CN113064969A - Query method, system, medium and device for question-answering system - Google Patents
Query method, system, medium and device for question-answering system Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, in particular to a reasoning query method, a reasoning query system, a reasoning query medium and a reasoning query device for a question-answering system, compared with the prior art, the reasoning query method for the question-answering system provided by the invention has the advantages that a reasoning query process is triggered through the question-answering system, a plurality of core entities are screened, and an alternative entity set is determined; extracting subgraphs from the knowledge graph by combining scene rules and constructing a probability graph model according to the alternative entity set; performing inference inquiry according to the probabilistic graphical model to acquire characteristics; performing marginal probability calculation on the core entity by using the characteristics; and feeding back the marginal probability as a result to the question-answering system. The rigor and reliability of the query result is improved through the probability of the multi-core entity.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a reasoning and inquiring method, a reasoning and inquiring system, a reasoning and inquiring medium and a reasoning and inquiring device for a question-answering system.
Background
Knowledge map (Knowledge Graph) is a series of different graphs displaying Knowledge development process and structure relationship in the book intelligence field, describing Knowledge resources and carriers thereof by using visualization technology, mining, analyzing, constructing, drawing and displaying Knowledge and mutual relation between Knowledge resources and Knowledge carriers. The modern theory of the multidisciplinary fusion purpose is achieved by combining the theory and method of applying mathematics, graphics, information visualization technology, information science and other disciplines with the method of metrology introduction analysis, co-occurrence analysis and the like and utilizing a visual map to vividly display the core structure, development history, frontier field and overall knowledge framework of the disciplines. The method displays the complex knowledge field through data mining, information processing, knowledge measurement and graph drawing, reveals the dynamic development rule of the knowledge field, and provides a practical and valuable reference for subject research.
The invention patent with the application number of CN201910592600.4 and the publication date of 2019, 11, 15 discloses a question-answering method, a question-answering device, computer equipment and a storage medium based on a knowledge graph, wherein the knowledge graph contains a large amount of information, the query range can be expanded, the accuracy of answers is improved, and a body comprises concepts and mutual relations in a specific field, so that a constructed target query graph is more accurate, and the accuracy of the answers can be further improved.
It can be seen that most of the current knowledge graph queries can only realize simple entity-relationship-entity RDF triple queries, and the query result rigidness cannot be guaranteed.
Disclosure of Invention
In order to solve the problem that the query result in the prior art is not strict enough, the invention provides a reasoning query method for a question-answering system, which comprises the following steps:
s10: the question-answering system triggers a reasoning inquiry process, screens a plurality of core entities and determines an alternative entity set;
s20: extracting subgraphs from the knowledge graph by combining scene rules and constructing a probability graph model according to the alternative entity set;
s30: performing inference inquiry according to the probabilistic graphical model to acquire characteristics;
s40: performing marginal probability calculation on the core entity by using the characteristics;
s50: and feeding back the marginal probability as a result to the question-answering system.
Further, the knowledge graph further comprises statistical weights, and the statistical weights are used for realizing the construction of the probability graph model.
Further, the statistical weight refers to a conditional probability in a probability map model, the relationship weight is obtained through statistics, and a statistical method follows a law of large numbers and a maximum likelihood estimation method.
Further, step S20 includes the steps of:
s21: extracting the subgraph by taking a plurality of core entities in the alternative entity set as cores;
s22: inquiring according to the subgraph to acquire key information, wherein the key information comprises entities, relations and weights;
s23: and constructing the probability graph model according to the key information.
Further, step S23 further includes: and generating a probability table according to the key information.
Further, in step S40, the inference information is collected by querying according to the probabilistic graphical model and the probability table, and the edge probability of the core entity is calculated by using the inference information as a conditional probability.
Further, the inference calculation method for calculating the edge probability of the core entity according to the probability map model and the probability table adopts one of a bayesian network, a neural network or a decision tree.
The invention also provides a reasoning inquiry system for the question-answering system, which comprises a triggering module, an extraction module, a question module, a calculation module and a feedback module;
the triggering module receives a reasoning inquiry process triggered by a question-answering system, screens a plurality of core entities and determines an alternative entity set;
the extraction module extracts subgraphs from the knowledge graph and constructs a probability graph model according to the alternative entity set by combining with scene rules;
the query module carries out reasoning query according to the probabilistic graph model so as to acquire characteristics;
the computing module uses the features to perform marginal probability computation on the core entity;
the feedback module feeds the marginal probability as a result back to the question-answering system.
The present invention also provides a computer-readable storage medium characterized in that: the computer readable storage medium stores computer instructions, and when executed by a processor, the computer implements the inference query method for question-answering system as described in any one of the above.
The present invention also provides a computer device characterized in that: comprising at least one processor, and a memory communicatively coupled to the processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to cause the processor to perform the inferential query method for a question-and-answer system as described in any one of the above.
