CN106933983B - Method for constructing traditional Chinese medicine knowledge map - Google Patents
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- CN106933983B CN106933983B CN201710090066.8A CN201710090066A CN106933983B CN 106933983 B CN106933983 B CN 106933983B CN 201710090066 A CN201710090066 A CN 201710090066A CN 106933983 B CN106933983 B CN 106933983B
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The invention discloses a method for constructing a knowledge graph of traditional Chinese medicine, relates to the technical field of medical big data and knowledge graphs, and solves the problems that the existing technical evaluation standards of the knowledge graph of traditional Chinese medicine are inconsistent, and the obtained conclusion is redundant or can not be explained with the traditional compatibility theory. The method is characterized in that the relation between knowledge elements and samples is mined and extracted from Chinese medicine documents and texts, the relation between the knowledge elements and the samples is converted into a directed network vector model through a deep learner, a directed/undirected network of the relation presents a knowledge map model in a visual mode, and then the knowledge map model and a knowledge inference network are visually output. The invention integrates the deep learning technology on the basis of the existing map construction method, endows each knowledge unit with individual coordinate mapping, fully utilizes distance information, embodies the association between the knowledge units and the distance information, and has obvious superiority in the association research of common medicines on semantic retrieval, visual traditional Chinese medicine community discovery, single medicine and basic prescription.
Description
Technical Field
The invention relates to the technical field of medical big data maps, in particular to a construction method of a traditional Chinese medicine knowledge map.
Background
The knowledge map is a knowledge base with a map structure, and belongs to the field of knowledge engineering. Different from a common knowledge base, the knowledge graph integrates all disciplines, knowledge units with different sources, different types and different structures are linked and associated into a graph, and a wider and deeper knowledge system is provided for a user and is continuously expanded on the basis of metadata of each discipline. The method is essentially to systematize and relate domain knowledge data and visualize knowledge in a graph mode. Briefly, the knowledge graph is a knowledge system established based on an information system, and the complex knowledge field is systematically displayed through technologies such as data acquisition, data mining, information processing, knowledge measurement and graph drawing, so that the dynamic development rule of the knowledge field is revealed. The application of the knowledge graph expands the connotation of the original scientific knowledge graph and extends the application scene of the knowledge graph. However, the application of the current knowledge graph is still limited to the aspects of a search engine, a question-answering system and the like, and the application of other aspects is less. In the medical field, especially in traditional Chinese medicine and traditional Chinese medicine, there are usually complex and intricate relationships between diseases and diagnosis and treatment means, and the data storage mode of the existing relationship model is not convenient for the expansion of the content of the medical knowledge system and can not provide intuitive reference for the traditional Chinese medicine personnel. In the existing traditional Chinese medicine knowledge graph technology, under the condition that the parameter setting level is not uniform, the evaluation standards obtained by the traditional Chinese medicine knowledge graph are inconsistent, and the conclusion that redundancy exists or the traditional Chinese medicine knowledge graph cannot be explained with the traditional compatibility theory is easy to obtain.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a method for constructing a knowledge graph of traditional Chinese medicine.
In order to achieve the purpose, the invention adopts the following technical scheme:
a construction method of a traditional Chinese medicine knowledge graph comprises the following steps:
s1, mining and extracting relations between the knowledge elements and the samples from traditional Chinese medicine documents, clinical standardized medical records and famous traditional Chinese medicine test records to serve as basic data for constructing a traditional Chinese medicine knowledge map;
s2, establishing a complex network structure through a deep learner, constructing a knowledge element node set and an edge set according to the relationship between the knowledge elements and the samples, and converting the relationship data into directed weight network data;
and S3, visually outputting the knowledge map model.
Further, in the above method for constructing a knowledge graph of traditional Chinese medicine, the implementation process of step S1 further includes the following steps:
s11, importing data, namely, inputting or importing the data according to a standard structured information template defined by the clinical practice experience of famous old Chinese medicine, and storing demographic data, symptom and chief complaint data, examination and inspection data, syndrome differentiation and medication data and the like in a classified manner;
s12, extracting knowledge element nodes by referring to the existing traditional Chinese medicine ontology standard and establishing a knowledge element node set;
s13, building an ontology-knowledge element classification conceptual structure hierarchy by referring to the existing traditional Chinese medicine ontology system;
and S14, converting the Boolean type data and the classification data checking and checking results into numerical type data.
