CN112463973A

CN112463973A - Construction method, device and medium of medical knowledge graph and electronic equipment

Info

Publication number: CN112463973A
Application number: CN201910844375.9A
Authority: CN
Inventors: 李林峰
Original assignee: Yidu Cloud Beijing Technology Co Ltd
Current assignee: Yidu Cloud Beijing Technology Co Ltd
Priority date: 2019-09-06
Filing date: 2019-09-06
Publication date: 2021-03-09

Abstract

The embodiment of the disclosure provides a construction method and device of a medical knowledge graph, a computer readable medium and electronic equipment, and relates to the technical field of self-medical data processing. The method comprises the following steps: acquiring a triplet for representing target medical knowledge; acquiring additional semantic features defining the target medical knowledge on the basis of the semantic features expressed by the triples; expanding the triplets into quadruplets based on the additional semantic features to construct a medical knowledge-graph based on the quadruplets. The knowledge graph provided by the technical scheme can improve the expression accuracy of medical knowledge; meanwhile, the number of original triples is not additionally increased, and the low query efficiency based on the medical knowledge graph is avoided. In addition, the technical scheme can improve the visualization degree of the medical knowledge graph and the medical reasoning efficiency.

Description

Construction method, device and medium of medical knowledge graph and electronic equipment

Technical Field

The present disclosure relates to the technical field of medical data processing, and in particular, to a method and an apparatus for constructing a medical knowledge graph, a computer-readable medium, and an electronic device.

Background

Medical knowledge-graph technology is a key technology that enables a computer algorithm system to understand medical knowledge. Illustratively, the computer algorithm system can realize various medical tasks such as auxiliary diagnosis and treatment, quality control of medical records, intelligent diagnosis guide, automatic underwriting and the like based on the medical knowledge map technology.

In the prior art, a medical knowledge map is generally constructed based on a triplet in the form of "Subject predictor Object" (which may be abbreviated as "sp O" respectively), such as: < S: type 2 diabetes P: symptom O: polydipsia >. In the triplet, the subject S is "type 2 diabetes mellitus", the predicate P is "pathology", and the object O is "polydipsia". Thus, a medical knowledge graph is constructed based on a plurality of triplets in the form of "principals and predicates".

However, the medical knowledge map provided by the related art needs to be improved in the information content to improve the accuracy of the representation of the medical knowledge.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

An object of the embodiments of the present disclosure is to provide a method for constructing a medical knowledge graph, a device for constructing a medical knowledge graph, a computer-readable medium, and an electronic device, so as to improve the amount of information included in the medical knowledge graph at least to a certain extent and improve the accuracy of expression of medical knowledge.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.

According to a first aspect of the embodiments of the present disclosure, there is provided a method for constructing a medical knowledge-graph, including:

acquiring a triplet for representing target medical knowledge;

acquiring additional semantic features for limiting the target medical knowledge on the basis of the semantic features expressed by the triples;

and expanding the triples into quadruples based on the additional semantic features so as to construct the medical knowledge map based on the quadruples.

In an embodiment of the present disclosure, based on the foregoing scheme, expanding the triplet into a quadruplet based on the additional semantic features includes:

expressing the additional semantic features as a mode of 'key word-value' to obtain an extended field;

and combining the extension field and the triple to obtain an extended quadruple.

In an embodiment of the present disclosure, based on the foregoing scheme, the extension field includes at least one "keyword-value"; when a plurality of "key-values" are included, the extension field has a parallel structure.

In an embodiment of the present disclosure, based on the foregoing scheme, the extension field is in a nested structure, and the "value" of the "keyword-value" corresponding to the extension field includes "sub-keyword-sub-value".

In an embodiment of the present disclosure, based on the foregoing solution, the additional semantic features at least include one or more of the following information: probability limits, age limits, and population limits.

In one embodiment of the present disclosure, based on the foregoing scheme, obtaining a triplet for representing target medical knowledge includes:

acquiring a first entity related to the target medical knowledge and acquiring attribute information related to the target medical knowledge;

acquiring a second entity related to the target medical knowledge according to the first entity and the attribute information;

determining a triplet for representing the target medical knowledge based on the first entity, the attribute relationship, and the second entity.

obtaining a first entity relating to the target medical knowledge and obtaining a second entity relating to the target medical knowledge;

determining attribute information about between said first entity and said second entity;

According to a second aspect of the embodiments of the present disclosure, there is provided a medical knowledge-graph constructing apparatus, including:

a triplet acquisition module to: acquiring a triplet for representing target medical knowledge;

an additional semantic feature acquisition module to: acquiring additional semantic features for limiting the target medical knowledge on the basis of the semantic features expressed by the triples;

a knowledge graph construction module to: and expanding the triples into quadruples based on the additional semantic features so as to construct the medical knowledge map based on the quadruples.

