CN113609268A - Intelligent psychological knowledge question-answering method and device based on knowledge graph - Google Patents
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Abstract
The application discloses an intelligent psychological knowledge question-answering method and device based on a knowledge graph. The method comprises the steps of carrying out feature extraction on problem information based on an entity relation module to obtain a triple set of the problem information and a main entity, wherein the triple set comprises at least one triple; performing problem classification on the triple set based on a preset problem classification module to obtain a problem category of the problem information; generating semantic network structure information based on the set of triples, the primary entity, and the problem category; and matching and inquiring in a preset knowledge graph based on the semantic network structure information to obtain an answer. The method and the system solve the problem that the accuracy of the questions answered by the question-answering system of the relevant psychological robot is poor.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an intelligent psychological knowledge question-answering method and device based on a knowledge graph.
Background
In the interaction process between the mental robot and the user, the user asks some basic common knowledge questions about the psychological aspect, the requirement can be realized by means of intention judgment and matching, but the intention judgment is not accurate enough, and the intention judgment needs a large data volume, so that the mutual effect is not good for a simple knowledge question-answering system, and the accuracy of answering the questions is poor.
Disclosure of Invention
The main purpose of the present application is to provide an intelligent psychological knowledge question-answering method based on a knowledge graph, so as to solve the problem in the related art that the question-answering system of a psychological robot has poor accuracy in answering questions.
In order to achieve the above objects, according to one aspect of the present application, there is provided a method for intellectual mental intellectual questioning and answering based on a knowledge graph.
The intelligent psychological knowledge question-answering method based on the knowledge graph comprises the following steps:
performing feature extraction on problem information based on an entity relationship module to obtain a triple set of the problem information and a main entity, wherein the triple set comprises at least one triple;
performing problem classification on the triple set based on a preset problem classification module to obtain a problem category of the problem information;
generating semantic network structure information based on the set of triples, the primary entity, and the problem category;
and matching and inquiring in a preset knowledge graph based on the semantic network structure information to obtain an answer.
Optionally, the performing, by the entity relationship module, feature extraction on the problem information to obtain a triple set of the problem information and a main entity includes:
carrying out entity identification and entity attribute classification based on a deep learning model based on the problem information to obtain a triple set;
and determining a main entity based on the entity relationship included in the triple set.
Optionally, the performing, by the preset-problem-classification-based module, problem classification on the triple set to obtain a problem category of the problem information includes:
and performing problem classification on the triple set based on a Transformer as a classification model of a feature extractor, wherein the problem type of the problem information is described.
Optionally, the performing matching query in a preset knowledge graph based on the semantic network structure information to obtain an answer includes:
searching a map database based on a preset knowledge map through a main entity, and finding out a node corresponding to the main entity as a core node;
carrying out knowledge inference on the problem category and the triple set by adopting an inference algorithm based on a knowledge graph path to determine the semantic intention of the problem information;
and querying answers in the nodes of the graph database by combining the core nodes and the semantic intents.
In a second aspect, the present application further provides a device for intellectual mental knowledge questioning and answering based on knowledge-graph, comprising:
the extracting module is used for extracting the characteristics of the problem information based on the entity relation module to obtain a triple set of the problem information and a main entity, wherein the triple set comprises at least one triple;
the classification module is used for performing problem classification on the triple set based on a preset problem classification module to obtain the problem category of the problem information;
a generating module for generating semantic network structure information based on the triple set, the main entity and the question category;
and the query module is used for performing matching query in a preset knowledge graph based on the semantic network structure information to obtain an answer.
Optionally, the extracting module is configured to:
carrying out entity identification and entity attribute classification based on a deep learning model based on the problem information to obtain a triple set;
and determining a main entity based on the entity relationship included in the triple set.
Optionally, the classification module is configured to:
and performing problem classification on the triple set based on a Transformer as a classification model of a feature extractor, wherein the problem type of the problem information is described.
Optionally, the query module is configured to:
searching a map database based on a preset knowledge map through a main entity, and finding out a node corresponding to the main entity as a core node;
carrying out knowledge inference on the problem category and the triple set by adopting an inference algorithm based on a knowledge graph path to determine the semantic intention of the problem information;
and querying answers in the nodes of the graph database by combining the core nodes and the semantic intents.
In a third aspect, the present application further provides a computer device, including: a processor, a memory, and a network interface;
the processor is connected with the memory and the network interface, wherein the network interface is used for providing a network communication function, the memory is used for storing program codes, and the processor is used for calling the program codes to execute the method.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program comprising program instructions which, when executed by a processor, perform the above-described method.
