CN108694177A - Knowledge mapping construction method and system - Google Patents
Knowledge mapping construction method and system Download PDFInfo
- Publication number
- CN108694177A CN108694177A CN201710220269.4A CN201710220269A CN108694177A CN 108694177 A CN108694177 A CN 108694177A CN 201710220269 A CN201710220269 A CN 201710220269A CN 108694177 A CN108694177 A CN 108694177A
- Authority
- CN
- China
- Prior art keywords
- knowledge
- instance
- meta
- content
- text segment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
- G06N5/025—Extracting rules from data
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Computing Systems (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
A kind of knowledge mapping construction method of present invention offer and system, wherein method include:Obtain the corpus information at least one field of knowledge mapping to be built;Participle and part-of-speech tagging are carried out to the knowledge content of text segment that corpus information includes, obtain keyword therein;Keyword is matched according to default rule with domain body, the knowledge meta-instance in acquisition knowledge content of text segment, the incidence relation between the attribute and knowledge meta-instance of knowledge meta-instance;Domain body includes:Field theme, at least one model that field theme includes, the incidence relation between the attribute and model of model;Model includes at least one knowledge meta-instance;Knowledge mapping is built according to the incidence relation between the attribute and knowledge meta-instance of knowledge meta-instance, knowledge meta-instance in multiple knowledge content of text segments.The present invention realizes automation structure knowledge mapping, reduces the construction cost of knowledge mapping, improves the structure efficiency and accuracy rate of knowledge mapping.
Description
Technical field
The present invention relates to pro digital publishing area more particularly to a kind of knowledge mapping construction method and systems.
Background technology
Knowledge services are the hot spots that pro digital publishing area is pursued for a period of time recently, and country is a large amount of in this respect to be provided
Gold input also accelerates the time that knowledge services are landed in publisher.But the domestic Knowledge Service System built is general at present
For be still traditional document rank knowledge services, traditional full-text search mode is provided, is also in terms of resource associations
Interrelational form between document and bibliography.
In order to realize real knowledge retrieval, build as knowledge retrieval base support various types of knowledge hierarchies just
At key, current part society of commercial press leading in terms of knowledge hierarchy structure has conveniently had in field thesaurus
Certain accumulation, but for knowledge retrieval, build domain body and knowledge mapping is only optimal target.
However, for professional domain, it is desirable to be able to which the expert being well understood by this profession could be qualified next manual
Knowledge mapping is built, has put into a large amount of input of artificial and time, the efficiency for building knowledge mapping is too low, and cost is too high, accurately
Rate is too low.
Invention content
The present invention provides a kind of knowledge mapping construction method and system, for solve in the prior art structure knowledge mapping at
This height, the low problem of efficiency.
The first aspect of the invention is to provide a kind of knowledge mapping construction method, including:
The corpus information at least one field of knowledge mapping to be built is obtained, the corpus information includes:Multiple knowledge
Content of text segment;
Participle and part-of-speech tagging are carried out to the knowledge content of text segment, obtained in the knowledge content of text segment
Keyword;
The keyword is matched according to default rule with domain body, the knowledge content of text segment is obtained
In knowledge meta-instance, the knowledge meta-instance attribute and the knowledge meta-instance between incidence relation;The field
Ontology includes:Field theme, at least one model that field theme includes, the association between the attribute and model of model are closed
System;The model includes at least one knowledge meta-instance;
According to the attribute of knowledge meta-instance, the knowledge meta-instance in the multiple knowledge content of text segment and institute
State the incidence relation structure knowledge mapping between knowledge meta-instance.
Further, described to match the keyword with domain body according to default rule, know described in acquisition
Knowledge meta-instance in knowledge content of text segment, the association between the attribute and the knowledge meta-instance of the knowledge meta-instance
Relationship, including:
By the field theme at least one of knowledge content of text segment keyword and the domain body into
Row matching, determines the field theme of the knowledge content of text segment;
By at least one of knowledge content of text segment keyword according to default rule and corresponding field master
The included model of topic is matched, in determining and the knowledge content of text fragment match model and the knowledge text
Hold the knowledge meta-instance of segment;
By at least one of knowledge content of text segment keyword according to default rule and corresponding model
Attribute is matched, and determines the attribute of knowledge meta-instance in the knowledge content of text segment;
The attribute of knowledge meta-instance in incidence relation and the knowledge content of text segment between binding model determines
Incidence relation between knowledge meta-instance.
Further, it is described by least one of knowledge content of text segment keyword according to default rule with
Model included by corresponding field theme is matched, and is determined and the model of the knowledge content of text fragment match and institute
The knowledge meta-instance of knowledge content of text segment is stated, including:
By at least one of knowledge content of text segment keyword and the model included by corresponding field theme
It is matched successively, determines the confidence level of each model;
The model with the knowledge content of text fragment match is determined according to the confidence level of each model;
At least one of knowledge content of text segment keyword is matched with the content of corresponding model, really
The knowledge meta-instance of the fixed knowledge content of text segment.