According to a third aspect of the embodiments of the present disclosure, there is provided a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the method of constructing a medical knowledge-graph as described in the first aspect of the embodiments above.

According to a fourth aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method of constructing a medical knowledge-graph as described in the first aspect of the embodiments above.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:

in some embodiments of the present disclosure, a triplet for representing the target medical knowledge is first obtained, and additional semantic features that further define the target medical knowledge are obtained on the basis of the semantic features expressed by the triplet. And then, expanding the triple into a quadruple based on the acquired additional semantic features. Thus, the amount of information contained in the constituent units of the knowledge graph is increased. Furthermore, the information content contained in the medical knowledge map lock constructed based on the quadruple is improved. Compared with the knowledge graph directly constructed by the triples in the related technology, the knowledge graph provided by the technical scheme can improve the expression accuracy of the medical knowledge.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty. In the drawings:

FIG. 1 shows a system architecture diagram for implementing a construction method of a medical knowledge-graph in an exemplary embodiment of the present disclosure;

FIG. 2 shows a flow diagram of a method of construction of a medical knowledge-graph according to an embodiment of the present disclosure;

FIG. 3 illustrates a flow diagram of a method of determining triples in accordance with an embodiment of the present disclosure;

fig. 4 shows a flow diagram of a method of determining triples according to another embodiment of the present disclosure;

FIG. 5 shows a flow diagram of a method of expansion of a quad according to an embodiment of the disclosure;

FIG. 6 shows a schematic structural diagram of a medical knowledge-map construction apparatus according to an embodiment of the present disclosure;

FIG. 7 shows a schematic diagram of a structure of a computer storage medium in an exemplary embodiment of the disclosure; and the number of the first and second groups,

fig. 8 shows a schematic structural diagram of an electronic device in an exemplary embodiment of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.

The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

The present exemplary embodiment first provides a system architecture for implementing a construction method of a medical knowledge graph, which can be applied to various data processing scenarios. Referring to fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send request instructions or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a photo processing application, a shopping application, a web browser application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, for example, acquiring a triple input by a user using the

terminal device

101, 102, 103 to represent the target medical knowledge, and acquiring additional semantic features defining the target medical knowledge on the basis of the semantic features expressed by the triple (for example only). The backend management server may expand the triples into quadruples based on the additional semantic features to build a medical knowledge-graph based on the quadruples (just an example). The background management server can also receive a query instruction and perform query in the medical knowledge graph based on the query instruction.

It should be noted that the method for constructing the medical knowledge graph provided in the embodiment of the present application is generally executed by the server 105, and accordingly, the device for constructing the medical knowledge graph is generally disposed in the terminal apparatus 101.

In the construction method of the medical knowledge graph provided by the prior art, the triplet based on the method cannot represent the fact based on the additional semantic features with probabilities and the like, for example: not all type II diabetics have symptoms of polydipsia and it is probable that this medical knowledge is established. For another example: the infant pneumonia patients can have 'milk cough' symptoms, so that the people are children in the 'pneumonia symptom milk cough' medical knowledge, and the additional semantic feature needs to be added. Since the triples provided by the related art cannot represent such information, the amount of information contained in the medical knowledge graph formed by the triples based on the related art needs to be improved, and the accuracy of expression of medical knowledge is further improved.

In another related technology, the original triple is used as a subject of a composite triple, the additional semantic features of the medical knowledge expressed by the original triple are used as predicates of the composite triple, and further, the limit value is used as an object of the composite triple. Thus forming a conforming triplet that contains a greater amount of information. For example: the original triad is: < S: type 2 diabetes P: symptom O: polydipsia >. To express that the symptom of the condition type 2 diabetes is polydipsia with a probability of 85%, the following triplets were obtained according to the above-mentioned related art: < S > type 2 diabetes symptom polydipsia > P: probability O:0.85 >.

Although another related technology described above can improve the representation method of the original triplet deadlines, the amount of information contained in the medical knowledge map constructed based on the composite triplets can be improved. However, the manner of adding the additional semantic features to the original triples by conforming to the triples may cause the amount of the triples in the medical map to explode, which may further cause the query efficiency based on the medical knowledge map to be low, for example, when reasoning, a plurality of triples need to be queried to obtain the actual additional semantic features.