Further, in the above method for constructing a knowledge graph of traditional Chinese medicine, the implementation process of step S2 further includes the following steps:
s21, weighting the knowledge element according to the node degree of the knowledge element;
s22, establishing a knowledge element relation edge set by taking the text co-occurrence as a basis;
s23, distinguishing edge directions according to the time sequence of the occurrence of the knowledge elements;
and S24, weighting the edge set according to the total number of the simultaneous occurrence text of the knowledge elements.
Further, in the above method for constructing a knowledge graph of traditional Chinese medicine, the implementation process of step S3 further includes the following steps:
s31, according to the step S11 in the step S1, carrying out distribution statistics on each field in the classified storage;
s32, establishing a knowledge element classification tree graph and coloring according to the ontology-knowledge element concept classification level established in the step S13 in the step S1, and adjusting the area of the blocks of the tree graph according to the statistic distribution of the overall data set;
s33, establishing classification data and a diagnosis test result pivot table by taking the concept hierarchy determined in the step S13 in the step S1 as row data and taking the field of the numerical examination test result in the step S14 in the step S1 as measurement;
s34, displaying the statistical result distribution in the step S31 in a corresponding visualization form such as a pie chart, a paper bar chart, a data sheet and the like according to the data field type;
s35, constructing a patient image according to the steps S11 in the step S1, S31 in the step S3, S33 and S34;
s36, establishing symptom chief complaint data analysis according to the step S11 in the step S1 and the step S31 in the step S3;
s37, establishing syndrome differentiation drug use data analysis according to the step S11 in the step S1 and the step S31 in the step S3;
s38, establishing comprehensive perspective analysis of the patient data according to the steps S11 in the step S1, S31 in the step S3 and S32.
Preferably, one of the forms of visually outputting the knowledge map model includes: and (5) integrating the knowledge elements in a classification manner.
Preferably, one of the forms of visually outputting the knowledge map model includes: a user represents a representation.
Preferably, one of the forms of visually outputting the knowledge map model includes: a knowledge map.
Further, the parameters used by the map generator to generate the knowledge map include at least: number of blocks, map size, and a range of itoms.
Preferably, one of the forms of visually outputting the knowledge map model includes: a knowledge inference network.
Further, the knowledge inference network is automatically generated by the knowledge graph model and is used for generating a directed weighting network formed by the knowledge elements, and all sample relations and nodes in the directed weighting network formed by the knowledge elements have weights and can automatically/manually define the clustering quantity.
The invention has the beneficial effects that: the invention integrates the deep learning technology on the basis of the existing map construction method, endows each knowledge unit with individual coordinate mapping, fully utilizes distance information, embodies the association between the distance information and the knowledge units, and has obvious superiority in semantic retrieval of common medicines, visualization traditional Chinese medicine community discovery and association research between symptoms and user groups and between syndrome types and user groups.
Drawings
FIG. 1 is a flow chart of a method for constructing a knowledge graph of traditional Chinese medicine in an embodiment of the present invention;
FIG. 2 is a partial interface diagram of a knowledge graph model for visually outputting a patient image in accordance with the present invention;
FIG. 3 is a partial interface diagram for visually outputting the analysis results of the symptom chief complaint data according to the present invention;
FIG. 4 is a partial interface diagram for visually outputting the results of the dialectical data analysis according to the present invention;
FIG. 5 is a partial interface diagram of a comprehensive perspective analysis of visually outputted patient data in accordance with the present invention;
FIG. 6 is a partial interface diagram of the core symptom cluster knowledge map of the present invention;
FIG. 7 is a local interface diagram of the knowledge reasoning network for medical syndrome differentiation in the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments.
