CN113254650A - Knowledge graph-based assessment pushing method, system, equipment and medium - Google Patents
Knowledge graph-based assessment pushing method, system, equipment and medium Download PDFInfo
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Abstract
The invention provides a knowledge graph-based assessment pushing method, a knowledge graph-based assessment pushing system, knowledge graph-based assessment pushing equipment and a knowledge graph-based assessment pushing medium, wherein the knowledge graph-based assessment pushing system comprises the following steps: acquiring a detection record of a target object in a preset time period, and generating a knowledge graph of the target object according to the detection record; creating a negative entity library, and comparing entity information in the knowledge graph with entity information in the negative entity library to obtain a negative entity set of the target object; acquiring the information entropy of the negative entity set, and acquiring corresponding push information according to the negative entity information contained in the negative entity set if the information entropy exceeds a set threshold; the invention can ensure the accuracy of information pushing and improve the data processing efficiency.
Description
Technical Field
The invention relates to the field of natural language processing, in particular to a knowledge graph-based assessment pushing method, system, device and medium.
Background
A large amount of personal related data records can be generated in daily activities and communication processes, problems can be found in time aiming at effective mining of personal data, and targeted guidance is given. Although the existing data pushing method aiming at preference information such as personal browsing records and the like is lack of more precise data filtering, a large amount of junk information can be generated, and great inconvenience is brought to personal life.
Disclosure of Invention
In view of the problems existing in the prior art, the invention provides a knowledge graph-based assessment push method, a knowledge graph-based assessment push system, a knowledge graph-based assessment push device and a knowledge graph-based assessment push medium, and mainly solves the problems that the existing method is low in efficiency and insufficient in pertinence in processing personal data.
In order to achieve the above and other objects, the present invention adopts the following technical solutions.
A knowledge graph-based assessment pushing method comprises the following steps:
acquiring a detection record of a target object in a preset time period, and generating a knowledge graph of the target object according to the detection record;
creating a negative entity library, and comparing entity information in the knowledge graph with entity information in the negative entity library to obtain a negative entity set of the target object;
and acquiring the information entropy of the negative entity set, and acquiring corresponding push information according to the negative entity information contained in the negative entity set if the information entropy exceeds a set threshold.
Optionally, creating a negative entity library, comprising:
acquiring a sample data set, labeling negative entities in the sample data set, and segmenting words according to labeling information to acquire a keyword data set;
clustering the keyword data sets to obtain a plurality of synonym sets;
and creating a standard word bank based on the negative entities, and performing associated storage on each standard word in the standard word bank and the matched similar meaning word set to construct a negative entity bank.
Optionally, obtaining the information entropy of the negative entity set includes:
acquiring the quantity of associated negative entities of each negative entity in the negative entity set according to the knowledge graph, and constructing the information entropy of the negative entity set according to the ratio of the quantity of associated negative entities to the total quantity of negative entities in the negative entity set.
Optionally, obtaining the number of associated negative entities of each negative entity in the set of negative entities according to the knowledge graph includes:
judging whether the current negative entity in the knowledge graph is connected with one or more other negative entities or not, and taking the connected negative entity as an associated negative entity of the current negative entity; and the number of the first and second groups,
if any two negative entities are connected with a common negative entity, the two negative entities connected with the common negative entity are associated negative entities;
the number of associated negative entities of each negative entity is obtained in the above manner.
Optionally, constructing the information entropy of the negative entity set, further includes:
setting weights corresponding to all negative entities in a negative entity library, and determining the weight of each negative entity in the negative entity set according to the weight of entity information in the negative entity library;
and calculating the entropy value of each negative entity in the negative entity set, and then carrying out weighted summation on all negative entities in the negative entity set according to the weight to obtain the information entropy of the negative entity set.
Optionally, obtaining corresponding push information according to the negative entity information included in the negative entity set includes:
and sequencing the negative entities in the negative entity set according to the matched weights in the negative entity library, selecting one or more negative entities exceeding the weight threshold, matching the selected negative entities with data in a pre-established push information database, and acquiring matched push information as output.
