning unit, a wage registration unit and a financing loan unit;
the innovation entrepreneurship learning unit is used for uploading, downloading and playing an innovation entrepreneurship video;
the business registration unit is used for the user to fill in the information required by the business registration and submit the information required by the business registration to the business system;
the financing and loan unit is used for the user to initiate a financing request and a loan request, and for the user to sign a financing agreement or a loan agreement after the request passes.
4. A knowledge-graph based personalized knowledge service recommendation system according to claim 1, characterized in that: the system also comprises a question-answer community service module which is used for the users to issue questions and answer questions in the community.
5. A knowledge-graph based personalized knowledge service recommendation system according to claim 1, characterized in that: the knowledge graph construction unit comprises the following steps when constructing a knowledge graph system:
acquiring a knowledge point adding request initiated by a user; the knowledge point adding request comprises a knowledge point name, knowledge point description and knowledge point mastering degree;
and inquiring whether the current knowledge map has knowledge point information in the knowledge point adding request, if so, returning that the current knowledge point exists, if not, storing the current knowledge point into a knowledge map system, acquiring the mapping relation between the current knowledge point and other knowledge points in the knowledge map, and if so, establishing the association relation between the two knowledge points.
6. A knowledge-graph based personalized knowledge service recommendation system according to claim 1, characterized in that: the knowledge learning path planning unit comprises the following steps when recommending new knowledge point learning to a user:
acquiring the current knowledge points which are just learned, screening out the knowledge points which are associated with the knowledge points which are just learned, and exiting if no associated knowledge points exist; and if the associated knowledge points are associated, further screening the associated knowledge points which are not learned, and learning the knowledge point with the lowest level in the screened associated knowledge points, wherein the level of the knowledge point refers to the number of the preposed knowledge points of the knowledge point.
7. The system of claim 6, wherein the knowledge-graph based personalized knowledge service recommendation system comprises: when the knowledge extension unit pushes the extended knowledge points to the user, the method comprises the following steps:
acquiring a current knowledge graph of a user;
inquiring a cloud map with the highest similarity to the current knowledge map;
acquiring a knowledge point which is different from the current knowledge graph in the inquired cloud graph;
and taking the knowledge point with the lowest level from the different knowledge points to push the user.
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