CN112527924A - Dynamically updated knowledge graph expansion method and device - Google Patents
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Abstract
The invention provides a method and a device for expanding a dynamically updated knowledge graph, wherein the method comprises the following steps: constructing a knowledge graph based on a first knowledge base to generate the knowledge graph of the target field, wherein the first knowledge base is composed of original data of the target field; generating a second knowledge base based on the new knowledge of the target field, and expanding data in the second knowledge base into the knowledge graph to obtain an updated knowledge graph; and/or executing a knowledge enabling operation based on the knowledge graph to obtain an application scheme of the knowledge graph, executing a knowledge reasoning operation on the application scheme to obtain a third knowledge base, and expanding data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph. The dynamically updated knowledge graph expanding method and device provided by the invention can realize the dynamic expansion of the knowledge graph and ensure the dynamic updating and real-time performance of the knowledge graph.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for expanding a dynamically updated knowledge graph.
Background
The knowledge graph is a database for storing knowledge, is a concept formally proposed by google corporation in 2012, and is mainly used for enhancing the search efficiency and improving the user experience in the era of high-speed internet development and explosive network data growth. The knowledge graph establishes a foundation for intelligent information application by virtue of excellent semantic processing technology and interconnectivity, is widely applied to the aspects of search, question answering, information analysis and the like, and promotes the development of information technology from information service to knowledge service. In recent years, all walks of life are researching and applying the knowledge map to the professional field and better serve the specific field.
However, at present, the knowledge graph is basically constructed and used directly, and a mechanism for dynamic updating and expansion from the use process is not provided, so that the real-time performance is poor.
Disclosure of Invention
The invention provides a method and a device for expanding a dynamically updated knowledge graph, which are used for solving the defects of poor real-time performance caused by the fact that no mechanism for dynamically updating and expanding in the using process exists in the prior art.
The invention provides a dynamically updated knowledge graph expanding method, which comprises the following steps:
constructing a knowledge graph based on a first knowledge base to generate the knowledge graph of the target field, wherein the first knowledge base is composed of original data of the target field;
generating a second knowledge base based on the new knowledge of the target field, and expanding data in the second knowledge base into the knowledge graph to obtain an updated knowledge graph; and/or the presence of a gas in the gas,
and executing knowledge enabling operation based on the knowledge graph to obtain an application scheme of the knowledge graph, executing knowledge reasoning operation on the application scheme to obtain a third knowledge base, and expanding data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph.
According to the dynamically updated knowledge graph expanding method provided by the invention, the construction of the knowledge graph based on the first knowledge base to generate the knowledge graph of the target field comprises the following steps:
preprocessing the original data of the target field to obtain preprocessed target data;
executing knowledge modeling operation based on the preprocessed target data to obtain concept data, and upper and lower relations among different concepts and concept attribute data;
executing knowledge acquisition operation based on the preprocessed target data to obtain instance data, a relation between an instance and a concept and instance attribute data;
and executing knowledge fusion operation according to the concept data, the superior-inferior relation and the concept attribute data among different concepts, the example data, the relation among the examples and the concepts and the example attribute data, and generating the knowledge graph of the target field.
According to the dynamically updated knowledge graph expanding method provided by the invention, the step of executing the knowledge reasoning operation on the application scheme to obtain a third knowledge base comprises the following steps:
determining a feedback evaluation result of the knowledge graph user on the application scheme based on the application scheme;
and performing semantic reasoning or preset reasoning by using knowledge graph data corresponding to the application scheme based on the feedback evaluation result to obtain new knowledge, and generating a third knowledge base.
According to the dynamically updated knowledge graph expanding method provided by the invention, the knowledge modeling operation is executed based on the preprocessed target data to obtain concept data, the superior-inferior relation between different concepts and the concept attribute data, and the method comprises the following steps:
executing concept acquisition operation based on the preprocessed target data to obtain concept data;
executing concept context generation operation based on the preprocessed target data to obtain the upper and lower relations among different concepts;
and executing concept attribute acquisition operation based on the preprocessed target data to obtain concept attribute data.
According to the dynamically updated knowledge graph expanding method provided by the invention, the method for executing knowledge acquisition operation based on the preprocessed target data to obtain instance data, the relation between an instance and a concept and instance attribute data comprises the following steps:
executing instance extraction operation based on the preprocessed target data to obtain instance data;
executing instance classification operation based on the preprocessed target data to obtain the relation between the instances and the concepts;
and executing instance attribute extraction operation based on the preprocessed target data to obtain instance attribute data.