The invention provides a method for constructing a traditional Chinese medicine knowledge graph, the flow steps of the specific construction method are shown in figure 1,
s1, mining and extracting relation between the knowledge elements and the samples from the traditional Chinese medicine documents and the traditional Chinese medicine texts to serve as basic data for constructing the traditional Chinese medicine knowledge map;
s2, establishing a complex network structure through a deep learner, constructing a knowledge element node set and an edge set according to the relationship between the knowledge elements and the samples, and converting the relationship data into directed weight network data;
and S3, visually outputting the knowledge map model.
Specifically, the implementation process of step S1 further includes the following steps:
s11, importing data, namely, inputting or importing the data according to a standard structured information template defined by the clinical practice experience of famous old Chinese medicine, and storing demographic data, symptom and chief complaint data, examination and inspection data, syndrome differentiation and medication data and the like in a classified manner;
s12, extracting knowledge element nodes by referring to the existing traditional Chinese medicine ontology standard and establishing a knowledge element node set;
s13, building an ontology-knowledge element classification conceptual structure hierarchy by referring to the existing traditional Chinese medicine ontology system;
and S14, converting the Boolean type data and the classification data checking and checking results into numerical type data.
The implementation process of step S2 further includes the following steps:
s21, weighting the knowledge element according to the node degree of the knowledge element;
s22, establishing a knowledge element relation edge set by taking the text co-occurrence as a basis;
s23, distinguishing edge directions according to the time sequence of the occurrence of the knowledge elements;
and S24, weighting the edge set according to the total number of the simultaneous occurrence text of the knowledge elements.
The implementation process of step S3 further includes the following steps:
s31, according to the step S11 in the step S1, carrying out distribution statistics on each field in the classified storage;
s32, establishing a knowledge element classification tree graph and coloring according to the ontology-knowledge element concept classification level established in the step S13 in the step S1, and adjusting the area of the blocks of the tree graph according to the statistic distribution of the overall data set;
s33, establishing classification data and a diagnosis test result pivot table by taking the concept hierarchy determined in the step S13 in the step S1 as row data and taking the field of the numerical examination test result in the step S14 in the step S1 as measurement;
s34, displaying the statistical result distribution in the step S31 in a corresponding visualization form such as a pie chart, a paper bar chart, a data sheet and the like according to the data field type;
s35, constructing a patient portrait according to the steps S11 in the step S1, S31 in the step S3, S33 and S34, as shown in FIG. 2;
s36, constructing a chief complaint data analysis according to the step S11 in the step S1 and the step S31 in the step S3, and specifically referring to FIG. 3;
s37, establishing syndrome differentiation drug use data analysis according to the step S11 in the step S1 and the step S31 in the step S3, and specifically as shown in FIG. 4;
s38, patient data comprehensive perspective analysis is constructed according to the steps S11 in the step S1, S31 in the step S3 and S32, and the method is specifically shown in FIG. 5.
In the above method for constructing a knowledge graph of traditional Chinese medicine, one of the visual output forms of the knowledge graph model includes: a knowledge map. Specifically, the parameters used by the map generator to generate the knowledge map at least include: number of blocks, map size, and a range of itoms.
Still further, in the above method for constructing a knowledge graph of traditional Chinese medicine, one of the visual output forms of the knowledge graph model further comprises: a knowledge inference network. As shown in fig. 2, specifically, the knowledge inference network is automatically generated by the knowledge graph spectrum model to generate a directed weighting network formed by the knowledge elements, and each sample relationship and node in the directed weighting network formed by the knowledge elements has a weight and can automatically/manually define the clustering number. The generated core symptom clustering knowledge map is shown in fig. 6, marked by different color blocks, and the generated knowledge inference network for syndrome differentiation of traditional Chinese medicine is shown in fig. 7.
