CN110674307A - Knowledge deduction method and system for knowledge center network - Google Patents
Knowledge deduction method and system for knowledge center network Download PDFInfo
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- CN110674307A CN110674307A CN201910772393.0A CN201910772393A CN110674307A CN 110674307 A CN110674307 A CN 110674307A CN 201910772393 A CN201910772393 A CN 201910772393A CN 110674307 A CN110674307 A CN 110674307A
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Abstract
The embodiment of the invention provides a knowledge deduction method and a knowledge deduction system for a knowledge center network, wherein the knowledge deduction method comprises the following steps: analyzing and processing the knowledge correlation of any knowledge according to a knowledge address list of a knowledge center network to obtain a correlation knowledge vector; carrying out standardization processing on the correlation knowledge vector to obtain a knowledge graph set based on the correlation knowledge vector; and performing fusion deduction on the knowledge graph of the knowledge graph set to obtain new knowledge of the knowledge center network. The embodiment of the invention enables the knowledge with correlation in the knowledge center network to be mutually fused, and deduces new knowledge with higher value, thereby realizing the efficient utilization of knowledge information.
Description
Technical Field
The invention relates to the technical field of electronic information, in particular to a knowledge deduction method and a knowledge deduction system for a knowledge center network.
Background
The knowledge center network is a social network among knowledge participants, and can realize creation and transmission of knowledge outside individuals, organizations and organizations, and people can cooperate and exchange information through the knowledge center network. The goal is to connect technology with people to achieve an effective combination of intellectual capital, structural capital, and customer capital. The method can be divided into an internal knowledge center network and an external knowledge center network, wherein the former emphasizes knowledge exchange between internal staff and organizations in the organization, and the latter emphasizes knowledge sources outside the organization, including communities, national social relations and competitors.
Knowledge Graph (knowledgegraph) as an important component of the Knowledge-centric network describes concepts and their interrelations in the real physical world in symbolic form, providing the ability to analyze questions from a "relational" perspective, making Knowledge accessible (search), queryable (question-answer), supportable for action (decision). With the development of the information age, the knowledge graph is an important way and method for solving the challenges of many traditional computing technologies as a structural and semantic expression mode of knowledge. At present, the knowledge information in the knowledge base of the existing knowledge center network has a single source, and most of knowledge base is updated by external knowledge, so that the existing knowledge information cannot be better utilized.
Therefore, a knowledge deduction method and system for knowledge-centric networking are needed to solve the above problems.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a knowledge deduction method and a knowledge deduction system for a knowledge center network.
In a first aspect, an embodiment of the present invention provides a knowledge deduction method for a knowledge-centric network, including:
analyzing and processing the knowledge correlation of any knowledge according to a knowledge address list of a knowledge center network to obtain a correlation knowledge vector;
carrying out standardization processing on the correlation knowledge vector to obtain a knowledge graph set based on the correlation knowledge vector;
and performing fusion deduction on the knowledge graph of the knowledge graph set to obtain new knowledge of the knowledge center network.
Further, the analyzing and processing the knowledge correlation of any knowledge according to the knowledge address list of the knowledge center network to obtain a correlation knowledge vector includes:
acquiring a plurality of second knowledge according to the first knowledge and the knowledge address list, wherein the second knowledge is the existing knowledge related to the first knowledge in the knowledge center network;
and acquiring a correlation knowledge vector based on the first knowledge and the plurality of second knowledge according to the first knowledge and the plurality of second knowledge.
Further, after the normalizing the correlation knowledge vector to obtain the set of knowledge maps based on the correlation knowledge vector, the method further includes:
and acquiring the knowledge types of the plurality of second knowledge according to the knowledge type of the first knowledge, so as to be used for performing fusion deduction on the knowledge graph of the knowledge graph set.
Further, after the normalizing the correlation knowledge vector to obtain the set of knowledge maps based on the correlation knowledge vector, the method further includes:
and acquiring knowledge value information of the plurality of second knowledge according to the knowledge value information of the first knowledge, so as to be used for performing fusion deduction on the knowledge graph of the knowledge graph set.
Further, the fusion deduction of the knowledge graph set to obtain new knowledge of the knowledge center network includes:
and performing fusion deduction on the knowledge graph of the knowledge graph set according to the knowledge types and the knowledge value information of the plurality of second knowledge to obtain new knowledge of the knowledge center network so as to update the knowledge base of the knowledge center network.