Further, described by least one of knowledge content of text segment keyword and corresponding field theme
Included model is matched successively, determines the confidence level of each model, including:
Multiple models included by the theme of field for the knowledge content of text segment, by the knowledge content of text
At least one of segment keyword is matched successively with multiple contents of the model, determines the weight of the model;
According to the weight of the multiple model, the confidence level of the multiple model is determined.
Further, the attribute of the knowledge meta-instance includes:Common property and relating attribute;
The common property includes:Primary attribute;
The knowledge meta-instance according in the multiple knowledge content of text segment, the knowledge meta-instance attribute with
And after the incidence relation structure knowledge mapping between the knowledge meta-instance, further include:
Obtain the relevant information of the corresponding knowledge content of text segment of knowledge meta-instance;The relevant information includes:Knowledge
The content and source-information of content of text segment;
The relevant information of the knowledge content of text segment is determined as to the primary attribute value of knowledge meta-instance.
Further, knowledge meta-instance, the Knowledge Element reality according in the multiple knowledge content of text segment
After incidence relation structure knowledge mapping between the attribute and the knowledge meta-instance of example, further include:
Multiple association knowledges member that there is the first incidence relation with the knowledge meta-instance is obtained from the knowledge mapping
Example;
The related letter of the corresponding knowledge content of text segment of the association knowledge meta-instance is obtained from the knowledge mapping
Breath;
The relevant information of the corresponding knowledge content of text segment of the association knowledge meta-instance is determined as knowledge meta-instance
The first relating attribute value.
Further, knowledge meta-instance, the Knowledge Element reality according in the multiple knowledge content of text segment
After incidence relation structure knowledge mapping between the attribute and the knowledge meta-instance of example, further include:
The knowledge mapping is shown.
Further, knowledge meta-instance, the Knowledge Element reality according in the multiple knowledge content of text segment
After incidence relation structure knowledge mapping between the attribute and the knowledge meta-instance of example, further include:
The inquiry instruction of user is received, knowledge meta-instance is carried in the inquiry instruction;
According to the Knowledge Element Query By Example knowledge mapping, attribute, the Knowledge Element for obtaining the knowledge meta-instance are real
Example incidence relation, the corresponding knowledge content of text segment of the knowledge meta-instance and with the knowledge meta-instance have be associated with
The corresponding knowledge content of text segment of association knowledge meta-instance of relationship;
The attribute of the knowledge meta-instance, the incidence relation of the knowledge meta-instance, the knowledge meta-instance is corresponding
Knowledge content of text segment and there is the association knowledge meta-instance of incidence relation corresponding knowledge text with the knowledge meta-instance
This contents fragment is shown.
In the present invention, the corpus information at least one field by obtaining knowledge mapping to be built, the corpus information
Including:Multiple knowledge content of text segments;Participle and part-of-speech tagging are carried out to the knowledge content of text segment, described in acquisition
Keyword in knowledge content of text segment;The keyword is matched according to default rule with domain body, is obtained
Between the attribute and the knowledge meta-instance of knowledge meta-instance, the knowledge meta-instance in the knowledge content of text segment
Incidence relation;The domain body includes:Field theme, at least one model that field theme includes, the attribute of model with
And the incidence relation between model;The model includes at least one knowledge meta-instance;According to the multiple knowledge content of text
Incidence relation structure between the attribute and the knowledge meta-instance of knowledge meta-instance, the knowledge meta-instance in segment is known
Know collection of illustrative plates.The present invention realizes automation structure knowledge mapping, reduces the construction cost of knowledge mapping, improves knowledge mapping
Structure efficiency and accuracy rate.
The second aspect of the invention is to provide a kind of knowledge mapping structure system, including:
Acquisition module, the corpus information at least one field for obtaining knowledge mapping to be built, the corpus information
Including:Multiple knowledge content of text segments;
Participle and part-of-speech tagging module, for carrying out participle and part-of-speech tagging to the knowledge content of text segment,
Obtain the keyword in the knowledge content of text segment;
Matching module is known for matching the keyword with domain body according to default rule described in acquisition
Knowledge meta-instance in knowledge content of text segment, the association between the attribute and the knowledge meta-instance of the knowledge meta-instance
Relationship;The domain body includes:Field theme, at least one model that field theme includes, the attribute and model of model
Between incidence relation;The model includes at least one knowledge meta-instance;
Module is built, for real according to the knowledge meta-instance in the multiple knowledge content of text segment, the Knowledge Element
Incidence relation between the attribute and the knowledge meta-instance of example builds knowledge mapping.