In view of the above problems in the related art, the present technical solution provides a method and an apparatus for constructing a medical knowledge graph, a computer storage medium, and an electronic device. The construction method of the medical knowledge map is explained as follows:

fig. 2 shows a flow diagram of a method of construction of a medical knowledge-graph according to an embodiment of the present disclosure. The method for constructing a medical knowledge map provided by the embodiment overcomes the above problems in the prior art at least to a certain extent.

The execution subject of the construction method of the medical knowledge graph provided by the embodiment may be a device with a calculation processing function, such as a server.

Referring to fig. 2, the method for constructing a medical knowledge-graph provided by this embodiment includes:

step S210, acquiring a triple for representing target medical knowledge;

step S220, acquiring additional semantic features for limiting the target medical knowledge on the basis of the semantic features expressed by the triples; and the number of the first and second groups,

step S230, expanding the triples into quadruples based on the additional semantic features, and constructing a medical knowledge map based on the quadruples.

In the technical solution provided in the embodiment shown in fig. 2, on the one hand, the triples are expanded into quadruples based on the acquired additional semantic features. Thus, the amount of information contained in the constituent units of the knowledge graph is increased. Furthermore, the information content contained in the medical knowledge map lock constructed based on the quadruple is improved. Compared with the knowledge graph directly constructed by the triples in the related technology, the knowledge graph provided by the technical scheme can improve the expression accuracy of the medical knowledge.

On the other hand, compared with the composite triples in the related art, the quadruples serving as the constituent units of the medical knowledge graph provided based on the technical scheme do not increase the number of the original triples additionally, and the query efficiency based on the medical knowledge graph is not lowered.

Meanwhile, the medical knowledge is inquired based on the technical scheme, and the more comprehensive information of the related intention knowledge, the visualization degree of the medical knowledge map and the medical reasoning efficiency can be obtained by inquiring a quadruple.

The implementation details of the steps of the solution shown in fig. 2 are explained in detail below:

in an exemplary embodiment, the triples acquired in step S210 for representing the target medical knowledge may be preexisting triples. For example, from an existing medical knowledge-graph constructed from triplets. And then carry out information extension to existing triplets through this technical scheme, further, obtain the medical knowledge map that comprises the quadruple after the extension, realized the improvement to current medical knowledge map, be favorable to promoting the expression accuracy to medical knowledge.

In an exemplary embodiment, the triplets used to represent the target medical knowledge in step S210 may also be obtained from medical knowledge. For example, as a specific implementation of step S210, fig. 3 and fig. 4 below respectively show a flow chart of a determination method of a triplet.

Exemplarily, fig. 3 shows a flow chart of a method for determining triples according to an embodiment of the present disclosure. Referring to fig. 3, the embodiment shown in the figure provides a method comprising:

step S310, acquiring a first entity related to the target medical knowledge, and acquiring attribute information related to the target medical knowledge;

step S320, acquiring a second entity related to the target medical knowledge according to the first entity and the attribute information; and the number of the first and second groups,

step S330, determining a triplet for representing the target medical knowledge based on the first entity, the attribute relationship, and the second entity.

In an exemplary embodiment, medical knowledge is generally derived from the following aspects: on one hand, the knowledge is literature knowledge and is obtained by learning textbooks, clinical guidelines, monographs, treatises and papers and the like; on the other hand, the method is empirical knowledge, and real world clinical data obtained by empirical observation and the like are accumulated in clinical diagnosis and treatment work. The two are complementary relations. In particular, as the medical industry has further recognized the value of empirical knowledge, the term "real world data" (RWD) is used to describe such real world clinical data, and the diagnosis and treatment methods are modified based on the real world data.

It can be seen that the first entity regarding the target medical knowledge and the attribute information regarding the target medical knowledge acquired in step S310, and the second entity regarding the same target medical knowledge acquired in step S320 can be determined from the above-mentioned literature knowledge and real world data.