In a second aspect, an embodiment of the present invention provides a knowledge deduction system for a knowledge-centric network, including:
the analysis module is used for analyzing and processing the knowledge correlation of any knowledge according to the knowledge address list of the knowledge center network to obtain a correlation knowledge vector;
the conversion module is used for carrying out standardization processing on the correlation knowledge vector to obtain a knowledge graph set based on the correlation knowledge vector;
and the fusion deduction module is used for performing fusion deduction on the knowledge graph of the knowledge graph set to obtain new knowledge of the knowledge center network.
Further, the analysis module includes:
the first processing unit is used for acquiring a plurality of second knowledge according to the first knowledge and the knowledge address list, wherein the second knowledge is the prior knowledge related to the first knowledge in the knowledge center network;
a second processing unit, configured to obtain, according to the first knowledge and the plurality of second knowledge, a correlation knowledge vector based on the first knowledge and the plurality of second knowledge.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method provided in the first aspect when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method as provided in the first aspect.
The knowledge deduction method and system for the knowledge center network provided by the embodiment of the invention can mutually fuse knowledge with correlation in the knowledge center network, and deduces new knowledge with higher value, thereby realizing efficient utilization of knowledge information.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a knowledge deduction method for a knowledge-centric network according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a knowledge deduction system for a knowledge-centric network according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
"knowledge" is a summary of the knowledge of objective facts (things) and human experience. In particular, knowledge is a summary of the knowledge and human experience of objective facts (things), abstracted from daily routines, procedures, executions, and specifications, including structured experience, value, and textual information, that has the value of guiding decisions and actions. Although some meaningful information in data is given, the value of the information usually begins to decline after the time utility is invalid, and valuable parts of the information are precipitated only by means of induction, deduction, comparison and the like of people participating in the mining of the information and are combined in an existing human knowledge system, so that the valuable information is converted into knowledge.
The important difference between knowledge and information, data, i.e. knowledge must be valuable. Knowledge is further refined abstraction and value improvement of information, and is a result of explaining and evaluating the information, and the information is processed in a purposeful and meaningful way, so that rules and principle relations among the information can be expressed or predicted, and the evaluation for determining the authenticity of the information is included. It follows that data is the carrier of information; the information is a form reflecting the essential characteristics of things and is an important basis for management and decision making; knowledge is the reflection of the representation, characteristics, rules and relationships of objects on data, and can support better decisions and bring better results. Specifically, knowledge can be divided into procedural knowledge and declarative knowledge, wherein the procedural knowledge is associated with a certain problem, is activated in the presence of a certain problem context, and is then executed. Declarative knowledge and procedural knowledge are distinguished from the perspective of knowledge processing, but they are interrelated in practical learning and problem solving activities: the declarative knowledge can be used for executing a certain experimental operation program to provide necessary information, is a basis for creation and is a basis for learning procedural knowledge in learning; in turn, the grasp of procedural knowledge also promotes the deepening of the declarative knowledge, and has an important meaning for learning the declarative knowledge.
The embodiment of the invention constructs the knowledge map set through the knowledge address list information, and deduces and predicts according to the links on the knowledge map set, and the essence of the invention is to deduce new and unknown knowledge according to the existing knowledge in the knowledge base of the knowledge center network, thereby establishing a more comprehensive knowledge base.
Specifically, the knowledge described in the embodiments of the present invention is knowledge in a large-scale military equipment model knowledge graph, abstracted from daily military tasks, processes, executions and specifications, including structured experience, value, and textual information, and has value in guiding decisions and actions. Knowledge deduction oriented to the knowledge graph aims at deducing new knowledge or inducing implied facts according to the existing knowledge in the knowledge graph and achieving evolution updating of the knowledge graph and knowledge deduction capacity. In addition, in the current internet era, new knowledge is continuously emerging from network space and military practice, the knowledge graph faces the problem of incompleteness, and the knowledge graph evolution aims to expand and update the existing knowledge graph so that the knowledge graph can model the current latest facts (things).