Further, the matching module includes:
First matching unit is used at least one of knowledge content of text segment keyword and the field sheet
Field theme in body is matched, and determines the field theme of the knowledge content of text segment;
Second matching unit is used at least one of knowledge content of text segment keyword according to preset rule
Then matched with the model included by corresponding field theme, determine with the model of the knowledge content of text fragment match with
And the knowledge meta-instance of the knowledge content of text segment;
Third matching unit is used at least one of knowledge content of text segment keyword according to preset rule
It is then matched with the attribute of corresponding model, determines the attribute of knowledge meta-instance in the knowledge content of text segment;
It is real to be used for Knowledge Element in the incidence relation between binding model and the knowledge content of text segment for determination unit
The attribute of example, determines the incidence relation between knowledge meta-instance.
In the present invention, the corpus information at least one field by obtaining knowledge mapping to be built, the corpus information
Including:Multiple knowledge content of text segments;Participle and part-of-speech tagging are carried out to the knowledge content of text segment, described in acquisition
Keyword in knowledge content of text segment;The keyword is matched according to default rule with domain body, is obtained
Between the attribute and the knowledge meta-instance of knowledge meta-instance, the knowledge meta-instance in the knowledge content of text segment
Incidence relation;The domain body includes:Field theme, at least one model that field theme includes, the attribute of model with
And the incidence relation between model;The model includes at least one knowledge meta-instance;According to the multiple knowledge content of text
Incidence relation structure between the attribute and the knowledge meta-instance of knowledge meta-instance, the knowledge meta-instance in segment is known
Know collection of illustrative plates.The present invention realizes automation structure knowledge mapping, reduces the construction cost of knowledge mapping, improves knowledge mapping
Structure efficiency and accuracy rate.
Description of the drawings
Fig. 1 is the flow chart of knowledge mapping construction method one embodiment provided by the invention;
Fig. 2 is the flow chart of another embodiment of knowledge mapping construction method provided by the invention;
Fig. 3 is the flow chart of another embodiment of knowledge mapping construction method provided by the invention;
Fig. 4 is the structural schematic diagram that knowledge mapping provided by the invention builds system one embodiment;
Fig. 5 is the structural schematic diagram that knowledge mapping provided by the invention builds another embodiment of system.
Specific implementation mode
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
The every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
Fig. 1 is the flow chart of knowledge mapping construction method one embodiment provided by the invention, as shown in Figure 1, including:
101, the corpus information at least one field of knowledge mapping to be built is obtained, corpus information includes:Multiple knowledge
Content of text segment.
The executive agent of knowledge mapping construction method provided by the invention is that knowledge mapping builds system, knowledge mapping structure
System can be hardware devices or the softwares on hardware device such as computer, server.
Wherein, field can refer to professional domain, such as " metallurgy " field, " economy " field, " medicine " field etc., and field can
There is " pediatric medicine " field below multiple subdomains, such as " medicine " field to have.Corpus information refers to knowledge content of text piece
Section, the Description of Knowledge content by way of natural language can be divided into raw language material and idiom material.The language of original not processed index
Expect that language material of making a living, the language material after Machining Analysis are known as idiom material.Idiom material includes raw text content, natural language point
Knowledge meta-instance and its attribute etc. described in word result and part of speech analysis result, text.Involved in the present embodiment to wait for structure
The corpus information for building knowledge mapping is made a living language material.For example " meningitis can be confirmed knowledge content of text segment by medical practitioner
Rear antibiotic medicine is treated ".
102, participle and part-of-speech tagging are carried out to knowledge content of text segment, obtains the pass in knowledge content of text segment
Keyword.
103, keyword is matched according to default rule with domain body, is obtained in knowledge content of text segment
Incidence relation between knowledge meta-instance, the attribute of knowledge meta-instance and knowledge meta-instance;Domain body includes:Field master
Topic, at least one model that field theme includes, the incidence relation between the attribute and model of model;Model includes at least one
A knowledge meta-instance.
Wherein, field theme can refer to professional domain or subdomains.Professional domain, such as " metallurgy " field, " economy "
Field, " medicine " field etc., field can have and have " pediatric medicine " field below multiple subdomains, such as " medicine " field.Often
Being had in a field has the models such as " hospital ", " expert ", " disease ", " drug " in multiple models, such as " medicine " field.Each
Model has oneself distinctive attribute, such as " disease " model has " symptom ", " diagnosis ", " pathological change ", " therapeutic scheme "
Equal attributes.Had between model and have " treatment " relationship between various incidence relations, such as " drug " and " disease ", " expert " and
Also the relationship of " being good at treatment " is had between " disease "." meningitis " is a knowledge meta-instance of " disease " model." antibiotic
Drug " is a knowledge meta-instance of " drug " model.There is " treatment " relationship between " antibiotic medicine " and " meningitis ".
104, according to the attribute and Knowledge Element of knowledge meta-instance, knowledge meta-instance in multiple knowledge content of text segments
Incidence relation between example builds knowledge mapping.