According to the method for dynamically updating the knowledge graph expansion provided by the invention, the knowledge fusion operation is executed according to the concept data, the superior-inferior relation and the concept attribute data among different concepts, the example data, the relation among examples and concepts and the example attribute data, and the knowledge graph of the target field is generated, and the method comprises the following steps:
executing concept fusion operation according to the concept data, the superior-inferior relation and the concept attribute data among different concepts, the example data, the relation among the examples and the concepts and the example attribute data, and realizing the data alignment of the concept layer;
executing instance fusion operation according to the concept data, the superior-inferior relation and the concept attribute data among different concepts, the instance data, the relation among the instances and the concepts and the instance attribute data, and realizing the alignment of the data of the instance layer;
and executing a relationship fusion operation according to the concept data, the superior-inferior relationship and the concept attribute data among different concepts, the instance data, the relationship among the instances and the concepts and the instance attribute data, realizing the alignment of the relationship among the concepts, the relationship among the concepts and the instances and the relationship among the instances, and generating the knowledge graph of the target field.
According to the dynamically updated knowledge graph expanding method provided by the invention, the expanding the data in the second knowledge base into the knowledge graph to obtain the updated knowledge graph comprises the following steps:
sequentially performing preprocessing, knowledge modeling, knowledge acquisition and knowledge fusion operations on the data in the second knowledge base to obtain a first extended knowledge graph of the target field;
repeatedly detecting based on the first extended knowledge graph of the target field and the knowledge graph of the target field, distinguishing repeated knowledge by state information, and fusing the first extended knowledge graph of the target field and the knowledge graph of the target field to obtain an updated knowledge graph;
the expanding the data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph includes:
sequentially performing preprocessing, knowledge modeling, knowledge acquisition and knowledge fusion operations on the data in the third knowledge base to obtain a second extended knowledge graph of the target field;
and repeatedly detecting based on the second extended knowledge graph of the target field and the knowledge graph of the target field, distinguishing repeated knowledge by using state information, and fusing the second extended knowledge graph of the target field and the knowledge graph of the target field to obtain an updated knowledge graph.
The invention also provides a dynamically updated knowledge graph expanding device, which comprises:
the knowledge graph construction unit is used for constructing a knowledge graph based on a first knowledge base to generate the knowledge graph of the target field, wherein the first knowledge base is composed of original data of the target field;
the knowledge graph expanding unit is used for generating a second knowledge base based on the new knowledge of the target field, expanding data in the second knowledge base into the knowledge graph and obtaining an updated knowledge graph; and/or the presence of a gas in the gas,
and executing knowledge enabling operation based on the knowledge graph to obtain an application scheme of the knowledge graph, executing knowledge reasoning operation on the application scheme to obtain a third knowledge base, and expanding data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph.
The present invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the dynamically updated knowledge-graph extension method as described in any of the above when executing the program.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the dynamically updated knowledge-graph extension method as described in any one of the above.
The dynamically updated knowledge graph expanding method and device provided by the invention can realize the dynamic expansion of the knowledge graph and ensure the dynamic updating and real-time performance of the knowledge graph.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for 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 diagram of a method for dynamically updating an expansion knowledge base according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of creating a knowledge graph of a target domain based on a first knowledge base according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating semantic level knowledge inference provided by embodiments of the present invention;
FIG. 4 is a schematic structural diagram of a dynamically updated knowledge-map expanding apparatus provided in the present invention;
fig. 5 is a schematic physical structure 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 present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, 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.
The dynamically updated knowledge-graph expansion method and apparatus of the present invention are described below in conjunction with FIGS. 1-5.
The terms to which the present invention relates will be explained first.
Knowledge graph: a database storing knowledge, in which are stored triplets, such as yaoming, birth place, shanghai, etc., each of which represents a fact. The knowledge graph can also be seen in the form of a graph, such as the above triples, where Yaoming and Shanghai are nodes, and the radix rehmanniae is a line of Yaoming pointing to Shanghai and having a label.
The concept is as follows: a class of entities in a knowledge graph, such as fruits, pomes, and the like.
Entity: specific real objects in the knowledge map, such as apple, hawthorn and the like.
The attributes are as follows: the knowledge-graph includes the characteristics of the concept or entity, such as the origin and color of apple.