Fig. 1 is a schematic flow chart of a knowledge deduction method for a knowledge-centric network according to an embodiment of the present invention, and as shown in fig. 1, an embodiment of the present invention provides a knowledge deduction method for a knowledge-centric network, including:
In the embodiment of the invention, any knowledge pair in the knowledge center network is analyzed based on a real-time knowledge address list of the knowledge center network, so that a correlation knowledge vector is obtained, wherein in the embodiment of the invention, any knowledge in the knowledge center network can be set as a piece of preset specific knowledge, and second knowledge with correlation is obtained from the knowledge address list of the knowledge center network according to the knowledge address of the preset specific knowledge, so that the corresponding correlation knowledge vector is obtained according to the second knowledge with correlation. It should be noted that, in the embodiment of the present invention, the information for presetting the specific knowledge is known, and includes information types, information knowledge values, and the like.
Specifically, in the embodiment of the present invention, a preset specific knowledge k is set, and according to a real-time knowledge address list of a knowledge center network, the preset specific knowledge k is analyzed and processed by a correlation knowledge vector acquisition algorithm, so as to obtain a correlation knowledge vector Vkr of the preset specific knowledge k, where the correlation knowledge vector acquisition algorithm is:
and 102, carrying out standardization processing on the correlation knowledge vector to obtain a knowledge graph set based on the correlation knowledge vector.
In the embodiment of the invention, the correlation knowledge vector is standardized by a knowledge graph set acquisition algorithm, so that the correlation knowledge vector is converted into a knowledge graph set, wherein the knowledge graph set acquisition algorithm comprises the following steps:
and 103, performing fusion deduction on the knowledge graph of the knowledge graph set to obtain new knowledge of the knowledge center network.
In the embodiment of the invention, fusion deduction is carried out according to the knowledge type and the knowledge value of each knowledge in the knowledge graph set, so that new knowledge is obtained based on the deduction of the knowledge graph set, and the knowledge base of the knowledge center network is updated through the new knowledge.
The knowledge deduction method for the knowledge center network provided by the embodiment of the invention enables knowledge with correlation in the knowledge center network to be mutually fused, and deduces new knowledge with higher value, thereby realizing efficient utilization of knowledge information.
On the basis of the above embodiment, the analyzing and processing the knowledge correlation of any knowledge according to the knowledge address list of the knowledge center network to obtain a correlation knowledge vector includes:
acquiring a plurality of second knowledge according to the first knowledge and the knowledge address list, wherein the second knowledge is the existing knowledge related to the first knowledge in the knowledge center network;
and acquiring a correlation knowledge vector based on the first knowledge and the plurality of second knowledge according to the first knowledge and the plurality of second knowledge.
In an embodiment of the invention, a plurality of second knowledge related to the first knowledge is obtained from a knowledge base of the knowledge-centric network according to the information of the first knowledge in the knowledge address list, and a correlation knowledge vector of the first knowledge and the second knowledge is obtained according to a corresponding algorithm pseudo code (e.g., the correlation knowledge vector obtaining algorithm of the above-described embodiment).
On the basis of the above embodiment, after the normalizing the correlation knowledge vector to obtain the set of knowledge maps based on the correlation knowledge vector, the method further includes:
and acquiring the knowledge types of the plurality of second knowledge according to the knowledge type of the first knowledge, so as to be used for performing fusion deduction on the knowledge graph of the knowledge graph set.
In the embodiment of the invention, according to the knowledge type kt of the first knowledge k in the knowledge graph set g, through a knowledge type formula:
acquiring the knowledge type ft (g) of all the second knowledge in the knowledge graph set g, it should be noted that, in the embodiment of the present invention, the knowledge type represents the type features contained in the knowledge, such as images, sounds, words, or pulses, and each knowledge includes at least one of the type features.
On the basis of the above embodiment, after the normalizing the correlation knowledge vector to obtain the set of knowledge maps based on the correlation knowledge vector, the method further includes:
and acquiring knowledge value information of the plurality of second knowledge according to the knowledge value information of the first knowledge, so as to be used for performing fusion deduction on the knowledge graph of the knowledge graph set.
In the embodiment of the invention, according to knowledge value information kv of the first knowledge k in the knowledge graph set g, through a knowledge value formula:
acquiring knowledge value information fv (g) of all second knowledge in the knowledge graph set g, it should be noted that, in the embodiment of the present invention, the knowledge value information represents knowledge fields related to knowledge, such as a ship, an airplane, a vehicle, or a tank, and each knowledge includes at least one of the knowledge fields.