Further, after step 104, can also include:Knowledge mapping is shown.
Specifically, knowledge mapping structure system can provide the displaying interface that can be interacted, and be shown in a manner of visual
Knowledge mapping stores collection of illustrative plates node and relationship for example, by using chart database Neo4j, is shown on a web browser using D3 plug-in units whole
A knowledge mapping.In addition, knowledge mapping structure system can also according to the demand of user be shown knowledge mapping.For example,
The knowledge mapping for receiving user checks instruction, and the field theme in some field is carried in instruction;Knowledge mapping build system according to
The knowledge mapping for checking the corresponding field of instruction displaying of user.
Further, after step 104, can also include:The inquiry instruction of user is received, carries and knows in inquiry instruction
Know meta-instance;It is closed according to the association of Knowledge Element Query By Example knowledge mapping, the attribute, knowledge meta-instance that obtain knowledge meta-instance
System, the corresponding knowledge content of text segment of knowledge meta-instance and the association knowledge member reality with knowledge meta-instance with incidence relation
The corresponding knowledge content of text segment of example;By the attribute of knowledge meta-instance, the incidence relation of knowledge meta-instance, knowledge meta-instance pair
The knowledge content of text segment answered and there is the corresponding knowledge text of the association knowledge meta-instance of incidence relation with knowledge meta-instance
This contents fragment is shown.
The knowledge system construction that every field can be inquired by above-mentioned knowledge mapping user, can also inquire and certain
A relevant all documents of knowledge meta-instance, to use.
In the present embodiment, the corpus information at least one field by obtaining knowledge mapping to be built, the language material letter
Breath includes:Multiple knowledge content of text segments;Participle and part-of-speech tagging are carried out to the knowledge content of text segment, obtain institute
State the keyword in knowledge content of text segment;The keyword is matched according to default rule with domain body, is obtained
Take knowledge meta-instance in the knowledge content of text segment, the attribute of the knowledge meta-instance and the knowledge meta-instance it
Between incidence relation;The domain body includes:Field theme, at least one model that field theme includes, the attribute of model
And the incidence relation between model;The model includes at least one knowledge meta-instance;According in the multiple knowledge text
The incidence relation held between the attribute and the knowledge meta-instance of the knowledge meta-instance in segment, the knowledge meta-instance is built
Knowledge mapping.The present invention realizes automation structure knowledge mapping, reduces the construction cost of knowledge mapping, improves knowledge graph
The structure efficiency and accuracy rate of spectrum.
Fig. 2 is the flow chart of another embodiment of knowledge mapping construction method provided by the invention, as shown in Fig. 2, in Fig. 1
On the basis of illustrated embodiment, step 103 can specifically include:
1031, by the field theme progress at least one of knowledge content of text segment keyword and domain body
Match, determines the field theme of knowledge content of text segment.
1032, by least one of knowledge content of text segment keyword according to default rule and corresponding field master
The included model of topic is matched, the model and knowledge content of text segment of determination and knowledge content of text fragment match
Knowledge meta-instance.
Wherein, knowledge mapping structure system can by least one of knowledge content of text segment keyword with it is corresponding
Model included by the theme of field is matched successively, determines the confidence level of each model;Confidence level according to each model is true
Fixed and knowledge content of text fragment match model;By at least one of knowledge content of text segment keyword and corresponding mould
The content of type is matched, and determines the knowledge meta-instance of knowledge content of text segment.
Specifically, multiple models included by the field theme for knowledge content of text segment, knowledge mapping structure system
System can match at least one of knowledge content of text segment keyword successively with multiple contents of the model, really
The weight of cover half type;According to the weight of the multiple model, the confidence level of the multiple model is determined.Wherein, knowledge mapping structure
The system of building can carry out multiple contents of at least one of knowledge content of text segment keyword and the model successively
Match, the quantity of content similar at least one of knowledge content of text segment keyword is determined, so that it is determined that the power of model
Weight.
Wherein, the content of model is the knowledge meta-instance that model includes.
It needs to illustrate, matching rule includes mainly rule name and Rule content two parts, Rule content are
Knowledge mapping builds the object of network analysis application, is to be extended by regular expression, such as " creating the time " is one
Rule, its content are that " ^.* is created in (.*) year .* $ ", which is mainly used to identify the creation time of some object.
This system increases customized identifier on the basis of regular expression.
Proper name word indicator:[[{str}]], wherein str is enumerated value, indicates proper name word class, classification can be in proper name
Defined in vocabulary.Acquiescence includes following several:A:Personage;B:Place;C:Event;D:Mechanism;E:Time.
In addition to acquiescence, can in proper name vocabulary customization type, common sense class word built in the data system of proper name vocabulary
It converges, the proper name word of professional domain can import the dictionary of professional domain.Proper name vocabulary creates for auxiliary regular, extends canonical table
Up to the matching scope of formula, domain knowledge is added.
