CN113868396A - Task intelligent dialogue construction method and system based on knowledge graph - Google Patents
Task intelligent dialogue construction method and system based on knowledge graph Download PDFInfo
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- CN113868396A CN113868396A CN202111194340.9A CN202111194340A CN113868396A CN 113868396 A CN113868396 A CN 113868396A CN 202111194340 A CN202111194340 A CN 202111194340A CN 113868396 A CN113868396 A CN 113868396A
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Abstract
The invention discloses a task-based intelligent dialogue construction method and system based on a knowledge graph, which comprises the steps of firstly constructing the knowledge graph, and setting an intention entity and a word slot entity in the knowledge graph; and then setting the intention entities to point to the word slot entities or point to other intention entities, wherein no mutual relation exists between the word slot entities. By linking the intentions and word slots in the task question-answering into the construction process of the knowledge graph, the structural advantages of the knowledge graph are fully utilized to obtain the relation between different intentions and word slots, the configuration of the intentions and word slots is reduced, and the correlation between different tasks is facilitated; and in the flow direction of tasks among entities in the map, the intention entities are set to point to the word slot entities or other intention entities in a one-way mode, and the word slot entities do not have mutual relations, so that the structure of the map entities is simplified, the intention entities can be enabled to efficiently find the word slot entities or other intention entities related to the intention entities, and jumping among intentions and reverse inquiry of the intentions are realized.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a task-class intelligent dialogue construction method and system based on a knowledge graph.
Background
With the continuous development of internet technology, technologies such as natural language processing, knowledge mapping, machine learning and the like are continuously developed and widely applied. A good man-machine conversation system not only can greatly reduce the labor cost of customer service, but also has entertainment and practicability, brings great convenience to the life of people, becomes an unavailable tool for people to obtain mass information, and brings more and more research and application.
The task class-based dialog is an important component given by an intelligent dialog system, is mainly applied to the dialog of a specific task, and generally has contextual interaction, such as weather query, train ticket subscription, meal ordering and the like. There are two important concepts in task-like dialog-intent, word slots. The intent represents the category to which the conversation belongs, such as how much weather today belongs, and the intent of the weather query. The term slot can be regarded as parameters which need to be carried by inquiring current intention, for example, inquiring weather needs to know inquiring time and place, and then the time and the place can be used as two term slots for weather inquiry.
At present, a plurality of task-based dialogue modes are available, a task body is often required to be well defined in a traditional task-based dialogue system, word slots are independent from each other and the number of the word slots is fixed among different intentions, so that the dialogue system is difficult to bear complex dialogue tasks, and meanwhile, the setting of the word slots depends on specific intentions, so that the dialogue system is difficult to migrate in the field. At present, advanced task-based dialog is combined with a knowledge graph generally, the knowledge graph of field content is automatically constructed in a deep learning mode, the knowledge graph is introduced in the training and recognition processes, information in the conversation process is increased, and recognition and reply accuracy is improved. However, the biggest problem of the task-like dialogue system trained through deep learning or machine learning is that the result is uncontrollable and cannot be applied in many industries or fields. Therefore, a method for constructing a task-like intelligent dialog system is needed.
Disclosure of Invention
The invention aims to overcome the technical defects and provides a task-like intelligent dialogue construction method and system based on a knowledge graph, so as to solve the problems of complex scene, smaller training sample and overfitting of small samples in the prior art.
In order to achieve the technical purpose, a first aspect of the technical solution of the present invention provides a method for constructing a task-based intelligent dialogue based on a knowledge graph, which includes the following steps:
constructing a knowledge graph, and setting an intention entity and a word slot entity in the knowledge graph;
setting the intention entities to point to word slot entities or point to other intention entities, wherein no mutual relation exists between the word slot entities.
The invention provides a task-like intelligent dialogue building system based on a knowledge graph, which comprises the following functional modules:
the map construction module is used for constructing a knowledge map, and setting an intention entity and a word slot entity in the knowledge map;
and the entity association setting module is used for setting the intention entities to point to the word slot entities or point to other intention entities, and the word slot entities have no mutual relation.
A third aspect of the present invention provides a server, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for constructing intelligent dialogs based on knowledge-graph task class when executing the computer program.
A fourth aspect of the present invention provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the above-mentioned method for constructing a task-based intelligent dialog based on a knowledge-graph.