The relationship is as follows: the relationship between the concept, the entity and the attribute in the knowledge graph and the knowledge graph, for example, the entity apple is one of the entities under the concept of fruit, and the color attribute of the apple can be red, pink, golden yellow and the like.
Fig. 1 is a schematic flow chart of a dynamically updated knowledge-graph expanding method according to an embodiment of the present invention, as shown in fig. 1, the method includes:
and aiming at a certain target field, constructing a knowledge graph based on a first knowledge base formed by original data of the target field to generate the knowledge graph of the target field.
and executing knowledge enabling operation based on the knowledge graph to obtain an application scheme of the knowledge graph, executing knowledge reasoning operation on the application scheme to obtain a third knowledge base, and expanding data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph.
In the present invention, the constructed knowledge graph of the target domain is extended in consideration of two cases.
Optionally, in the first case, based on the new knowledge of the target field, a second knowledge base is generated, and data in the second knowledge base is expanded into the knowledge graph to obtain an updated knowledge graph.
It can be understood that as the external knowledge grows or new knowledge appears after the original knowledge is modified, a second knowledge base can be generated, and the data in the second knowledge base is expanded into the established knowledge graph of the target field, so that the dynamic expansion of the knowledge graph is realized.
It should be noted that the source of the data in the second knowledge base is other databases which are not in the scope of the source of the previously constructed knowledge graph data (i.e. the first knowledge base), and the source of the other databases may appear along with the social development, for example, a new research work comes out in a certain field, and the related knowledge of the new research work needs to be added to the knowledge graph in the field.
For example, in the music domain knowledge graph, a singer newly releases a new album, and the information of the album needs to be used as a second knowledge base for expanding the knowledge graph.
Optionally, in the second case, based on the knowledge graph, a knowledge enabling operation is performed to obtain an application scheme of the knowledge graph, a knowledge inference operation is performed on the application scheme to obtain a third knowledge base, and data in the third knowledge base is extended to the knowledge graph to obtain an updated knowledge graph.
And applying the knowledge graph to obtain an application scheme by executing a knowledge enabling operation on the knowledge graph in the target field, executing a knowledge reasoning operation on the application scheme in the application process to obtain a knowledge reasoning result, further generating a third knowledge base, and then expanding data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph.
For example, a knowledge graph of a user system is constructed based on user information, and in the using process, the user 'Liu somebody' wants to inquire the native information of the user, but no relevant information exists in the knowledge graph at present, but the identity number of the user 'Liu somebody' is '11010119900307 XXXX'. Most people who have the first 6 numbers of the other people's identification numbers in the knowledge graph as ' 1101010 ' native place in Beijing City can be inquired, so that certain native place in Liu is inferred to be ' the Dongchong district in Beijing City '. Then, the attribute 'native place' with the 6 first digits of the identification number being '110101' and the attribute value 'Beijing City east city' can be used as data of a third knowledge base to be added into the knowledge graph of the user system, and the dynamic update of the existing knowledge graph is realized.
It should be noted that, in the case where both the second knowledge base and the third knowledge base exist, the knowledge graph is dynamically updated based on the second knowledge base and the third knowledge base, and if only the second knowledge base or the third knowledge base exists, the knowledge graph may be dynamically updated based on only the second knowledge base or the third knowledge base. The knowledge-graph may be dynamically updated based on the first scenario or the second scenario, or may be dynamically updated based on the first scenario and the second scenario.
Examples of dynamically updating the knowledge-graph based on the second and third repositories are provided below. And establishing a dynamically updated knowledge graph in the field of power customer service. Firstly, a power price related knowledge graph is established based on a power price execution file of 2010-2019 year as a first knowledge base, and a power price knowledge graph with 12 concepts, 689 entities and 1379 relations is constructed through the steps of knowledge modeling, knowledge acquisition, knowledge fusion and the like. And then, the power price map is used for serving the user, so that the effect of knowledge enabling is realized. The knowledge enabling process is embodied as a question and answer, for example, a certain user asks for "is the price of electricity for life of resident now? ", reverts to" 0.54-tuple ", which is a simple application. Continuing the above dialog, the user says "i remember that when he remembers 17 years, he is 0.5 yuan, so he is expensive. "the price changes to 0.54 yuan in 4 months in 18 years after being inquired from the knowledge graph, and the electricity price is really 0.5 yuan in 2017, but the electricity price changes to 0.54 yuan in 4 months in 2018, and the information does not exist in the original knowledge graph before, the data is used as new information to be sorted and converted into data in a third knowledge base, so that the expansion of the knowledge graph is realized. After the electricity price related file is released in 2020, the electricity price execution file in 2020 can be used as a second knowledge base to realize the expansion of the knowledge graph. Therefore, dynamic updating and expansion of the power price knowledge graph are achieved.