Optionally, if the selected negative entities include a plurality of negative entities, judging whether the selected negative entities are associated with each other according to the knowledge graph, taking out the associated negative entities to construct an index, and acquiring corresponding push information according to the constructed index;
and respectively acquiring corresponding push information according to the remaining unassociated negative entities.
A knowledge-graph-based assessment push system, comprising:
the system comprises a knowledge graph construction module, a detection module and a processing module, wherein the knowledge graph construction module is used for acquiring a detection record of a target object in a preset time period and generating a knowledge graph of the target object according to the detection record;
the negative entity acquisition module is used for creating a negative entity library, comparing the entity information in the knowledge graph with the entity information in the negative entity library and acquiring a negative entity set of the target object;
and the push decision module is used for acquiring the information entropy of the negative entity set, and acquiring corresponding push information according to the negative entity information contained in the negative entity set if the information entropy exceeds a set threshold.
A knowledge-graph based assessment push device, comprising:
one or more processors; and
one or more machine-readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform the knowledgegraph-based assessment push method.
A machine-readable medium having stored thereon instructions, which when executed by one or more processors, cause an apparatus to perform the method for pushing knowledge-graph based assessments.
As described above, the present invention provides a method, a system, a device and a medium for pushing an assessment based on a knowledge graph, which have the following advantages.
The method generates the knowledge graph based on the data of the specific time period, simplifies the characteristic expression of the target object, and is convenient for rapidly knowing the state of the target object in the specific time period; the negative entity library is used for carrying out rapid data screening, and data pushing is obtained according to targeted entity information, so that the data processing efficiency and accuracy are improved; and judging the stability of the knowledge graph based on the information entropy, further filtering unnecessary push information, and ensuring the accuracy and effectiveness of data.
Drawings
Fig. 1 is a flowchart of a method for pushing knowledge-graph-based assessment according to an embodiment of the present invention.
FIG. 2 is a block diagram of a knowledge-graph based assessment push system according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Referring to fig. 1, the present invention provides a knowledge-graph-based assessment pushing method, which includes steps S01-S03.
In step S01, a detection record of the target object within a preset time period is acquired, and a knowledge map of the target object is generated from the detection record.
In an embodiment, taking a medical record as an example, an electronic medical record of a patient in a preset time period can be acquired, entity, relationship and attribute triplets in the electronic medical record are labeled, and entity extraction, entity relationship extraction and attribute extraction are respectively performed based on labeling information. The preset time period may be set according to actual application requirements, and may be set to the last month or half year, for example. The electronic medical record is generally unstructured data, optionally, the electronic medical record may also be converted into structured data to facilitate triple information extraction, and the structured data may be converted into RDF data (linked data) by using a D2R (Database to RDF) technology based on the structured data, which is not described herein in detail. The entities in the electronic medical record can include: name, time, disease, symptoms, etc.; and the relationship is between an entity and an entity, illustratively, "symptoms of a cold are runny noses", wherein the entities are "cold" and "runny noses", respectively, and the "symptoms" are the relationship of the two entities; "A is 35 years old", then "A" is the entity, "age" is the intrinsic attribute of "A", and "35 years old" is the attribute value. Based on the extraction of the triple information, the triple representation can be obtained: entity-relationship-entity, entity-attribute value. And calculating the similarity between the three group representations to establish the connection between the entities to form the knowledge graph. The specific calculation and knowledge graph construction process can adopt the prior art, and the detailed description is omitted here.
In step S02, a negative entity library is created, and the entity information in the knowledge graph is compared with the entity information in the negative entity library to obtain a negative entity set of the target object.