Compared with the prior art, the inference query method for the question-answering system provided by the invention has the advantages that the question-answering system triggers an inference query process, a plurality of core entities are screened, and an alternative entity set is determined; extracting subgraphs from the knowledge graph by combining scene rules and constructing a probability graph model according to the alternative entity set; performing inference inquiry according to the probabilistic graphical model to acquire characteristics; performing marginal probability calculation on the core entity by using the characteristics; and feeding back the marginal probability as a result to the question-answering system. The rigor and reliability of the query result is improved through the probability of the multi-core entity.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of an inference query method for a question-answering system according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a reasoning and inquiring method for a question-answering system, which comprises the following steps as shown in figure 1:
s10: the question-answering system triggers a reasoning inquiry process, screens a plurality of core entities and determines an alternative entity set;
s20: extracting subgraphs from the knowledge graph by combining scene rules and constructing a probability graph model according to the alternative entity set;
s30: performing inference inquiry according to the probabilistic graphical model to acquire characteristics;
s40: performing marginal probability calculation on the core entity by using the characteristics;
s50: and feeding back the marginal probability as a result to the question-answering system.
In specific implementation, the inference query process is triggered by the question-answering system, a plurality of core entities are screened, and an alternative entity set is determined; extracting subgraphs from the knowledge graph by combining scene rules and constructing a probability graph model according to the alternative entity set; performing inference inquiry according to the probabilistic graphical model to acquire characteristics; performing marginal probability calculation on the core entity by using the characteristics; and feeding back the marginal probability as a result to the question-answering system.
Specifically, the knowledge graph further comprises statistical weights, the statistical weights are used for realizing the construction of a probability graph model, statistical methods such as hypothesis testing, sampling distribution and the like, a statistical target comprises continuous parameters and Boolean values, and the purpose of statistics is to realize the introduction of prior probability in an inference process. The statistical weight refers to the conditional probability in the probability map model, the relation weight is obtained through statistics, and the statistical method follows a law of large numbers and a maximum likelihood estimation method.
Specifically, step S20 includes the steps of:
s21: extracting the subgraph by taking a plurality of core entities in the alternative entity set as cores;
s22: inquiring according to the subgraph to acquire key information, wherein the key information comprises entities, relations and weights;
s23: and constructing the probability graph model according to the key information.
Next, step S23 further includes: and generating a probability table according to the key information. The probability map is a directed acyclic map, and the probability table is a basis of the probability map and is obtained through statistics. The probability relation between the parent node and the child node can be calculated by the two nodes, and the marginal probability is deduced. The probability map and the probability table are common technologies, and are not described herein.
Then, in step S40, the inference information is collected by inquiry according to the probabilistic graphical model and the probability table, and the marginal probability of the core entity is calculated by using the inference information as a conditional probability. And the inference calculation method for calculating the marginal probability of the core entity according to the probability map model and the probability table adopts one of a Bayesian network, a neural network or a decision tree.
Specifically, the embodiment provides an interactive medical guide process, which is started after receiving a user activation instruction; the method comprises the steps of firstly entering a first page, initiating an inquiry request or a search request to a user in a search page mode, and recognizing the intention of the user as inquiry or encyclopedia search according to different entry information of the user. The sub-graph extraction is performed on the knowledge graph, for example, when the user intends to perform an inquiry, the relevant knowledge graph of the inquiry module is called as an inquiry path and enters an inquiry process to perform the inquiry according to the inquiry path, when the cold is subjected to the inquiry, the content of the inquiry path includes a plurality of pending results related to the user intention and a plurality of project relations related to the pending results, the pending results are, for example, a department treating the cold or a medicine treating the cold, and other related projects, such as headache, cough, rhinorrhea and the like related to the cold and duration, mutual relations and the like thereof; collecting user information such as basic information sex, age, physical sign weight, height, medical history, medicine allergy history, living habits, breakfast dinner time and the like in the forms of conversation, character tabs and picture tabs according to the probability map model, introducing the user information into the probability map model, and calling related prior probability; preferably, the query process will change the question of the next round based on the inference result of the current round.
The present embodiment also provides an example of inference calculation, when determining the query path as a bayesian network structure according to the user's intention, assuming that the total probability formula for a certain pending result (x1, x2, x3, x4, x5, x6, x7) is:
P(x1,x2,x3,x4,x5,x6,x7)＝P(x1)P(x2)P(x3)P(x4∣x1,x2,x3)P(x5∣x1,x3)P(x6∣x4)P(x7∣x4,x5)
at this time, the inquiry flow is designed according to the inquiry path requirement, and user information about x1, x2, x3, x4, x5, x6 and x7 is collected to the user and converted into characteristics, wherein the characteristics comprise information of various probabilities or conditional probabilities P (x1), P (x2), P (x3), P (x4 |. x1, x2, x3), P (x5 |. x1, x3), P (x6 |. x4), P (x7 |. x4, x5) and the like, and finally, probability values P (x1, x2, x3, x4, x5, x6 and x7) of the inference result are calculated according to the inference model, and are fed back to the user information base to update the prior probability map.