Compared with the prior art, the method has the advantages that the intentions and word slots in the task question-answering are linked into the construction process of the knowledge graph, the structural advantages of the knowledge graph are fully utilized, the relation between different intentions and word slots is obtained, the configuration of the intentions and word slots is reduced, and the correlation between different tasks is facilitated; and in the flow direction of tasks among entities in the map, the intention entities are set to point to the word slot entities or other intention entities in a one-way mode, and the word slot entities do not have mutual relations, so that the structure of the map entities is simplified, the intention entities can be enabled to efficiently find the word slot entities or other intention entities related to the intention entities, and jumping among intentions and reverse inquiry of the intentions are realized.
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FIG. 1 is a flow chart diagram of a method for constructing a task-based intelligent dialog based on a knowledge-graph according to an embodiment of the present invention;
FIG. 2 is a diagram of the relationship between the intent entities and the word slot entities according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating the steps of session and reply generation according to an embodiment of the present invention;
FIG. 4 is a block diagram of a task-based intelligent dialog building system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Based on the above, an embodiment of the present invention provides a method for constructing a task-based intelligent dialog based on a knowledge graph, as shown in fig. 1, which includes the following steps:
and S1, constructing a knowledge graph, and setting an intention entity and a word slot entity in the knowledge graph.
The method comprises the steps of setting an intention entity and a word slot entity in a knowledge graph, wherein the intention entity and the word slot entity are defined firstly, and the definition process of the intention and word slot entity can be manually set through a front-end interface or can be defined in a configuration file mode.
Then, setting a word slot entity structure and an intention entity structure, specifically, the word slot entity structure and the intention entity structure are as follows:
the word slot entity structure comprises:
the query times are used for limiting the times of querying the missing word slots in the multi-turn conversation process;
the inquiry call technique is used for limiting words which remind the user in a way of asking when the current word slot is lacked; specifically, the method is to supplement a word slot inquiry dialogue in the following conversation process, wherein the word slot inquiry dialogue can be a series of question lists or a single question;
the failed speech technology is used for limiting the returned speech when the current word slot fails to acquire the answer;
whether it is necessary to fill in, is used to define whether the current word slot must be carried when inquiring about the intent.
The intended entity structure is as follows:
a response mode for limiting the words replied under the condition that the current intention is met; either a fixed utterance or a third party link may be invoked to dynamically generate a reply;
an intent tag for defining a current entity as an intent entity;
a failed utterance for defining an utterance replied when the current intent to obtain an answer fails; for example, in the process of calling the third-party link, if the third-party link fails, failure reply is carried out;
the inquiry times are used for limiting the times of back inquiry when the map structure to which the current intention belongs is determined; for example, if there are multiple parent-level intentions for the first query, the graph structure to which the current intent belongs is determined by a question-back method, and the number of question-back times is limited in the query process.
Different structural attributes are set in the intention entity and the word slot entity, so that the generation of later-stage conversation and reply can be conveniently guided, unreasonable reply caused by machine learning or deep learning is avoided, the output of a machine is strictly controlled, and the accuracy of conversation is ensured.
S2, setting the intention entities to point to the word slot entities or point to other intention entities, wherein no mutual relation exists between the word slot entities.
By setting the intention entity to point to the word slot entity or to point to other intention entities, the word slot related to the intention entity can be positioned through the intention entity in the question-answering conversation process; location of the intent entity to other associated intent entities can also be implemented, enabling jumping of intents in multiple rounds of dialog.
Specifically, the association relationship between the intention entities and the word slot entity can refer to fig. 2, for example, intention entity a is connected to word slot entity a, word slot entity B, and intention entity B.
And the intention entity allows no word slot entity to exist and independently exists, for example, the intention of inquiring the accumulation fund can be defined, and no parameter is needed in the inquiry process, so the intention of inquiring the accumulation fund can be set as a single accumulation fund inquiry entity in the knowledge graph.