The dynamically updated knowledge graph expanding method and device provided by the invention can realize the dynamic expansion of the knowledge graph and ensure the dynamic updating and real-time performance of the knowledge graph.
Optionally, on the basis of the foregoing embodiment, as shown in fig. 2, the constructing a knowledge graph based on a first knowledge base to generate a knowledge graph of a target domain includes:
preprocessing refers to normalizing data before main processing.
Optionally, in an embodiment, the preprocessing the raw data to obtain preprocessed target data includes:
and performing abstract interception, text interception and information frame interception on the original data to obtain preprocessed target data.
knowledge modeling comprises the processes of concept acquisition, concept upper and lower generation, concept attribute acquisition and the like. The concept acquisition is to extract concept data from the preprocessed data, the concept context generation is to obtain the context relationship between different concepts from the original data through a certain rule, and the concept attribute extraction is to extract the concept attributes.
Optionally, in an embodiment, the performing knowledge modeling operation based on the preprocessed target data to obtain concept data, a context relationship between different concepts, and concept attribute data includes:
executing concept acquisition operation based on the preprocessed target data to obtain concept data;
executing concept context generation operation based on the preprocessed target data to obtain the upper and lower relations among different concepts;
and executing concept attribute acquisition operation based on the preprocessed target data to obtain concept attribute data.
the knowledge acquisition mainly comprises instance extraction, instance classification, instance attribute extraction and the like. The example extraction is to extract example data from the preprocessed data, the example classification is to extract the relation between the examples and the concepts from the preprocessed data, and the example attribute extraction is to extract the attribute data of the examples from the preprocessed data.
Optionally, in an embodiment, the executing a knowledge acquisition operation based on the preprocessed target data to obtain instance data, a relationship between an instance and a concept, and instance attribute data includes:
executing instance extraction operation based on the preprocessed target data to obtain instance data;
executing instance classification operation based on the preprocessed target data to obtain the relation between the instances and the concepts;
and executing instance attribute extraction operation based on the preprocessed target data to obtain instance attribute data.
The knowledge fusion mainly comprises concept fusion, instance fusion and relationship fusion, wherein the concept fusion mainly refers to fusion of concept layer data, the instance fusion mainly refers to fusion of instance layer data, and the relationship fusion refers to fusion of relationships between concepts, relationships between concepts and instances and relationships between instances.
Optionally, in an embodiment, the performing a knowledge fusion operation according to the concept data, the context relationship and the concept attribute data between different concepts, and the instance data, the relationship between the instance and the concept, and the instance attribute data to generate the knowledge graph of the target domain includes:
executing concept fusion operation according to the concept data, the superior-inferior relation and the concept attribute data among different concepts, the example data, the relation among the examples and the concepts and the example attribute data, and realizing the data alignment of the concept layer;
executing instance fusion operation according to the concept data, the superior-inferior relation and the concept attribute data among different concepts, the instance data, the relation among the instances and the concepts and the instance attribute data, and realizing the alignment of the data of the instance layer;
and executing a relationship fusion operation according to the concept data, the superior-inferior relationship and the concept attribute data among different concepts, the instance data, the relationship among the instances and the concepts and the instance attribute data, realizing the alignment of the relationship among the concepts, the relationship among the concepts and the instances and the relationship among the instances, and generating the knowledge graph of the target field.
In the embodiment of the invention, the knowledge graph construction in the target field is realized through the steps of preprocessing, knowledge modeling, knowledge acquisition, knowledge fusion and the like, upper-layer conceptual layer data can be constructed without expert knowledge, and the construction efficiency of the knowledge graph can be effectively improved.
Optionally, on the basis of the foregoing embodiment, the performing knowledge inference operation on the application scheme to obtain a third knowledge base includes:
determining a feedback evaluation result of the knowledge graph user on the application scheme based on the application scheme;
and performing semantic reasoning or preset reasoning by using knowledge graph data corresponding to the application scheme based on the feedback evaluation result to obtain new knowledge, and generating a third knowledge base.