Finally, generating a result report according to a probability result table consisting of reasoning calculation results and an inquiry process record and feeding the result report back to the user; preferably, different feedback strategies can be determined according to actual requirements, such as performing probability sorting and probability division and then performing feedback. In this embodiment, the factors involved in the inquiry flow, that is, the process records and feeds back the etiology content, and the diagnosis condition, that is, the probability inference calculation result, such as the diseased diagnosis result probability, the suggested medication probability, and the like, is fed back to the user.
The invention also provides a reasoning inquiry system for the question-answering system, which comprises a triggering module, an extraction module, a question module, a calculation module and a feedback module;
the triggering module receives a reasoning inquiry process triggered by a question-answering system, screens a plurality of core entities and determines an alternative entity set;
the extraction module extracts subgraphs from the knowledge graph and constructs a probability graph model according to the alternative entity set by combining with scene rules;
the query module carries out reasoning query according to the probabilistic graph model so as to acquire characteristics;
the computing module uses the features to perform marginal probability computation on the core entity;
the feedback module feeds the marginal probability as a result back to the question-answering system.
The present invention also provides a computer-readable storage medium characterized in that: the computer readable storage medium stores computer instructions, and when executed by a processor, the computer implements the inference query method for question-answering system as described in any one of the above.
In specific implementation, the computer-readable storage medium is a magnetic Disk, an optical Disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid-State Drive (SSD), or the like; the computer readable storage medium may also include a combination of memories of the above kinds.
The present invention also provides a computer device characterized in that: comprising at least one processor, and a memory communicatively coupled to the processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to cause the processor to perform the inferential query method for a question-and-answer system as described in any one of the above.
In particular, the number of processors may be one or more, and the processor may be a Central Processing Unit (CPU). The Processor may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or a combination thereof. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may be communicatively coupled to the processors via a bus or otherwise, the memory storing instructions executable by the at least one processor to cause the processor to perform the method of multi-round intelligent query processing as described in any of the above.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A reasoning inquiry method for a question-answering system is characterized by comprising the following steps:
s10: the question-answering system triggers a reasoning inquiry process, screens a plurality of core entities and determines an alternative entity set;
s20: extracting subgraphs from the knowledge graph by combining scene rules and constructing a probability graph model according to the alternative entity set;
s30: performing inference inquiry according to the probabilistic graphical model to acquire characteristics;
s40: performing marginal probability calculation on the core entity by using the features;
s50: and feeding back the marginal probability as a result to the question-answering system.
2. The inference query method for question-answering system according to claim 1, characterized by: the knowledge graph further comprises statistical weights, and the statistical weights are used for realizing the construction of the probability graph model.
3. The inference query method for question-answering system according to claim 2, characterized by: the statistical weight refers to the conditional probability in the probability map model, the statistical weight is obtained through statistics, and the statistical method follows a law of large numbers and a maximum likelihood estimation method.
4. The inference query method for question-answering system according to claim 3, characterized by: step S20 includes the following steps:
s21: extracting the subgraph by taking a plurality of core entities in the alternative entity set as cores;
s22: inquiring according to the subgraph to acquire key information, wherein the key information comprises entities, relations and weights;
s23: and constructing the probability graph model according to the key information.
5. The inference query method for question-answering system according to claim 4, characterized by: step S23 further includes: and generating a probability table according to the key information.
6. The inference query method for question-answering system according to claim 5, characterized by: in step S40, the inference information is collected by inquiry according to the probabilistic graphical model and the probability table, and the edge probability of the core entity is calculated by using the inference information as a conditional probability.
7. The inference query method for question-answering system according to claim 6, characterized by: and the inference calculation method for calculating the marginal probability of the core entity according to the probability map model and the probability table adopts one of a Bayesian network, a neural network or a decision tree.
8. A rational inquiry system for a question-answering system, characterized by: the system comprises a trigger module, an extraction module, an inquiry module, a calculation module and a feedback module;
the triggering module receives a reasoning inquiry process triggered by a question-answering system, screens a plurality of core entities and determines an alternative entity set;
the extraction module extracts subgraphs from the knowledge graph and constructs a probability graph model according to the alternative entity set by combining with scene rules;
the query module carries out reasoning query according to the probabilistic graph model so as to acquire characteristics;
the computing module uses the features to perform marginal probability computation on the core entity;
the feedback module feeds the marginal probability as a result back to the question-answering system.
9. A computer-readable storage medium characterized by: the computer-readable storage medium stores computer instructions which, when executed by a processor, implement the inference query method for question-answering system according to any one of claims 1 to 7.
10. A computer device, characterized by: comprising at least one processor, and a memory communicatively coupled to the processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to cause the processor to perform the inferential query method for a question-and-answer system as recited in any one of claims 1-7.
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