Illustratively, suppose the above objective medical knowledge is about the disease type 2 diabetes. The first entity may be a disease name entity, such as: the attribute information of the type 2 diabetes can be 'disease', 'cause of disease' or 'treatment', etc. Further, a second entity is determined according to the attribute information, for example: in the case where the above attribute information is "disorder", the second entity that can be determined from medical knowledge may be: polydipsia, polyuria, emaciation, etc. Further, at least one triplet of medical knowledge about the target may be determined, such as: < type 2 diabetes symptom polydipsia >, < type 2 diabetes symptom polyuria > and < type 2 diabetes symptom wasting >. Similarly, with respect to the target medical knowledge, the corresponding at least one second entity may also be determined according to the first entity and other attribute information, and finally the plurality of triples may be determined.

Exemplarily, fig. 4 shows a flow chart of a method of determining triples according to another embodiment of the present disclosure; referring to fig. 4, the embodiment shown in the figure provides a method comprising:

step S410, acquiring a first entity related to the target medical knowledge, and acquiring a second entity related to the target medical knowledge;

step S420, determining attribute information between the first entity and the second entity; and the number of the first and second groups,

step S430, determining a triplet for representing the target medical knowledge based on the first entity, the attribute relationship and the second entity.

In an exemplary embodiment, the first entity and the second entity regarding the target medical knowledge acquired in step S410, and the attribute information determined regarding the target medical knowledge in step S420 may be determined from the above-mentioned literature knowledge and real world data.

Illustratively, suppose the above objective medical knowledge is about the disease type 2 diabetes. The first entity may be a disease name entity, such as: "type 2 diabetes", the second entity above may be "polydipsia". Further, it may be determined in step S420 that the attribute information regarding the relationship between the first entity and the second entity is "disorder" according to medical knowledge. And then determining that the three elements are: < type 2 diabetes symptom polydipsia >. Similarly, regarding the target medical knowledge, the plurality of triples may be finally determined according to the attribute information determined by the first entity and the other second entities.

In an exemplary embodiment, with continued reference to fig. 2, after a triplet is acquired for representing target medical knowledge according to the above-described embodiments, in step S220, additional semantic features defining the target medical knowledge are acquired on the basis of the semantic features expressed by the triplet.

Illustratively, the combination of the triplets and the additional semantic features may be more appropriate for actually expressing the target medical knowledge than the semantic features expressed by the triplets. And then be favorable to promoting the information content that the constitution unit of knowledge map contains to and, be favorable to promoting the expression accuracy to medical knowledge.

Illustratively, the additional semantic features may include one or more of the following information: probability limits, age limits, and population limits. The additional semantic features may further include: a probability and/or degree of trustworthiness of the second entity in the triplet, etc. It should be noted that the additional semantic features are determined according to further defining the current triple features, that is, the specific content of the additional semantic features is determined according to actual requirements, and is not limited herein.

For example, with respect to the existing triplet "< type 2 diabetes symptom polydipsia >", the semantic features expressed are: symptoms of type 2 diabetes include polydipsia. However, it is known from medical knowledge that not all of the two types of diabetics have symptoms of polydipsia and that it is probable that this medical knowledge is established. In the technical scheme, the additional semantic features can be obtained according to medical knowledge, and particularly the probability that the two types of diabetes patients have polydipsia.

For another example: with respect to the existing triple "< pneumonia symptom cough >", the semantic features expressed by the triple are as follows: symptoms of pneumonia include milk cough. However, according to medical knowledge, only infant pneumonia patients can have 'milk cough' symptoms, and then the infant pneumonia patients are based on semantic features expressed by the 'pneumonia symptom milk cough'. It is also necessary to obtain the above-mentioned additional semantic features based on medical knowledge, in particular to limit the population of patients with pneumonia whose symptoms include milk cough to infants.

In an exemplary embodiment, additional semantic features about the target medical knowledge may be obtained from medical literature knowledge and medical true temporal data.

In an exemplary embodiment, with continued reference to fig. 2, after acquiring triples representing the target medical knowledge and additional semantic features according to the above embodiments, in step S230, the triples are expanded into quadruples based on the additional semantic features to construct a medical knowledge-graph based on the quadruples.

Exemplarily, fig. 5 shows a flowchart of a quadruple expansion method according to an embodiment of the present disclosure, which can be taken as a specific implementation manner of step S230. Referring to fig. 5, the embodiment shown in the figure provides a method comprising:

step S510, expressing the additional semantic features as a mode of 'key word-value' to obtain an expanded field; and the number of the first and second groups,

and step S520, combining the extension field and the triple to obtain an extended quadruple.