Optionally, after the application scheme is implemented, a feedback evaluation result of the knowledge graph user can be obtained.
It is understood that the third knowledge base is obtained by inference based on the application scheme, and may be automatic semantic level inference or automatic inference according to preset rules.
The semantic reasoning is based on the semantic relation between terms to reason a certain relation. For example, a subclass relationship, such as a being a subclass of b and b being a subclass of c, infers that a is a subclass of c. The inferred relationships further include: simple mapping, sub-attributes, types, implicit types, reflexes of sub-attributes, sub-class reflexes, and the like, as shown in fig. 3, which is a schematic diagram of semantic knowledge inference provided by the embodiment of the present invention.
In FIG. 3, sp is subpartityof, representing a child property; sc represents a subcategory; type represents type; domain represents a domain; range represents a range; the variable a, b, p, q, r belongs to uri ^ blues ^ ulits, wherein uri is a uniform resource identifier, blanks is a blank space, and lis is a symbol and represents the meaning of the variable.
The preset rule may be an inference rule temporarily made according to actual conditions. For example, as in the aforementioned knowledge graph of the user system, for example, the user wants to query the native information of "Liu somebody", but there is no relevant information in the current knowledge graph, but there is an identity number of "Liu somebody", which is "11010119900307 XXXX". The reasoning rule at this time is to reason out the native information of "Liu somebody" according to the native information corresponding to the first 6 digits of the identification number of other people. For example, most people who have the first 6 numbers of the other people's identification numbers of "1101010" in the knowledge graph can be native to "the east city area of Beijing City", so that the fact that some place of Liu has the native to "the east city area of Beijing City" can be inferred. Then, the attribute 'native place' with the 6 first digits of the identification number being '110101' and the attribute value 'Beijing City east city' can be used as data of a third knowledge base to be added into the knowledge graph of the user system, and the dynamic update of the existing knowledge graph is realized.
In the embodiment of the invention, reasoning is carried out based on the feedback evaluation result of the knowledge graph user on the application scheme to obtain the third knowledge base, and then the data corresponding to the third knowledge base is expanded into the original knowledge graph, thereby realizing the dynamic update of the knowledge graph.
Optionally, on the basis of the foregoing embodiment, the expanding the data in the second knowledge base into the knowledge graph to obtain an updated knowledge graph includes:
sequentially performing preprocessing, knowledge modeling, knowledge acquisition and knowledge fusion operations on the data in the second knowledge base to obtain a first extended knowledge graph of the target field;
repeatedly detecting based on the first extended knowledge graph of the target field and the knowledge graph of the target field, distinguishing repeated knowledge by state information, and fusing the first extended knowledge graph of the target field and the knowledge graph of the target field to obtain an updated knowledge graph;
the expanding the data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph includes:
sequentially performing preprocessing, knowledge modeling, knowledge acquisition and knowledge fusion operations on the data in the third knowledge base to obtain a second extended knowledge graph of the target field;
and repeatedly detecting based on the second extended knowledge graph of the target field and the knowledge graph of the target field, distinguishing repeated knowledge by using state information, and fusing the second extended knowledge graph of the target field and the knowledge graph of the target field to obtain an updated knowledge graph.
It can be understood that the preprocessing, knowledge modeling, knowledge acquisition and knowledge fusion operations as described in fig. 2 are sequentially performed on the data in the second knowledge base to obtain the first extended knowledge graph of the target domain;
performing repeated detection based on the first extended knowledge graph of the target domain and the knowledge graph of the target domain.
And if the repeated knowledge exists, distinguishing the repeated knowledge by using the state information, and fusing the first extended knowledge graph of the target field and the knowledge graph of the target field to obtain an updated knowledge graph.
And if no repeated knowledge exists, directly fusing the first extended knowledge graph of the target field with the knowledge graph of the target field to obtain an updated knowledge graph.
It can be understood that all information about concepts, instances, attributes, relationships, etc. that exist originally needs to be preserved, and different version data are distinguished using state information.
Similarly, the expanding the data in the third knowledge base into the knowledge-graph to obtain an updated knowledge-graph includes:
sequentially performing preprocessing, knowledge modeling, knowledge acquisition and knowledge fusion operations on the data in the third knowledge base to obtain a second extended knowledge graph of the target field;
repeatedly detecting based on the second extended knowledge graph of the target field and the knowledge graph of the target field, if repeated knowledge exists, distinguishing the repeated knowledge by using state information, and fusing the second extended knowledge graph of the target field and the knowledge graph of the target field to obtain an updated knowledge graph; and if no repeated knowledge exists, fusing the second extended knowledge graph of the target field with the knowledge graph of the target field to obtain an updated knowledge graph.