On the basis of the above embodiment, the fusion deduction of the knowledge graph set to obtain new knowledge of the knowledge center network includes:
and performing fusion deduction on the knowledge graph of the knowledge graph set according to the knowledge types and the knowledge value information of the plurality of second knowledge to obtain new knowledge of the knowledge center network so as to update the knowledge base of the knowledge center network.
In the embodiment of the invention, the formula is deduced through knowledge:
and in the deduction process, the knowledge type kt 'of the new knowledge fe (gk) is deduced according to the intersection of the knowledge types of all the second knowledge in the knowledge graph set, and the knowledge value information kv' of the new knowledge fe (gk) is deduced according to the union of the knowledge value information of all the second knowledge in the knowledge graph set. And updating the knowledge base of the knowledge center network according to the new knowledge deduced from the knowledge map set, thereby establishing a more comprehensive knowledge base.
Fig. 2 is a schematic structural diagram of a knowledge deduction system for a knowledge-centric network according to an embodiment of the present invention, and as shown in fig. 2, the knowledge deduction system for a knowledge-centric network according to an embodiment of the present invention includes an analysis module 201, a conversion module 202, and a fusion deduction module 203, where the analysis module 201 is configured to analyze and process knowledge correlation of any knowledge according to a knowledge address list of the knowledge-centric network to obtain a correlation knowledge vector; the conversion module 202 is configured to perform normalization processing on the correlation knowledge vector to obtain a knowledge graph set based on the correlation knowledge vector; the fusion deduction module 203 is configured to perform fusion deduction on the knowledge graph of the knowledge graph set to obtain new knowledge of the knowledge center network.
In this embodiment of the present invention, the analysis module 201 analyzes any knowledge pair in the knowledge-centric network based on the real-time knowledge address list of the knowledge-centric network, so as to obtain a relevant knowledge vector, where in this embodiment of the present invention, any knowledge may be set as a piece of preset specific knowledge, and according to the knowledge address of the preset specific knowledge, second knowledge with relevance is obtained from the knowledge address list of the knowledge-centric network, so as to obtain a corresponding relevant knowledge vector according to the second knowledge with relevance. It should be noted that, in the embodiment of the present invention, the information for presetting the specific knowledge is known, and includes information types, information knowledge values, and the like.
Specifically, the analysis module 201 obtains a preset specific knowledge k, and analyzes and processes the preset specific knowledge k through a correlation knowledge vector obtaining algorithm according to a real-time knowledge address list of the knowledge center network, so as to obtain a correlation knowledge vector Vkr of the preset specific knowledge k, where the correlation knowledge vector obtaining algorithm is:
then, the conversion module 202 performs normalization processing on the correlation knowledge vector through a knowledge graph set acquisition algorithm, so as to convert the correlation knowledge vector into a knowledge graph set. The knowledge graph set acquisition algorithm comprises the following steps:
finally, the fusion deduction module 203 performs fusion deduction according to the knowledge type and knowledge value of each knowledge in the knowledge graph set, thereby obtaining new knowledge based on the knowledge graph set deduction, and updating the knowledge base of the knowledge center network through the new knowledge.
The knowledge deduction system for the knowledge center network provided by the embodiment of the invention enables knowledge with correlation in the knowledge center network to be mutually fused, and deduces new knowledge with higher value, thereby realizing efficient utilization of knowledge information.
On the basis of the above embodiment, the analysis module includes a first processing unit and a second processing unit, where the first processing unit is configured to obtain a plurality of second knowledge according to first knowledge and a knowledge address list, where the second knowledge is existing knowledge related to the first knowledge in the knowledge-centric network; the second processing unit is used for acquiring a correlation knowledge vector based on the first knowledge and the plurality of second knowledge according to the first knowledge and the plurality of second knowledge.