In an exemplary embodiment, for the acquired additional semantic features, the technical scheme uses a storage mode of a (Key-Value, K-V for short) dictionary, so as to obtain the extension fields of the triples, and meanwhile, the Key-Value pair mode can effectively ensure the extensibility of data. Further, the extension field is used as a fourth tuple and is combined into the triple to obtain an extended Quadruplet quadrupulet. For example:

q1: < type 2 diabetes symptom polydipsia { "viability": 0.85} >;

q2: pneumonia symptom cough milk { "outputation": infant "} >;

q3: < pneumonia medicine moxifloxacin { "age": { "gt": 18} } >.

For example, the semantic features expressed by the quadruple Q1 are: the probability of polydipsia symptoms in type II diabetics was 85%. Wherein, the extension field in the quadruplet Q1 is { "robustness": 0.85}, and the keyword Key is the robustness and represents the probability limit; the Value is 0.85, representing a probability Value.

For another example: the semantic features expressed by the quadruple Q2 are: infants among pneumonia patients have symptoms of milk cough. Wherein, the extension field in the quadruplet Q2 is { "position": infant "}, and the keyword Key is position, which represents the crowd limitation; the Value is "infant".

For another example: the semantic features expressed by the quadruple Q3 are: patients with pneumonia older than 18 years old may be prescribed moxifloxacin. The extension field in the quadruplet Q3 is { "age": { "gt": 18} }, and the keyword Key is age and represents age limit; the Value is KV dictionary { "gt": 18}, wherein the sub-keyword Key is: gt, representing greater than "greaterthanan", the sub-Value is: 18.

in an exemplary embodiment, as seen from the quadruplet Q3, the extension field is a nested structure, wherein the value of the key-value of the extension field contains the sub-key-sub-value.

In an exemplary embodiment, the quadruplet Q3 may also be a parallel structure, and the extension fields correspond to "keyword 1-value 1", "keyword 2-value 2", and "keyword 3-value 3", etc. For example, the following quadruple Q4:

in the technical scheme provided by the embodiment shown in fig. 5, the medical knowledge is queried based on the technical scheme, and a quadruple is queried to obtain more comprehensive information of related intention knowledge, so that the knowledge graph query efficiency and the medical reasoning efficiency after the knowledge graph is queried are improved. Meanwhile, the actual medical knowledge of the attached medicine is expressed through the expression diversity of the additional semantic features, and the map visualization is favorably improved.

Those skilled in the art will appreciate that all or part of the steps for implementing the above embodiments are implemented as computer programs executed by a processor (including a CPU and a GPU). For example, model training of the risk prediction model is implemented by the GPU, or risk level prediction processing of the object to be measured is implemented by using the CPU or the GPU based on the trained risk prediction model. When executed by the CPU, performs the functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.

Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.

The following describes embodiments of the apparatus of the present disclosure, which can be used to perform the above-mentioned method for constructing a medical knowledge-graph of the present disclosure.

Fig. 6 shows a schematic structural diagram of a medical knowledge-map constructing apparatus according to an embodiment of the present disclosure, and referring to fig. 6, the present embodiment provides a medical knowledge-map constructing apparatus 600, including: a triple obtaining module 601, an additional semantic feature obtaining module 602, and a knowledge graph constructing module 603.

The triple obtaining module 601 is configured to: acquiring a triplet for representing target medical knowledge;

the above-mentioned additional semantic feature obtaining module 602 is configured to: acquiring additional semantic features for limiting the target medical knowledge on the basis of the semantic features expressed by the triples;

the knowledge graph building module 603 is configured to: and expanding the triples into quadruples based on the additional semantic features so as to construct the medical knowledge map based on the quadruples.

In an embodiment of the present disclosure, based on the foregoing solution, the knowledge graph constructing module 603 includes: an extension field determination submodule and a combination submodule.

Wherein, the extension field determining submodule is configured to: expressing the additional semantic features as a mode of 'key word-value' to obtain an extended field; and, the combining submodule, configured to: and combining the extension field and the triple to obtain an extended quadruple.

In an embodiment of the present disclosure, based on the foregoing scheme, the triple obtaining module 601 is specifically configured to:

acquiring a first entity related to the target medical knowledge and acquiring attribute information related to the target medical knowledge; acquiring a second entity related to the target medical knowledge according to the first entity and the attribute information; and determining a triplet for representing the target medical knowledge based on the first entity, the attribute relationship, and the second entity.

In an embodiment of the present disclosure, based on the foregoing scheme, the triple obtaining module 601 is further specifically configured to:

obtaining a first entity relating to the target medical knowledge and obtaining a second entity relating to the target medical knowledge; determining attribute information about between said first entity and said second entity; and determining a triplet for representing the target medical knowledge based on the first entity, the attribute relationship, and the second entity.