1033, by least one of knowledge content of text segment keyword according to default rule and corresponding model
Attribute is matched, and determines the attribute of knowledge meta-instance in knowledge content of text segment.
Specifically, the attribute of model includes:Common property and relating attribute.For common property, pass through rule
If with it is rear obtain it is multiple as a result, can in conjunction with weight and number of repetition it is high select one, other attributes are deposited as supplement
Storage is got up, and manual confirmation is waited for.Such as identify " Peking University " creation time have it is multiple as a result, wherein only have one be 1900
Year, others are all 1898, then result can adopt 1898.
For relating attribute, by after rule match if obtained multiple as a result, can be by resulting text content all
It stores.Then system does content of text following analysis.If the relationship between relating attribute and model is reflected
Penetrate, then illustrate that keyword must be Knowledge Element, such as " meningitis can be confirmed by medical practitioner after antibiotic medicine come
Treatment " this section words, the value that the treatment attribute of disease knowledge meta-instance " meningitis " has been gone out by Rule Extraction are that " medical practitioner is true
Antibiotic medicine after recognizing " is obtained " antibiotic medicine " by keyword extraction, and in domain body management, it is provided with
There is treatment relationship between " disease " model and " drug " model, and is mapped with " treatment " attribute of " disease ".So
It is hereby achieved that " antibiotic medicine " is exactly the knowledge meta-instance of " drug " model.At this point, if our knowledge mapping is deposited
At " antibiotic medicine ", it is associated with then can be established with " meningitis ", it can be certainly if there are no " antibiotic ", system in collection of illustrative plates
The dynamic new knowledge meta-instance for generating " drug " model.It is thus achieved that the automation extension of knowledge meta-instance.
1034, in the incidence relation between binding model and knowledge content of text segment knowledge meta-instance attribute, really
Determine the incidence relation between knowledge meta-instance.
In the present embodiment, the corpus information at least one field by obtaining knowledge mapping to be built, the language material letter
Breath includes:Multiple knowledge content of text segments;Participle and part-of-speech tagging are carried out to the knowledge content of text segment, obtain institute
State the keyword in knowledge content of text segment;It will be at least one of knowledge content of text segment keyword and domain body
Field theme matched, determine the field theme of knowledge content of text segment;By in knowledge content of text segment at least
One keyword is matched according to default rule with the model included by corresponding field theme, in determining and knowledge text
Hold the model of fragment match and the knowledge meta-instance of knowledge content of text segment;By at least one in knowledge content of text segment
A keyword is matched according to default rule with the attribute of corresponding model, determines Knowledge Element in knowledge content of text segment
The attribute of example;The attribute of knowledge meta-instance in incidence relation and knowledge content of text segment between binding model determines
Incidence relation between knowledge meta-instance;According to knowledge meta-instance, the knowledge in the multiple knowledge content of text segment
Incidence relation between the attribute of meta-instance and the knowledge meta-instance builds knowledge mapping.The present invention realizes automation structure
Knowledge mapping is built, the construction cost of knowledge mapping is reduced, improves the structure efficiency and accuracy rate of knowledge mapping.
Fig. 3 is the structural schematic diagram of another embodiment of knowledge mapping construction method provided by the invention, as shown in figure 3,
On the basis of embodiment shown in Fig. 1, further include:
105, the relevant information of the corresponding knowledge content of text segment of knowledge meta-instance is obtained;Relevant information includes:Knowledge
The content and source-information of content of text segment.
106, the relevant information of knowledge content of text segment is determined as to the primary attribute value of knowledge meta-instance.
107, multiple association knowledge meta-instances that there is the first incidence relation with knowledge meta-instance are obtained from knowledge mapping.
108, the relevant information of the corresponding knowledge content of text segment of association knowledge meta-instance is obtained from knowledge mapping.
109, the relevant information of the corresponding knowledge content of text segment of association knowledge meta-instance is determined as knowledge meta-instance
The first relating attribute value.
In the present embodiment, by the base that the relevant information of knowledge content of text segment is determined as to corresponding knowledge meta-instance
Plinth attribute value will have the corresponding knowledge content of text of multiple association knowledge meta-instances of the first incidence relation with knowledge meta-instance
Relevant information be determined as knowledge meta-instance the first relating attribute value so that knowledge mapping structure system receiving
It, can be by the attribute of knowledge meta-instance, the association of the knowledge meta-instance when inquiry request of the carrying knowledge meta-instance of user
Relationship, the corresponding knowledge content of text segment of the knowledge meta-instance and the pass with the knowledge meta-instance with incidence relation
The corresponding knowledge content of text segment of connection knowledge meta-instance is shown to user.