In the embodiment of the invention, the constructed knowledge graph is expanded by utilizing the second knowledge base and/or the third knowledge base, so that the dynamic expansion of the knowledge graph can be realized, and the dynamic real-time property of the knowledge graph is ensured.
The present invention provides a dynamically updated knowledge-graph extension apparatus, which can be referred to in correspondence with the above-described dynamically updated knowledge-graph extension method.
Fig. 4 is a schematic structural diagram of a dynamically updated knowledge-map expanding apparatus provided in the present invention, including: a knowledge-graph construction unit 410 and a knowledge-graph expansion unit 420, wherein,
a knowledge graph constructing unit 410, configured to construct a knowledge graph based on a first knowledge base, and generate a knowledge graph of a target field, where the first knowledge base is composed of original data of the target field;
a knowledge graph expanding unit 420, configured to generate a second knowledge base based on new knowledge of the target domain, and expand data in the second knowledge base into the knowledge graph to obtain an updated knowledge graph; and/or the presence of a gas in the gas,
and executing knowledge enabling operation based on the knowledge graph to obtain an application scheme of the knowledge graph, executing knowledge reasoning operation on the application scheme to obtain a third knowledge base, and expanding data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph.
Optionally, the knowledge graph constructing unit 410 is configured to:
preprocessing the original data of the target field to obtain preprocessed target data;
executing knowledge modeling operation based on the preprocessed target data to obtain concept data, and upper and lower relations among different concepts and concept attribute data;
executing knowledge acquisition operation based on the preprocessed target data to obtain instance data, a relation between an instance and a concept and instance attribute data;
and executing knowledge fusion operation according to the concept data, the superior-inferior relation and the concept attribute data among different concepts, the example data, the relation among the examples and the concepts and the example attribute data, and generating the knowledge graph of the target field.
Optionally, the performing knowledge inference operation on the application scheme to obtain a third knowledge base includes:
determining a feedback evaluation result of the knowledge graph user on the application scheme based on the application scheme;
and performing semantic reasoning or preset reasoning by using knowledge graph data corresponding to the application scheme based on the feedback evaluation result to obtain new knowledge, and generating a third knowledge base.
Optionally, the performing knowledge modeling operation based on the preprocessed target data to obtain concept data, a context relationship between different concepts, and concept attribute data includes:
executing concept acquisition operation based on the preprocessed target data to obtain concept data;
executing concept context generation operation based on the preprocessed target data to obtain the upper and lower relations among different concepts;
and executing concept attribute acquisition operation based on the preprocessed target data to obtain concept attribute data.
Optionally, the performing knowledge modeling operation based on the preprocessed target data to obtain concept data, a context relationship between different concepts, and concept attribute data includes:
executing concept acquisition operation based on the preprocessed target data to obtain concept data;
executing concept context generation operation based on the preprocessed target data to obtain the upper and lower relations among different concepts;
and executing concept attribute acquisition operation based on the preprocessed target data to obtain concept attribute data.
Optionally, the executing a knowledge fusion operation according to the concept data, the superior-inferior relationship and the concept attribute data between different concepts, and the instance data, the relationship between the instance and the concept, and the instance attribute data, to generate the knowledge graph of the target field includes:
executing concept fusion operation according to the concept data, the superior-inferior relation and the concept attribute data among different concepts, the example data, the relation among the examples and the concepts and the example attribute data, and realizing the data alignment of the concept layer;
executing instance fusion operation according to the concept data, the superior-inferior relation and the concept attribute data among different concepts, the instance data, the relation among the instances and the concepts and the instance attribute data, and realizing the alignment of the data of the instance layer;
and executing a relationship fusion operation according to the concept data, the superior-inferior relationship and the concept attribute data among different concepts, the instance data, the relationship among the instances and the concepts and the instance attribute data, realizing the alignment of the relationship among the concepts, the relationship among the concepts and the instances and the relationship among the instances, and generating the knowledge graph of the target field.