In one embodiment, creating a negative entity library comprises: acquiring a sample data set, labeling negative entities in the sample data set, and segmenting words according to labeling information to acquire a keyword data set; clustering the keyword data sets to obtain a plurality of synonym sets; and creating a standard word bank based on the negative entities, and performing associated storage on each standard word in the standard word bank and the matched similar meaning word set to construct a negative entity bank. Specifically, taking the electronic medical records as an example, the electronic medical records of various patients can be sorted, and negative entities such as diseases and symptoms in the electronic medical records are labeled to obtain a labeled sample data set. And further, performing word segmentation on the text data corresponding to each electronic medical record by adopting a word segmentation algorithm, and extracting keywords corresponding to the labeled text. Due to the fact that different doctors enter electronic diseases for different times and writing styles are different, the same diseases can be described in different ways, such as abbreviations, shorthand writing, approximate expressions and the like. Therefore, the extracted keywords need to be clustered, and keywords with similar meaning expressions are classified into one category, so as to obtain a plurality of synonym sets. The clustering can adopt a conventional clustering method such as a K-MEANS algorithm. Due to the diversity of the expression of the near-meaning words, in order to establish a stable corresponding relationship between the near-meaning words and the professional words in the corresponding field, a standard word bank can be constructed in advance based on the negative entities, and various standardized expression forms of the negative entities are recorded in the standard word bank. For example, the expression "solar dermatitis" corresponds to various expressions such as "solar dermatitis", "sunburn" or "sunburn". And the universal terms are used for constructing a standard word bank, and the standard word bank is corrected based on an industry expert to ensure the accuracy of the terms.
In an embodiment, similarity comparison may be performed between each standard word in the constructed standard word library and a keyword in a near-meaning word set, illustratively, a central key feature in the near-meaning word set is predetermined, similarity comparison is performed between the central key feature and the standard word, and if the similarity between the central key feature and the standard word reaches a similarity threshold, a corresponding near-meaning word set is used as the near-meaning word set of the standard word; further, the standard words and the similar meaning word set are stored in a correlated mode; and repeating the steps to obtain each standard word and a corresponding similar meaning word set for establishing a negative entity library.
In one embodiment, the knowledge graph obtained in step S01 is compared with the negative entities in the negative entity library to obtain the negative entities in the knowledge graph, so as to form a negative entity set of the individual.
In step S03, the information entropy of the negative entity set is obtained, and if the information entropy exceeds the set threshold, the corresponding push information is obtained according to the negative entity information included in the negative entity set.
In one embodiment, obtaining information entropy of the negative entity set comprises:
and acquiring the quantity of the associated negative entities of each negative entity in the negative entity set according to the knowledge graph, and constructing the information entropy of the negative entity set according to the ratio of the quantity of the associated negative entities to the total quantity of the negative entities in the negative entity set.
In one embodiment, obtaining the number of associated negative entities of each negative entity in the set of negative entities according to the knowledge-graph includes: judging whether the current negative entity in the knowledge graph is connected with one or more other negative entities or not, and taking the connected negative entity as an associated negative entity of the current negative entity; and if any two negative entities are connected with a common negative entity, the two negative entities connected with the common negative entity are associated negative entities; the number of associated negative entities of each negative entity is obtained in the above manner. Specifically, let the knowledge graph include nodes A, B, C, D corresponding to the negative entities, where a is connected to B and D, and C is connected to D. Since a is directly connected to B, D, B and D are associated negative entities of a, and C and a have a common negative entity D, the associated negative entity of a is B, C, D; the associated negative entity of B is A; c has an associated negative entity of A, D; the associated negative entity for D is A, C.
In an embodiment, constructing the information entropy of the negative entity set further includes: setting weights corresponding to all negative entities in a negative entity library, and determining the weight of each negative entity in a negative entity set according to the weight of entity information in the negative entity library; and calculating the entropy value of each negative entity in the negative entity set, and then carrying out weighted summation on all negative entities in the negative entity set according to the weight to obtain the information entropy of the negative entity set. Alternatively, a higher weight can be set for serious illness or severe symptom reaction, and a corresponding weight can be set for each negative entity in the negative entity library, for example, the weight of cardiovascular disease can be set to 0.7, and the weight of cold fever type disease can be set to 0.1. When the negative entity set of the knowledge graph is obtained, the corresponding negative entity weight can be synchronously obtained. And sorting the negative entities in the negative entity set from big to small according to the weight. The entropy value of a single negative entity can be expressed as:
Ei=σpilogpi
where σ is the weight of the negative entity i, piAssociation for negative entities iThe ratio of the number of negative entities to the total number of negative entities in the set of negative entities.
The information entropy of a set of negative entities can be expressed as:
E=∑nσpilogpi
wherein n is the total number of negative entities in the negative entity set.