For details which are not disclosed in the embodiment of the apparatus of the present disclosure, please refer to the embodiment of the method of constructing a medical knowledge-map described above in the present disclosure for the details which are not disclosed in the embodiment of the apparatus of the present disclosure.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

In an exemplary embodiment of the present disclosure, there is also provided a computer storage medium capable of implementing the above method. On which a program product capable of implementing the above-described method of the present specification is stored. In some possible embodiments, various aspects of the present disclosure may also be implemented in the form of a program product including program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present disclosure described in the "exemplary methods" section above of this specification when the program product is run on the terminal device.

Referring to fig. 7, a program product 700 for implementing the above method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product described above may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

An electronic device 800 according to this embodiment of the disclosure is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present disclosure.

As shown in fig. 8, electronic device 800 is in the form of a general purpose computing device. The components of the electronic device 800 may include, but are not limited to: the at least one processing unit 810, the at least one memory unit 820, and a bus 830 that couples the various system components including the memory unit 820 and the processing unit 810.

Wherein the storage unit stores program codes, and the program codes can be executed by the processing unit 810, so that the processing unit 810 executes the steps according to various exemplary embodiments of the present disclosure described in the "exemplary method" section above in this specification. For example, the processing unit 810 may perform the following as shown in fig. 2: step S210, acquiring a triple for representing target medical knowledge; step S220, acquiring additional semantic features for limiting the target medical knowledge on the basis of the semantic features expressed by the triples; and step S230, expanding the triples into quadruples based on the additional semantic features so as to construct a medical knowledge map based on the quadruples.

Illustratively, the processing unit 810 may further perform a method for constructing a medical knowledge-map as shown in any one of fig. 2 to 5.

The storage unit 820 may include readable media in the form of volatile memory units such as a random access memory unit (RAM)8201 and/or a cache memory unit 8202, and may further include a read only memory unit (ROM) 8203.

The storage unit 820 may also include a program/utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 830 may be any of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 800 may also communicate with one or more external devices 900 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 800, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 800 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 850. Also, the electronic device 700 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 870. As shown, the network adapter 860 communicates with the other modules of the electronic device 800 via the bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

Furthermore, the above-described figures are merely schematic illustrations of processes included in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A method for constructing a medical knowledge map, comprising:

acquiring a triplet for representing target medical knowledge;

acquiring additional semantic features defining the target medical knowledge on the basis of the semantic features expressed by the triples;

expanding the triplets into quadruplets based on the additional semantic features to construct a medical knowledge-graph based on the quadruplets.

2. The method of constructing a medical knowledge-graph according to claim 1, wherein expanding the triples into quadruples based on the additional semantic features comprises:

3. The medical knowledge-graph construction method according to claim 2, wherein the extension field contains at least one "keyword-value"; when multiple "keyword-values" are included, the extension field is a parallel structure.

4. The method for constructing a medical knowledge-graph according to claim 2, wherein the extension field is a nested structure, and the value of the keyword-value corresponding to the extension field comprises sub-keyword-sub-value.

5. The medical knowledge-graph construction method according to any one of claims 1 to 4, wherein the additional semantic features at least comprise one or more of the following information: probability limits, age limits, and population limits.

6. The method of constructing a medical knowledge-graph of claim 1 wherein obtaining triples representing a target medical knowledge comprises:

obtaining a first entity regarding the target medical knowledge, and obtaining attribute information regarding the target medical knowledge;

7. The method of constructing a medical knowledge-graph of claim 1 wherein obtaining triples representing a target medical knowledge comprises:

obtaining a first entity regarding the target medical knowledge and obtaining a second entity regarding the target medical knowledge;

determining attribute information about between the first entity and the second entity;

8. An apparatus for constructing a medical knowledge map, comprising:

an additional semantic feature acquisition module to: acquiring additional semantic features defining the target medical knowledge on the basis of the semantic features expressed by the triples;

a knowledge graph construction module to: expanding the triplets into quadruplets based on the additional semantic features to construct a medical knowledge-graph based on the quadruplets.

9. A computer-readable medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out a method of constructing a medical knowledge-graph according to any one of claims 1 to 7.

10. An electronic device, comprising:

one or more processors;

a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method of constructing a medical knowledge-graph as claimed in any one of claims 1 to 7.