Optionally, the expanding the data in the second knowledge base into the knowledge graph to obtain an updated knowledge graph includes:
sequentially performing preprocessing, knowledge modeling, knowledge acquisition and knowledge fusion operations on the data in the second knowledge base to obtain a first extended knowledge graph of the target field;
repeatedly detecting based on the first extended knowledge graph of the target field and the knowledge graph of the target field, distinguishing repeated knowledge by state information, and fusing the first extended knowledge graph of the target field and the knowledge graph of the target field to obtain an updated knowledge graph;
the expanding the data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph includes:
sequentially performing preprocessing, knowledge modeling, knowledge acquisition and knowledge fusion operations on the data in the third knowledge base to obtain a second extended knowledge graph of the target field;
and repeatedly detecting based on the second extended knowledge graph of the target field and the knowledge graph of the target field, distinguishing repeated knowledge by using state information, and fusing the second extended knowledge graph of the target field and the knowledge graph of the target field to obtain an updated knowledge graph.
The dynamically updated knowledge graph expanding device provided by the invention can realize each process realized by the method embodiments of fig. 1 to fig. 3, and achieve the same technical effect, and is not repeated here for avoiding repetition.
Fig. 5 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 5: a processor (processor)510, a communication Interface (Communications Interface)520, a memory (memory)530 and a communication bus 540, wherein the processor 510, the communication Interface 520 and the memory 530 communicate with each other via the communication bus 540. Processor 510 may invoke logic instructions in memory 530 to perform a dynamically updated knowledge-graph extension method comprising: constructing a knowledge graph based on a first knowledge base to generate the knowledge graph of the target field, wherein the first knowledge base is composed of original data of the target field; generating a second knowledge base based on the new knowledge of the target field, and expanding data in the second knowledge base into the knowledge graph to obtain an updated knowledge graph; and/or executing a knowledge enabling operation based on the knowledge graph to obtain an application scheme of the knowledge graph, executing a knowledge reasoning operation on the application scheme to obtain a third knowledge base, and expanding data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph.
Furthermore, the logic instructions in the memory 530 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units 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, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the dynamically updated knowledge-graph extension method provided by the above methods, the method comprising: constructing a knowledge graph based on a first knowledge base to generate the knowledge graph of the target field, wherein the first knowledge base is composed of original data of the target field; generating a second knowledge base based on the new knowledge of the target field, and expanding data in the second knowledge base into the knowledge graph to obtain an updated knowledge graph; and/or executing a knowledge enabling operation based on the knowledge graph to obtain an application scheme of the knowledge graph, executing a knowledge reasoning operation on the application scheme to obtain a third knowledge base, and expanding data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program that, when executed by a processor, is implemented to perform the dynamically updated knowledge-graph extension method provided above, the method comprising: constructing a knowledge graph based on a first knowledge base to generate the knowledge graph of the target field, wherein the first knowledge base is composed of original data of the target field; generating a second knowledge base based on the new knowledge of the target field, and expanding data in the second knowledge base into the knowledge graph to obtain an updated knowledge graph; and/or executing a knowledge enabling operation based on the knowledge graph to obtain an application scheme of the knowledge graph, executing a knowledge reasoning operation on the application scheme to obtain a third knowledge base, and expanding data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph.
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 (10)
1. A dynamically updated knowledge graph expansion method, comprising:
constructing a knowledge graph based on a first knowledge base to generate the knowledge graph of the target field, wherein the first knowledge base is composed of original data of the target field;
generating a second knowledge base based on the new knowledge of the target field, and expanding data in the second knowledge base into the knowledge graph to obtain an updated knowledge graph; and/or the presence of a gas in the gas,
and executing knowledge enabling operation based on the knowledge graph to obtain an application scheme of the knowledge graph, executing knowledge reasoning operation on the application scheme to obtain a third knowledge base, and expanding data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph.
2. The method for dynamically updating the knowledge-graph expansion according to claim 1, wherein the constructing the knowledge-graph based on the first knowledge base to generate the knowledge-graph of the target domain comprises:
preprocessing the original data of the target field to obtain preprocessed target data;
executing knowledge modeling operation based on the preprocessed target data to obtain concept data, and upper and lower relations among different concepts and concept attribute data;
executing knowledge acquisition operation based on the preprocessed target data to obtain instance data, a relation between an instance and a concept and instance attribute data;
and executing knowledge fusion operation according to the concept data, the superior-inferior relation and the concept attribute data among different concepts, the example data, the relation among the examples and the concepts and the example attribute data, and generating the knowledge graph of the target field.