One of ordinary skill in the art will appreciate that:Realize that all or part of step of above-mentioned each method embodiment can lead to
The relevant hardware of program instruction is crossed to complete.Program above-mentioned can be stored in a computer read/write memory medium.The journey
When being executed, execution includes the steps that above-mentioned each method embodiment to sequence;And storage medium above-mentioned includes:ROM, RAM, magnetic disc or
The various media that can store program code such as person's CD.
Fig. 4 is the structural schematic diagram that knowledge mapping provided by the invention builds system one embodiment, as shown in figure 4, packet
It includes:
Acquisition module 41, the corpus information at least one field for obtaining knowledge mapping to be built, corpus information packet
It includes:Multiple knowledge content of text segments;
Participle and part-of-speech tagging module 42 are obtained for carrying out participle and part-of-speech tagging to knowledge content of text segment
Take the keyword in knowledge content of text segment;
Matching module 43 obtains knowledge text for matching keyword with domain body according to default rule
Incidence relation between the attribute and knowledge meta-instance of knowledge meta-instance, knowledge meta-instance in contents fragment;Domain body
Including:Field theme, at least one model that field theme includes, the incidence relation between the attribute and model of model;Mould
Type includes at least one knowledge meta-instance;
Module 44 is built, for the category according to knowledge meta-instance, knowledge meta-instance in multiple knowledge content of text segments
Property and knowledge meta-instance between incidence relation build knowledge mapping.
Knowledge mapping structure system provided by the invention can be the hardware devices such as computer, server or be mounted on hard
Software in part equipment.
Further, knowledge mapping structure system can also include:Display module, for being shown to knowledge mapping.
Specifically, knowledge mapping structure system can provide the displaying interface that can be interacted, and be shown in a manner of visual
Knowledge mapping stores collection of illustrative plates node and relationship for example, by using chart database Neo4j, is shown on a web browser using D3 plug-in units whole
A knowledge mapping.In addition, knowledge mapping structure system can also according to the demand of user be shown knowledge mapping.For example,
The knowledge mapping for receiving user checks instruction, and the field theme in some field is carried in instruction;Knowledge mapping build system according to
The knowledge mapping for checking the corresponding field of instruction displaying of user.
Further, knowledge mapping structure system can also receive the inquiry instruction of user, carry and know in inquiry instruction
Know meta-instance;It is closed according to the association of Knowledge Element Query By Example knowledge mapping, the attribute, knowledge meta-instance that obtain knowledge meta-instance
System, the corresponding knowledge content of text segment of knowledge meta-instance and the association knowledge member reality with knowledge meta-instance with incidence relation
The corresponding knowledge content of text segment of example;By the attribute of knowledge meta-instance, the incidence relation of knowledge meta-instance, knowledge meta-instance pair
The knowledge content of text segment answered and there is the corresponding knowledge text of the association knowledge meta-instance of incidence relation with knowledge meta-instance
This contents fragment is shown.
The knowledge system construction that every field can be inquired by above-mentioned knowledge mapping user, can also inquire and certain
A relevant all documents of knowledge meta-instance, to use.
Further, after knowledge mapping structure system structure knowledge mapping, it is corresponding that knowledge meta-instance can also be obtained
The relevant information of knowledge content of text segment;Relevant information includes:The content and source-information of knowledge content of text segment;It will
The relevant information of knowledge content of text segment is determined as the primary attribute value of knowledge meta-instance;From knowledge mapping obtain with it is described
Knowledge meta-instance has multiple association knowledge meta-instances of the first incidence relation;Association knowledge meta-instance is obtained from knowledge mapping
The relevant information of corresponding knowledge content of text segment;By the corresponding knowledge content of text segment of the association knowledge meta-instance
Relevant information is determined as the value of the first relating attribute of knowledge meta-instance.
It is real by the way that the relevant information of knowledge content of text segment is determined as corresponding Knowledge Element that knowledge mapping builds system
The primary attribute value of example will have multiple association knowledge meta-instances of the first incidence relation corresponding knowledge text with knowledge meta-instance
The relevant information of this content is determined as the value of the first relating attribute of knowledge meta-instance, so that knowledge mapping structure system exists
It, can be by the attribute of knowledge meta-instance, the knowledge meta-instance when receiving the inquiry request of carrying knowledge meta-instance of user
Incidence relation, the corresponding knowledge content of text segment of the knowledge meta-instance and with the knowledge meta-instance have be associated with
The corresponding knowledge content of text segment of association knowledge meta-instance of system is shown to user.
In the present embodiment, the corpus information at least one field by obtaining knowledge mapping to be built, the language material letter
Breath includes:Multiple knowledge content of text segments;Participle and part-of-speech tagging are carried out to the knowledge content of text segment, obtain institute
State the keyword in knowledge content of text segment;The keyword is matched according to default rule with domain body, is obtained
Take knowledge meta-instance in the knowledge content of text segment, the attribute of the knowledge meta-instance and the knowledge meta-instance it
Between incidence relation;The domain body includes:Field theme, at least one model that field theme includes, the attribute of model
And the incidence relation between model;The model includes at least one knowledge meta-instance;According in the multiple knowledge text
The incidence relation held between the attribute and the knowledge meta-instance of the knowledge meta-instance in segment, the knowledge meta-instance is built
Knowledge mapping.The present invention realizes automation structure knowledge mapping, reduces the construction cost of knowledge mapping, improves knowledge graph
The structure efficiency and accuracy rate of spectrum.