In the embodiment of the application, a triple set and a main entity of the problem information are obtained by performing feature extraction on the problem information based on an entity relationship module, wherein the triple set comprises at least one triple; performing problem classification on the triple set based on a preset problem classification module to obtain a problem category of the problem information; generating semantic network structure information based on the set of triples, the primary entity, and the problem category; and matching and inquiring in a preset knowledge graph based on the semantic network structure information to obtain an answer. Therefore, the triple set and the main entity are identified through the entity relation module, the problem category of the problem information is determined through the preset problem classification module, namely the semantic intention is determined, and then the main entity, the triple set and the semantic intention are combined to perform accurate matching query in the preset knowledge graph to obtain an answer. Therefore, the technical problem that the accuracy of the questions answered by the question-answering system of the psychological robot in the related art is poor is solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a flow diagram of a method for intellectual mental intellectual questioning and answering based on a knowledge graph according to one embodiment of the present application;
fig. 2 is a schematic structural diagram of an intelligent intellectual knowledge questioning and answering device based on a knowledge map according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
According to an embodiment of the present application, there is provided a method for intellectual psychological knowledge questioning and answering based on a knowledge graph, as shown in fig. 1, the method includes the following steps S100 to S400:
100, performing feature extraction on problem information based on an entity relationship module to obtain a triple set of the problem information and a main entity, wherein the triple set comprises at least one triple.
The entity relation module preprocesses the problem information, performs word segmentation, part of speech tagging and keyword extraction on the problem information, and then identifies the triple set and the main entity.
Optionally, in step 100, performing feature extraction on the problem information based on the entity relationship module to obtain a triple set of the problem information and a main entity, including:
carrying out entity identification and entity attribute classification based on a deep learning model based on the problem information to obtain a triple set;
and determining a main entity based on the entity relationship included in the triple set.
Specifically, entity identification and entity attribute classification based on a deep learning model are carried out based on the problem information to obtain a triple set; specifically, the text of the question information is processed by natural language, and the question information is processed into the form of triple such as entity-relation-entity, entity-attribute-data and the like in a knowledge graph.
And 200, performing problem classification on the triple set based on a preset problem classification module to obtain the problem category of the problem information.
Specifically, the problem classification of the triple set based on the preset problem classification module to obtain the problem category of the problem information includes:
and performing problem classification on the triple set based on a Transformer as a classification model of a feature extractor, wherein the problem type of the problem information is described.
Among them, based on the Transformer as a feature extractor, the Transformer has the following advantages compared with CNN and RNN: 1. the parallel computing capability is much stronger than that of RNN; 2. the long-distance feature capture capability of the CNN is much stronger than that of the CNN; in addition, the Transformer is essentially defect free.
Generating semantic network structure information based on the set of triples, the primary entity, and the issue category 300.
And 400, performing matching query in a preset knowledge graph based on the semantic network structure information to obtain an answer.
Specifically, the step 400 includes:
searching a map database based on a preset knowledge map through a main entity, and finding out a node corresponding to the main entity as a core node;
carrying out knowledge inference on the problem category and the triple set by adopting an inference algorithm based on a knowledge graph path to determine the semantic intention of the problem information;
and querying answers in the nodes of the graph database by combining the core nodes and the semantic intents.
The method comprises the steps of determining a core node through a main entity, determining a semantic intention according to a question category and a triple set, and inquiring an answer in a node of a graph database by combining the core node and the semantic intention.
In one embodiment of the present application, the method of establishing the preset knowledge-graph before the method comprises:
collecting technical data with mental knowledge;
carrying out entity identification and relation extraction based on the technical data to obtain a map semantic triple;
and establishing the preset knowledge graph based on the graph semantic triple.
In the embodiment of the application, a triple set and a main entity of the problem information are obtained by performing feature extraction on the problem information based on an entity relationship module, wherein the triple set comprises at least one triple; performing problem classification on the triple set based on a preset problem classification module to obtain a problem category of the problem information; generating semantic network structure information based on the set of triples, the primary entity, and the problem category; and matching and inquiring in a preset knowledge graph based on the semantic network structure information to obtain an answer. Therefore, the triple set and the main entity are identified through the entity relation module, the problem category of the problem information is determined through the preset problem classification module, namely the semantic intention is determined, and then the main entity, the triple set and the semantic intention are combined to perform accurate matching query in the preset knowledge graph to obtain an answer. Therefore, the technical problem that the accuracy of the questions answered by the question-answering system of the psychological robot in the related art is poor is solved.
Based on the same technical concept, as shown in fig. 2, the present application further provides an apparatus for intellectual mental intellectual questioning and answering based on a knowledge graph, comprising:
the extracting module 10 is configured to perform feature extraction on the problem information based on the entity relationship module to obtain a triple set of the problem information and a main entity, where the triple set includes at least one triple;
the classification module 20 is configured to perform problem classification on the triple set based on a preset problem classification module 20 to obtain a problem category of the problem information;
a generating module 30, configured to generate semantic network structure information based on the triple set, the main entity, and the question category;
and the query module 40 is configured to perform matching query in a preset knowledge graph based on the semantic network structure information to obtain an answer.