In an embodiment, acquiring corresponding push information according to negative entity information included in a negative entity set includes:
and sequencing the negative entities in the negative entity set according to the matched weights in the negative entity library, selecting one or more negative entities exceeding the weight threshold, matching the selected negative entities with data in a pre-established push information database, and acquiring matched push information as output. The negative entities can be further screened by setting the weight threshold value, more serious diseases or symptoms can be focused on, and unnecessary pushed information is reduced. A database can be established in advance to store health protection knowledge related to various diseases, similarity comparison is carried out on the feature vectors corresponding to the negative entities and the pushing information in the database, and the information with the highest similarity is selected from the matched data and is output as the pushing information.
In one embodiment, if the selected negative entities include a plurality of negative entities, judging whether the selected negative entities are correlated with each other according to the knowledge graph, taking out the correlated negative entities to construct a search formula, and acquiring corresponding push information according to the constructed search formula; and respectively acquiring corresponding push information according to the remaining unassociated negative entities. As shown in the previous example, when the weight of A, C, D reaches the weight threshold, the feature corresponding to A, C, D is used to construct a search formula to form a piece of search information, so as to obtain more comprehensive and accurate push information according to the associated entity, and improve the data processing efficiency.
Referring to fig. 2, the present embodiment provides a knowledge-graph-based evaluation pushing system, which is used for implementing the knowledge-graph-based evaluation pushing method in the foregoing method embodiments. Since the technical principle of the system embodiment is similar to that of the method embodiment, repeated description of the same technical details is omitted.
In one embodiment, a knowledge-graph based assessment push system includes:
the knowledge graph building module 10 is configured to obtain a detection record of a target object within a preset time period, and generate a knowledge graph of the target object according to the detection record;
the negative entity obtaining module 11 is configured to create a negative entity library, compare entity information in the knowledge graph with entity information in the negative entity library, and obtain a negative entity set of the target object;
a push decision module 12, configured to obtain an information entropy of the negative entity set, and if the information entropy exceeds a set threshold, obtain corresponding push information according to the negative entity information included in the negative entity set
The knowledge-graph building module 10 is used to assist in performing step S01 described in the foregoing method embodiments; the negative entity obtaining module 11 is configured to perform step S02 described in the foregoing method embodiment; the push decision module 12 is configured to perform step S03 described in the foregoing method embodiment.
The embodiment of the application further provides an assessment pushing device based on the knowledge graph, and the device may include: one or more processors; and one or more machine readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform the method of fig. 1. In practical applications, the device may be used as a terminal device, and may also be used as a server, where examples of the terminal device may include: the mobile terminal includes a smart phone, a tablet computer, an electronic book reader, an MP3 (Moving Picture Experts Group Audio Layer III) player, an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop, a vehicle-mounted computer, a desktop computer, a set-top box, an intelligent television, a wearable device, and the like.
The present application also provides a machine-readable medium, in which one or more modules (programs) are stored, and when the one or more modules are applied to an apparatus, the apparatus may execute instructions (instructions) of steps included in the knowledge-graph-based assessment pushing method in fig. 1 according to the present application. The machine-readable medium can be any available medium that a computer can store or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Referring to fig. 3, the present embodiment provides a device 80, and the device 80 may be a desktop device, a laptop computer, a smart phone, or the like. In detail, the device 80 comprises at least, connected by a bus 81: a memory 82 and a processor 83, wherein the memory 82 is used for storing computer programs, and the processor 83 is used for executing the computer programs stored in the memory 82 to execute all or part of the steps of the foregoing method embodiments.
The above-mentioned system bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The Memory may include a Random Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
In summary, the knowledge graph-based assessment pushing method, system, device and medium of the present invention simplify information expression of personal data and reduce data occupation by constructing a knowledge graph; based on negative entity matching, quickly acquiring entity information required in the knowledge graph, and narrowing the data range; entity searching is carried out based on the similar meaning word set, and the accuracy of identification is improved; stability evaluation of the knowledge graph is carried out based on the information entropy, and most negative entities which are not concerned are removed only by setting a reasonable information entropy threshold, so that excessive garbage push information is avoided, and user experience is enhanced. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A knowledge graph-based assessment pushing method is characterized by comprising the following steps:
acquiring a detection record of a target object in a preset time period, and generating a knowledge graph of the target object according to the detection record;
creating a negative entity library, and comparing entity information in the knowledge graph with entity information in the negative entity library to obtain a negative entity set of the target object;
and acquiring the information entropy of the negative entity set, and acquiring corresponding push information according to the negative entity information contained in the negative entity set if the information entropy exceeds a set threshold.