In addition, the same intent entity may correspond to multiple parent intent entities, such as intent entity C in fig. 2, with two parent intents, intent entity B and intent entity D. When a plurality of parent level intentions exist, determining the atlas structure to which the current intentions belong in a mode of question return; for example, during a session, if intent C is first located, the current intent entity relationship would be asked in reverse to belong to either B or D. For example, if intention C is a Kendeji order, intention D is a McDonald order, and intention B is a definite hamburger, then the following form of task class conversation is supported during the query process:
the user: i want to define a hamburger
The robot comprises: whether you want kendir or mcdonald's labor
The user: diken bar
The robot comprises: hamburger which has reserved kendirk for you
The conversation and reply generation process of the intelligent dialogue system obtained by the knowledge graph-based task intelligent dialogue construction method is as follows:
when a user inputs a text, firstly, judging whether the input of the user is in a first inquiry or unintentional state through dialogue state tracking, if so, performing intention identification, judging whether the input of the user has an intention, and if not, directly generating a reply. If the current intention entity exists, the intention is linked into the intention entity in the knowledge graph, whether the current intention entity has multi-level intention is judged through the link relation of the intention entity in the knowledge graph, if yes, the reverse parent intention inquiry is confirmed, if not, the current intention is located in a parent node, then a word slot in a user input text is identified, meanwhile, the word slot related to the current intention entity is inquired, the word slot identified in the user input text is associated with the word slot inquired by the current intention entity, whether the current intention lacks the word slot is judged, if the word slot information lacks, the inquiry is carried out according to the inquiry technology in the word slot attribute, and the conversation is recorded. And if the word slot is not missing, judging whether the current intention is associated with other intention entities, if not, directly generating an intention answer in a response mode in the intention entities, otherwise, inquiring and confirming the associated intention, recording the session process and transferring the session process to the next round of session.
When the user inputs a non-initial query, firstly, judging whether the last conversation process is a question sentence with a missing word slot according to the conversation state, if so, identifying the word slot for the input of the user, if so, associating the word slot with a word slot entity detected by an intention entity, storing a conversation record, and transferring the question-answering process to the process 2 of the figure 3. If no word slot exists, judging whether the current word slot inquiry reaches the upper limit according to the inquiry time attribute in the word slot entity, if so, replying according to the failed word operation in the word slot entity attribute, otherwise, inquiring according to the inquiry word operation in the entity attribute. If the last session is not the intention query in the last session process, it needs to be further determined whether the last session is the intention query, and if not, it is determined that the session link is not entered, and the session is transferred to the process 3 of fig. 3, and the flow of intention recognition is performed again. Otherwise, judging whether the current input of the user is the intention of the intention entity link, if so, transferring the conversation state to the process 1 of the figure 3, judging whether the intention of the link has the lack of a word slot, otherwise, judging whether the current intention inquiry reaches the upper limit, if so, replying the failed operation in the attribute of the intention entity, otherwise, inquiring again according to the intention inquiry operation in the attribute of the intention entity, and recording the conversation.
During the generation process of conversation and reply, the intention and word slot entities identified based on template matching, deep learning or machine learning are linked into the knowledge graph, and the word slot entities related to the intention entities in the knowledge graph and the identified word slots are combined to respond to the input of the user, so that the whole process of the task-class dialog system is realized; and nodes of the knowledge graph are recorded in the session process based on the knowledge graph, and the conversation state is utilized for tracking, so that multiple rounds of conversation in the same task are realized.
According to the invention, the intentions and word slots in the task question-answering are linked into the construction process of the knowledge graph, the structural advantages of the knowledge graph are fully utilized, the relation between different intentions and word slots is obtained, the configuration of the intentions and word slots is reduced, and the correlation between different tasks is facilitated; and in the flow direction of tasks among entities in the map, the intention entities are set to point to the word slot entities or other intention entities in a one-way mode, and the word slot entities do not have mutual relations, so that the structure of the map entities is simplified, the intention entities can be enabled to efficiently find the word slot entities or other intention entities related to the intention entities, and jumping among intentions and reverse inquiry of the intentions are realized.
Based on the above method for constructing task-based intelligent dialogs based on the knowledge graph, the invention also provides a system for constructing task-based intelligent dialogs based on the knowledge graph, as shown in fig. 4, which comprises the following functional modules:
the map building module 10 is used for building a knowledge map, and setting an intention entity and a word slot entity in the knowledge map;
and an entity association setting module 20, configured to set that the intent entity points to a word slot entity or points to another intent entity, where no mutual relationship exists between the word slot entities.
The execution mode of the system for establishing task-based intelligent dialogues based on the knowledge graph is basically the same as that of the method for establishing task-based intelligent dialogues based on the knowledge graph, and therefore, detailed description is omitted.