Optionally, the extracting module 10 is configured to:
carrying out entity identification and entity attribute classification based on a deep learning model based on the problem information to obtain a triple set;
and determining a main entity based on the entity relationship included in the triple set.
Optionally, the classification module 20 is configured to:
and performing problem classification on the triple set based on a Transformer as a classification model of a feature extractor, wherein the problem type of the problem information is described.
Optionally, the query module 40 is configured to:
searching a map database based on a preset knowledge map through a main entity, and finding out a node corresponding to the main entity as a core node;
carrying out knowledge inference on the problem category and the triple set by adopting an inference algorithm based on a knowledge graph path to determine the semantic intention of the problem information;
and querying answers in the nodes of the graph database by combining the core nodes and the semantic intents.
Based on the same technical concept, the present application also provides a computer device, comprising: a processor, a memory, and a network interface;
the processor is connected with the memory and the network interface, wherein the network interface is used for providing a network communication function, the memory is used for storing program codes, and the processor is used for calling the program codes to execute the method.
Based on the same technical concept, the present application also provides a computer-readable storage medium storing a computer program comprising program instructions that, when executed by a processor, perform the above-mentioned method.
It will be apparent to those skilled in the art that the modules or steps of the present application described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and they may alternatively be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. Thus, the present application is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. An intelligent psychological knowledge question-answering method based on a knowledge graph is characterized by comprising the following steps:
performing feature extraction on problem information based on an entity relationship module to obtain a triple set of the problem information and a main entity, wherein the triple set comprises at least one triple;
performing problem classification on the triple set based on a preset problem classification module to obtain a problem category of the problem information;
generating semantic network structure information based on the set of triples, the primary entity, and the problem category;
and matching and inquiring in a preset knowledge graph based on the semantic network structure information to obtain an answer.
2. The intellectual mental knowledge questioning and answering method based on knowledge graph according to claim 1, wherein the entity relationship module performs feature extraction on the question information to obtain the triple set of the question information and the main entity, including:
carrying out entity identification and entity attribute classification based on a deep learning model based on the problem information to obtain a triple set;
and determining a main entity based on the entity relationship included in the triple set.
3. The intellectual mental knowledge questioning and answering method based on knowledge graph according to claim 1, wherein the problem classification of the problem information obtained by the problem classification of the triple set based on the preset problem classification module comprises:
and performing problem classification on the triple set based on a Transformer as a classification model of a feature extractor, wherein the problem type of the problem information is described.
4. The intellectual mental knowledge questioning and answering method based on knowledge graph according to claim 1, wherein the matching query in the preset knowledge graph based on the semantic network structure information to obtain the answer comprises:
searching a map database based on a preset knowledge map through a main entity, and finding out a node corresponding to the main entity as a core node;
carrying out knowledge inference on the problem category and the triple set by adopting an inference algorithm based on a knowledge graph path to determine the semantic intention of the problem information;
and querying answers in the nodes of the graph database by combining the core nodes and the semantic intents.
5. An intelligent psychological knowledge question-answering device based on a knowledge graph is characterized by comprising:
the extracting module is used for extracting the characteristics of the problem information based on the entity relation module to obtain a triple set of the problem information and a main entity, wherein the triple set comprises at least one triple;
the classification module is used for performing problem classification on the triple set based on a preset problem classification module to obtain the problem category of the problem information;
a generating module for generating semantic network structure information based on the triple set, the main entity and the question category;
and the query module is used for performing matching query in a preset knowledge graph based on the semantic network structure information to obtain an answer.
6. The intellectual knowledge questioning and answering device based on the knowledge graph according to claim 5, wherein said extraction module is configured to:
carrying out entity identification and entity attribute classification based on a deep learning model based on the problem information to obtain a triple set;
and determining a main entity based on the entity relationship included in the triple set.
7. The intellectual mental knowledge quiz device based on knowledge graph according to claim 5 wherein, the classification module is used for:
and performing problem classification on the triple set based on a Transformer as a classification model of a feature extractor, wherein the problem type of the problem information is described.
8. The intellectual knowledge domain based mental quiz apparatus of claim 5 wherein the query module is configured to:
searching a map database based on a preset knowledge map through a main entity, and finding out a node corresponding to the main entity as a core node;
carrying out knowledge inference on the problem category and the triple set by adopting an inference algorithm based on a knowledge graph path to determine the semantic intention of the problem information;
and querying answers in the nodes of the graph database by combining the core nodes and the semantic intents.
9. A computer device, comprising: a processor, a memory, and a network interface;
the processor is connected to the memory and the network interface, wherein the network interface is configured to provide a network communication function, the memory is configured to store program code, and the processor is configured to call the program code to perform the method of any one of claims 1 to 4.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program comprising program instructions which, when executed by a processor, perform the method of any of claims 1-4.
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