The system provided by the embodiment of the present invention is used for executing the above method embodiments, and for details of the process and the details, reference is made to the above embodiments, which are not described herein again.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and referring to fig. 3, the electronic device may include: a processor (processor)301, a communication Interface (communication Interface)302, a memory (memory)303 and a communication bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the communication bus 304. Processor 301 may call logic instructions in memory 303 to perform the following method: analyzing and processing the knowledge correlation of any knowledge according to a knowledge address list of a knowledge center network to obtain a correlation knowledge vector; carrying out standardization processing on the correlation knowledge vector to obtain a knowledge graph set based on the correlation knowledge vector; and performing fusion deduction on the knowledge graph of the knowledge graph set to obtain new knowledge of the knowledge center network.
In addition, the logic instructions in the memory 303 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to execute the knowledge deduction method for a knowledge-centric network provided in the foregoing embodiments, for example, including: analyzing and processing the knowledge correlation of any knowledge according to a knowledge address list of a knowledge center network to obtain a correlation knowledge vector; carrying out standardization processing on the correlation knowledge vector to obtain a knowledge graph set based on the correlation knowledge vector; and performing fusion deduction on the knowledge graph of the knowledge graph set to obtain new knowledge of the knowledge center network.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A knowledge deduction method for a knowledge-centric network, comprising:
analyzing and processing the knowledge correlation of any knowledge according to a knowledge address list of a knowledge center network to obtain a correlation knowledge vector;
carrying out standardization processing on the correlation knowledge vector to obtain a knowledge graph set based on the correlation knowledge vector;
and performing fusion deduction on the knowledge graph of the knowledge graph set to obtain new knowledge of the knowledge center network.
2. The knowledge deduction method for the knowledge-centric network according to claim 1, wherein the analyzing and processing the knowledge correlation of any knowledge according to the knowledge address list of the knowledge-centric network to obtain the correlation knowledge vector comprises:
acquiring a plurality of second knowledge according to the first knowledge and the knowledge address list, wherein the second knowledge is the existing knowledge related to the first knowledge in the knowledge center network;
and acquiring a correlation knowledge vector based on the first knowledge and the plurality of second knowledge according to the first knowledge and the plurality of second knowledge.
3. The knowledge deduction method for knowledge-centric network according to claim 2, wherein after the normalizing the relevant knowledge vectors to obtain the set of knowledge-graphs based on the relevant knowledge vectors, the method further comprises:
and acquiring the knowledge types of the plurality of second knowledge according to the knowledge type of the first knowledge, so as to be used for performing fusion deduction on the knowledge graph of the knowledge graph set.
4. The knowledge deduction method for knowledge-centric network according to claim 3, wherein after the normalizing the relevant knowledge vectors to obtain the set of knowledge-graphs based on the relevant knowledge vectors, the method further comprises:
and acquiring knowledge value information of the plurality of second knowledge according to the knowledge value information of the first knowledge, so as to be used for performing fusion deduction on the knowledge graph of the knowledge graph set.
5. The knowledge deduction method for the knowledge-centric network according to claim 4, wherein the fusion deduction of the knowledge-graphs of the knowledge-graph set to obtain new knowledge of the knowledge-centric network comprises:
and performing fusion deduction on the knowledge graph of the knowledge graph set according to the knowledge types and the knowledge value information of the plurality of second knowledge to obtain new knowledge of the knowledge center network so as to update the knowledge base of the knowledge center network.
6. A knowledge deduction system for a knowledge-centric network, comprising:
the analysis module is used for analyzing and processing the knowledge correlation of any knowledge according to the knowledge address list of the knowledge center network to obtain a correlation knowledge vector;
the conversion module is used for carrying out standardization processing on the correlation knowledge vector to obtain a knowledge graph set based on the correlation knowledge vector;
and the fusion deduction module is used for performing fusion deduction on the knowledge graph of the knowledge graph set to obtain new knowledge of the knowledge center network.
7. The knowledge deduction system for a knowledge-centric network of claim 6, wherein the analysis module comprises:
the first processing unit is used for acquiring a plurality of second knowledge according to the first knowledge and the knowledge address list, wherein the second knowledge is the prior knowledge related to the first knowledge in the knowledge center network;
a second processing unit, configured to obtain, according to the first knowledge and the plurality of second knowledge, a correlation knowledge vector based on the first knowledge and the plurality of second knowledge.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, implements the steps of the knowledge deduction method for a knowledge-centric network as claimed in any one of claims 1 to 5.
9. A non-transitory computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, performs the steps of the knowledge deduction method for a knowledge-centric network according to any of the claims 1 to 5.
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