The invention combines the advantages of deep learning, namely the analysis result integrates the characteristics of directed network, semantic distance, coordinate positioning, hierarchical clustering-based community analysis and the like, and greatly enhances the discovery capability of the medicine on the core part, the syndrome pair, the syndrome, the evolution rule of the traditional Chinese medicine pathogenesis and the mutual correlation relationship among the knowledge elements. By integrating a deep learning technology on the basis of the existing map construction method, the individual coordinate mapping of each knowledge unit is given, the distance information is fully utilized, the association between the knowledge units is embodied, and the method has remarkable superiority in association research of common medicines on semantic retrieval and visualized traditional Chinese medicine community discovery.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (8)
1. A construction method of a traditional Chinese medicine knowledge graph is characterized by comprising the following steps:
s1, mining and extracting relations between the knowledge elements and the samples from traditional Chinese medicine documents, clinical standardized medical records and famous traditional Chinese medicine test records to serve as basic data for constructing a traditional Chinese medicine knowledge map;
the implementation process of step S1 further includes the following steps:
s11, importing data, namely, inputting or importing the data according to a standard structured information template defined by the clinical practice experience of famous old Chinese medicine, and storing demographic data, symptom and chief complaint data, examination and inspection data and syndrome differentiation and medication data in a classified manner;
s12, extracting knowledge element nodes by referring to the existing traditional Chinese medicine ontology standard and establishing a knowledge element node set;
s13, building an ontology-knowledge element classification conceptual structure hierarchy by referring to the existing traditional Chinese medicine ontology system;
s14, converting the Boolean data and the classified data inspection result into numerical data;
s2, establishing a complex network structure through a deep learner, constructing a knowledge element node set and an edge set according to the relationship between the knowledge elements and the samples, and converting the relationship data into directed weight network data;
s3, visually outputting the knowledge map model;
the implementation process of step S3 further includes the following steps:
s31, according to the step S11 in the step S1, carrying out distribution statistics on each field in the classified storage;
s32, establishing a knowledge element classification tree graph and coloring according to the ontology-knowledge element classification concept structure hierarchy established in the step S13 in the step S1, and adjusting the area of the blocks of the tree graph according to the statistic distribution of the overall data set;
s33, establishing classification data and a diagnosis test result pivot table by taking the concept hierarchy determined in the step S13 in the step S1 as row data and taking the field of the numerical examination test result in the step S14 in the step S1 as measurement;
s34, displaying the statistical result distribution in the step S31 in a corresponding visualization form such as a pie chart, a paper bar chart, a data sheet and the like according to the data field type;
s35, constructing a patient image according to the steps S11 in the step S1, S31 in the step S3, S33 and S34;
s36, establishing symptom chief complaint data analysis according to the step S11 in the step S1 and the step S31 in the step S3;
s37, establishing syndrome differentiation drug use data analysis according to the step S11 in the step S1 and the step S31 in the step S3;
s38, establishing comprehensive perspective analysis of the patient data according to the steps S11 in the step S1, S31 in the step S3 and S32.
2. The method for constructing a knowledge graph of traditional Chinese medicine according to claim 1, wherein the implementation process of step S2 further comprises the following steps:
s21, weighting the knowledge element according to the node degree of the knowledge element;
s22, establishing a knowledge element relation edge set by taking the text co-occurrence as a basis;
s23, distinguishing edge directions according to the time sequence of the occurrence of the knowledge elements;
and S24, weighting the edge set according to the total number of the simultaneous occurrence text of the knowledge elements.
3. The method of claim 1, wherein the visually outputting the knowledge graph model in one of the forms comprises: and (5) integrating the knowledge elements in a classification manner.
4. The method of claim 1, wherein the visually outputting the knowledge graph model in one of the forms comprises: a user represents a representation.
5. The method of claim 1, wherein the visually outputting the knowledge graph model in one of the forms comprises: a knowledge map.
6. The method of claim 5, wherein the map generator for generating the parameters of the knowledge map at least comprises: number of blocks, map size, and a range of itoms.
7. The method of claim 1, wherein the visually outputting the knowledge graph model in one of the forms comprises: a knowledge inference network.
8. The method according to claim 7, wherein the knowledge inference network is automatically generated by the knowledge graph model to generate a directed weighting network composed of the knowledges, and each sample relationship and node in the directed weighting network composed of the knowledges has a weight and can automatically/manually define the clustering quantity.
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