The server in this embodiment is a device for providing computing services, and generally refers to a computer with high computing power, which is provided to a plurality of consumers via a network. The server of this embodiment includes: a memory including an executable program stored thereon, a processor, and a system bus, it will be understood by those skilled in the art that the terminal device structure of the present embodiment does not constitute a limitation of the terminal device, and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
The memory may be used to store software programs and modules, and the processor may execute various functional applications of the terminal and data processing by operating the software programs and modules stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the terminal, etc. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The executable program of the task-based intelligent dialogue building method based on the knowledge graph is contained in a memory, the executable program can be divided into one or more modules/units, the one or more modules/units are stored in the memory and executed by a processor to complete the information acquisition and implementation process, and the one or more modules/units can be a series of computer program instruction segments capable of completing specific functions and are used for describing the execution process of the computer program in the server. For example, the computer program may be divided into a graph building module 10, an entity association setting module 20.
The processor is a control center of the server, connects various parts of the whole terminal equipment by various interfaces and lines, and executes various functions of the terminal and processes data by running or executing software programs and/or modules stored in the memory and calling data stored in the memory, thereby performing overall monitoring of the terminal. Alternatively, the processor may include one or more processing units; preferably, the processor may integrate an application processor, which mainly handles operating systems, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor.
The system bus is used to connect various functional units in the computer, and can transmit data information, address information and control information, and the types of the functional units can be PCI bus, I SA bus, VESA bus and the like. The system bus is responsible for data and instruction interaction between the processor and the memory. Of course, the system bus may also access other devices such as network interfaces, display devices, etc.
The server at least includes a CPU, a chipset, a memory, a disk system, and the like, and other components are not described herein again.
In the embodiment of the present invention, the executable program executed by the processor included in the terminal specifically includes: a task-class intelligent dialogue construction method based on a knowledge graph comprises the following steps:
constructing a knowledge graph, and setting an intention entity and a word slot entity in the knowledge graph;
setting the intention entities to point to word slot entities or point to other intention entities, wherein no mutual relation exists between the word slot entities.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art would appreciate that the modules, elements, and/or method steps of the various embodiments described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; 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 task-class intelligent dialogue construction method based on a knowledge graph is characterized by comprising the following steps:
constructing a knowledge graph, and setting an intention entity and a word slot entity in the knowledge graph;
setting the intention entities to point to word slot entities or point to other intention entities, wherein no mutual relation exists between the word slot entities.
2. The method for constructing task-based intelligent dialog based on knowledge graph as claimed in claim 1, wherein the setting of the intention entity and the word slot entity in the knowledge graph comprises:
defining an intention entity and a word slot entity;
setting a word slot entity structure and an intention entity structure.
3. The knowledge-graph-based task-like intelligent dialogue building method according to claim 2, wherein the word slot entity structure comprises:
the query times are used for limiting the times of querying the missing word slots in the multi-turn conversation process;
the inquiry call technique is used for limiting words which remind the user in a way of asking when the current word slot is lacked;
the failed speech technology is used for limiting the returned speech when the current word slot fails to acquire the answer;
whether it is necessary to fill in, is used to define whether the current word slot must be carried when inquiring about the intent.
4. The knowledge-graph-based task-like intelligent dialog construction method of claim 2, wherein the intent entity structure is as follows:
a response mode for limiting the words replied under the condition that the current intention is met;
an intent tag for defining a current entity as an intent entity;
a failed utterance for defining an utterance replied when the current intent to obtain an answer fails;
the number of queries is used to define the number of question backs in determining the graph structure to which the current intent belongs.
5. The method of claim 1, wherein the same intent entity may correspond to multiple parent intent entities.
6. The method for constructing a task-based intelligent dialog based on a knowledge graph as claimed in claim 1, wherein when there are a plurality of parent level intents, the graph structure to which the current intents belong is determined by means of question back.
7. The intellectual dialogue construction method based on knowledge-graph task class according to claim 3 or 4, characterized in that during the conversation and reply generation process, the nodes of the knowledge-graph during each conversation are recorded.
8. A task-class intelligent dialogue building system based on a knowledge graph is characterized by comprising the following functional modules:
the map construction module is used for constructing a knowledge map, and setting an intention entity and a word slot entity in the knowledge map;
and the entity association setting module is used for setting the intention entities to point to the word slot entities or point to other intention entities, and the word slot entities have no mutual relation.
9. A server comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the intellectual graph based task class intelligent dialog construction method according to any of the claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for intellectual property graph based task class intelligent dialog construction according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116701662A (en) * | 2023-08-07 | 2023-09-05 | 国网浙江浙电招标咨询有限公司 | Knowledge graph-based supply chain data management method, device, equipment and medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116701662A (en) * | 2023-08-07 | 2023-09-05 | 国网浙江浙电招标咨询有限公司 | Knowledge graph-based supply chain data management method, device, equipment and medium |
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