Further, in conjunction with reference to figure 5, on the basis of embodiment shown in Fig. 4, the matching module 43 includes:
First matching unit 431, for will be at least one of knowledge content of text segment keyword and domain body
Field theme matched, determine the field theme of knowledge content of text segment;
Second matching unit 432 is used at least one of knowledge content of text segment keyword according to preset rule
It is then matched, determine the model with knowledge content of text fragment match and is known with the model included by corresponding field theme
Know the knowledge meta-instance of content of text segment;
Third matching unit 433 is used at least one of knowledge content of text segment keyword according to preset rule
It is then matched with the attribute of corresponding model, determines the attribute of knowledge meta-instance in knowledge content of text segment;
It is real to be used for Knowledge Element in the incidence relation between binding model and knowledge content of text segment for determination unit 434
The attribute of example, determines the incidence relation between knowledge meta-instance.
Wherein, knowledge mapping structure system can by least one of knowledge content of text segment keyword with it is corresponding
Model included by the theme of field is matched successively, determines the confidence level of each model;Confidence level according to each model is true
Fixed and knowledge content of text fragment match model;By at least one of knowledge content of text segment keyword and corresponding mould
The content of type is matched, and determines the knowledge meta-instance of knowledge content of text segment.
Specifically, multiple models included by the field theme for knowledge content of text segment, knowledge mapping structure system
System can match at least one of knowledge content of text segment keyword successively with multiple contents of the model, really
The weight of cover half type;According to the weight of the multiple model, the confidence level of the multiple model is determined.Wherein, knowledge mapping structure
The system of building can carry out multiple contents of at least one of knowledge content of text segment keyword and the model successively
Match, the quantity of content similar at least one of knowledge content of text segment keyword is determined, so that it is determined that the power of model
Weight.
Wherein, the content of model is the knowledge meta-instance that model includes.
It also needs to illustrate, the attribute of model may include:Common property and relating attribute.For common property
For, by after rule match if obtain it is multiple as a result, can in conjunction with weight and number of repetition it is high select one, other
Attribute is stored as supplement, waits for manual confirmation.Such as identify " Peking University " creation time have it is multiple as a result, wherein
Only one is 1900, and others are all 1898, then result can adopt 1898.
For relating attribute, by after rule match if obtained multiple as a result, can be by resulting text content all
It stores.Then system does content of text following analysis.If the relationship between relating attribute and model is reflected
Penetrate, then illustrate that keyword must be Knowledge Element, such as " meningitis can be confirmed by medical practitioner after antibiotic medicine come
Treatment " this section words, the value that the treatment attribute of disease knowledge meta-instance " meningitis " has been gone out by Rule Extraction are that " medical practitioner is true
Antibiotic medicine after recognizing " is obtained " antibiotic medicine " by keyword extraction, and in domain body management, it is provided with
There is treatment relationship between " disease " model and " drug " model, and is mapped with " treatment " attribute of " disease ".So
It is hereby achieved that " antibiotic medicine " is exactly the knowledge meta-instance of " drug " model.At this point, if our knowledge mapping is deposited
At " antibiotic medicine ", it is associated with then can be established with " meningitis ", it can be certainly if there are no " antibiotic ", system in collection of illustrative plates
The dynamic new knowledge meta-instance for generating " drug " model.It is thus achieved that the automation extension of knowledge meta-instance.
In the present embodiment, the corpus information at least one field by obtaining knowledge mapping to be built, the language material letter
Breath includes:Multiple knowledge content of text segments;Participle and part-of-speech tagging are carried out to the knowledge content of text segment, obtain institute
State the keyword in knowledge content of text segment;It will be at least one of knowledge content of text segment keyword and domain body
Field theme matched, determine the field theme of knowledge content of text segment;By in knowledge content of text segment at least
One keyword is matched according to default rule with the model included by corresponding field theme, in determining and knowledge text
Hold the model of fragment match and the knowledge meta-instance of knowledge content of text segment;By at least one in knowledge content of text segment
A keyword is matched according to default rule with the attribute of corresponding model, determines Knowledge Element in knowledge content of text segment
The attribute of example;The attribute of knowledge meta-instance in incidence relation and knowledge content of text segment between binding model determines
Incidence relation between knowledge meta-instance;According to knowledge meta-instance, the knowledge in the multiple knowledge content of text segment
Incidence relation between the attribute of meta-instance and the knowledge meta-instance builds knowledge mapping.The present invention realizes automation structure
Knowledge mapping is built, the construction cost of knowledge mapping is reduced, improves the structure efficiency and accuracy rate of knowledge mapping.