2. The knowledge-graph-based assessment pushing method according to claim 1, wherein creating a negative entity library comprises:
acquiring a sample data set, labeling negative entities in the sample data set, and segmenting words according to labeling information to acquire a keyword data set;
clustering the keyword data sets to obtain a plurality of synonym sets;
and creating a standard word bank based on the negative entities, and performing associated storage on each standard word in the standard word bank and the matched similar meaning word set to construct a negative entity bank.
3. The knowledge-graph-based assessment pushing method according to claim 1, wherein obtaining information entropy of said negative entity set comprises:
acquiring the quantity of associated negative entities of each negative entity in the negative entity set according to the knowledge graph, and constructing the information entropy of the negative entity set according to the ratio of the quantity of associated negative entities to the total quantity of negative entities in the negative entity set.
4. The knowledge-graph-based assessment pushing method according to claim 1, wherein obtaining the number of associated negative entities of each negative entity in the set of negative entities according to the knowledge-graph comprises:
judging whether the current negative entity in the knowledge graph is connected with one or more other negative entities or not, and taking the connected negative entity as an associated negative entity of the current negative entity; and the number of the first and second groups,
if any two negative entities are connected with a common negative entity, the two negative entities connected with the common negative entity are associated negative entities;
the number of associated negative entities of each negative entity is obtained in the above manner.
5. The knowledge-graph-based assessment pushing method according to claim 3, wherein constructing the information entropy of said negative entity set further comprises:
setting weights corresponding to all negative entities in a negative entity library, and determining the weight of each negative entity in the negative entity set according to the weight of entity information in the negative entity library;
and calculating the entropy value of each negative entity in the negative entity set, and then carrying out weighted summation on all negative entities in the negative entity set according to the weight to obtain the information entropy of the negative entity set.
6. The knowledge-graph-based assessment pushing method according to claim 1, wherein obtaining corresponding pushing information according to negative entity information contained in the negative entity set comprises:
and sequencing the negative entities in the negative entity set according to the matched weights in the negative entity library, selecting one or more negative entities exceeding the weight threshold, matching the selected negative entities with data in a pre-established push information database, and acquiring matched push information as output.
7. The knowledge-graph-based assessment pushing method according to claim 6, wherein if the selected negative entities include a plurality of negative entities, judging whether the selected negative entities are associated with each other according to the knowledge graph, taking out the associated negative entities to construct a search formula, and acquiring corresponding pushing information according to the constructed search formula;
and respectively acquiring corresponding push information according to the remaining unassociated negative entities.
8. A knowledge-graph-based assessment pushing system is characterized by comprising:
the system comprises a knowledge graph construction module, a detection module and a processing module, wherein the knowledge graph construction module is used for acquiring a detection record of a target object in a preset time period and generating a knowledge graph of the target object according to the detection record;
the negative entity acquisition module is used for creating a negative entity library, comparing the entity information in the knowledge graph with the entity information in the negative entity library and acquiring a negative entity set of the target object;
and the push decision module is used for acquiring the information entropy of the negative entity set, and acquiring corresponding push information according to the negative entity information contained in the negative entity set if the information entropy exceeds a set threshold.
9. A knowledge-graph-based assessment pushing device, comprising:
one or more processors; and
one or more machine-readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform the method of any of claims 1-7.
10. A machine-readable medium having stored thereon instructions, which when executed by one or more processors, cause an apparatus to perform the method of any of claims 1-7.
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| CN111161213A (en) * | 2019-12-09 | 2020-05-15 | 浙江大学 | Industrial product defect image classification method based on knowledge graph |
| CN111899089A (en) * | 2020-07-01 | 2020-11-06 | 苏宁金融科技(南京)有限公司 | Enterprise risk early warning method and system based on knowledge graph |
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| CN115374106A (en) * | 2022-07-15 | 2022-11-22 | 北京三维天地科技股份有限公司 | Intelligent data grading method based on knowledge graph technology |
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