3. The dynamically updated knowledge-graph expanding method of claim 1, wherein the performing a intellectual inference operation on the application schema to obtain a third knowledge base comprises:
determining a feedback evaluation result of the knowledge graph user on the application scheme based on the application scheme;
and performing semantic reasoning or preset reasoning by using knowledge graph data corresponding to the application scheme based on the feedback evaluation result to obtain new knowledge, and generating a third knowledge base.
4. The method of claim 2, wherein the performing a knowledge modeling operation based on the preprocessed target data to obtain concept data, context relationships between different concepts, and concept attribute data comprises:
executing concept acquisition operation based on the preprocessed target data to obtain concept data;
executing concept context generation operation based on the preprocessed target data to obtain the upper and lower relations among different concepts;
and executing concept attribute acquisition operation based on the preprocessed target data to obtain concept attribute data.
5. The method of claim 2, wherein the performing a knowledge modeling operation based on the preprocessed target data to obtain concept data, context relationships between different concepts, and concept attribute data comprises:
executing concept acquisition operation based on the preprocessed target data to obtain concept data;
executing concept context generation operation based on the preprocessed target data to obtain the upper and lower relations among different concepts;
and executing concept attribute acquisition operation based on the preprocessed target data to obtain concept attribute data.
6. The dynamically updated knowledge-graph expansion method of claim 2, wherein the performing a knowledge fusion operation to generate the knowledge-graph of the target domain according to the concept data, the superior-inferior relationship between different concepts and the concept attribute data, and the instance data, the relationship between instances and concepts and the instance attribute data comprises:
executing concept fusion operation according to the concept data, the superior-inferior relation and the concept attribute data among different concepts, the example data, the relation among the examples and the concepts and the example attribute data, and realizing the data alignment of the concept layer;
executing instance fusion operation according to the concept data, the superior-inferior relation and the concept attribute data among different concepts, the instance data, the relation among the instances and the concepts and the instance attribute data, and realizing the alignment of the data of the instance layer;
and executing a relationship fusion operation according to the concept data, the superior-inferior relationship and the concept attribute data among different concepts, the instance data, the relationship among the instances and the concepts and the instance attribute data, realizing the alignment of the relationship among the concepts, the relationship among the concepts and the instances and the relationship among the instances, and generating the knowledge graph of the target field.
7. The dynamically updated knowledgegraph extending method of claim 1, wherein the extending data in the second knowledgebase into the knowledgegraph to obtain an updated knowledgegraph comprises:
sequentially performing preprocessing, knowledge modeling, knowledge acquisition and knowledge fusion operations on the data in the second knowledge base to obtain a first extended knowledge graph of the target field;
repeatedly detecting based on the first extended knowledge graph of the target field and the knowledge graph of the target field, distinguishing repeated knowledge by state information, and fusing the first extended knowledge graph of the target field and the knowledge graph of the target field to obtain an updated knowledge graph;
the expanding the data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph includes:
sequentially performing preprocessing, knowledge modeling, knowledge acquisition and knowledge fusion operations on the data in the third knowledge base to obtain a second extended knowledge graph of the target field;
and repeatedly detecting based on the second extended knowledge graph of the target field and the knowledge graph of the target field, distinguishing repeated knowledge by using state information, and fusing the second extended knowledge graph of the target field and the knowledge graph of the target field to obtain an updated knowledge graph.
8. A dynamically updated knowledge-graph extending apparatus, comprising:
the knowledge graph construction unit is used for constructing a knowledge graph based on a first knowledge base to generate the knowledge graph of the target field, wherein the first knowledge base is composed of original data of the target field;
the knowledge graph expanding unit is used for generating a second knowledge base based on the new knowledge of the target field, expanding data in the second knowledge base into the knowledge graph and obtaining an updated knowledge graph; and/or the presence of a gas in the gas,
and executing knowledge enabling operation based on the knowledge graph to obtain an application scheme of the knowledge graph, executing knowledge reasoning operation on the application scheme to obtain a third knowledge base, and expanding data in the third knowledge base into the knowledge graph to obtain an updated knowledge graph.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the dynamically updated knowledge-graph extension method of any one of claims 1 to 7 are implemented when the program is executed by the processor.
10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, performs the steps of the dynamically updated knowledge-graph extension method of any one of claims 1 to 7.
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