Finally it should be noted that:The above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations;To the greatest extent
Present invention has been described in detail with reference to the aforementioned embodiments for pipe, it will be understood by those of ordinary skill in the art that:Its according to
So can with technical scheme described in the above embodiments is modified, either to which part or all technical features into
Row equivalent replacement;And these modifications or replacements, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme.
Claims (10)
1. a kind of knowledge mapping construction method, which is characterized in that including:
The corpus information at least one field of knowledge mapping to be built is obtained, the corpus information includes:Multiple knowledge texts
Contents fragment;
Participle and part-of-speech tagging are carried out to the knowledge content of text segment, obtain the pass in the knowledge content of text segment
Keyword;
The keyword is matched according to default rule with domain body, is obtained in the knowledge content of text segment
Knowledge meta-instance, the knowledge meta-instance attribute and the knowledge meta-instance between incidence relation;The domain body
Including:Field theme, at least one model that field theme includes, the incidence relation between the attribute and model of model;Institute
It includes at least one knowledge meta-instance to state model;
According to the attribute of knowledge meta-instance, the knowledge meta-instance in the multiple knowledge content of text segment and described know
The incidence relation known between meta-instance builds knowledge mapping.
2. according to the method described in claim 1, it is characterized in that, it is described by the keyword according to default rule and field
Ontology is matched, obtain knowledge meta-instance in the knowledge content of text segment, the knowledge meta-instance attribute and
Incidence relation between the knowledge meta-instance, including:
By the field theme progress at least one of knowledge content of text segment keyword and the domain body
Match, determines the field theme of the knowledge content of text segment;
By at least one of knowledge content of text segment keyword according to default rule and corresponding field theme institute
Including model matched, determine and the model of the knowledge content of text fragment match and the knowledge content of text piece
The knowledge meta-instance of section;
By at least one of knowledge content of text segment keyword according to the attribute of default rule and corresponding model
It is matched, determines the attribute of knowledge meta-instance in the knowledge content of text segment;
The attribute of knowledge meta-instance, determines knowledge in incidence relation and the knowledge content of text segment between binding model
Incidence relation between meta-instance.
3. according to the method described in claim 2, it is characterized in that, it is described by the knowledge content of text segment at least one
A keyword is matched according to default rule with the model included by corresponding field theme, is determined and the knowledge text
The knowledge meta-instance of the matched model of contents fragment and the knowledge content of text segment, including:
Successively by least one of knowledge content of text segment keyword and the model included by corresponding field theme
It is matched, determines the confidence level of each model;
The model with the knowledge content of text fragment match is determined according to the confidence level of each model;
At least one of knowledge content of text segment keyword is matched with the content of corresponding model, determines institute
State the knowledge meta-instance of knowledge content of text segment.
4. according to the method described in claim 3, it is characterized in that, it is described by the knowledge content of text segment at least one
A keyword is matched successively with the model included by corresponding field theme, determines the confidence level of each model, including:
Multiple models included by the theme of field for the knowledge content of text segment, by the knowledge content of text segment
At least one of keyword matched successively with multiple contents of the model, determine the weight of the model;
According to the weight of the multiple model, the confidence level of the multiple model is determined.
5. according to the method described in claim 1, it is characterized in that, the attribute of the knowledge meta-instance includes:Common property and
Relating attribute;
The common property includes:Primary attribute;
The knowledge meta-instance according in the multiple knowledge content of text segment, the attribute of the knowledge meta-instance and institute
After stating the incidence relation structure knowledge mapping between knowledge meta-instance, further include:
Obtain the relevant information of the corresponding knowledge content of text segment of knowledge meta-instance;The relevant information includes:Knowledge text
The content and source-information of contents fragment;
The relevant information of the knowledge content of text segment is determined as to the primary attribute value of knowledge meta-instance.
6. according to the method described in claim 5, it is characterized in that, described according in the multiple knowledge content of text segment
Knowledge meta-instance, the knowledge meta-instance attribute and the knowledge meta-instance between incidence relation structure knowledge mapping it
Afterwards, further include:
Multiple association knowledge meta-instances that there is the first incidence relation with the knowledge meta-instance are obtained from the knowledge mapping;
The relevant information of the corresponding knowledge content of text segment of the association knowledge meta-instance is obtained from the knowledge mapping;
The relevant information of the corresponding knowledge content of text segment of the association knowledge meta-instance is determined as the of knowledge meta-instance
The value of one relating attribute.
7. according to the method described in claim 1, it is characterized in that, described according in the multiple knowledge content of text segment
Knowledge meta-instance, the knowledge meta-instance attribute and the knowledge meta-instance between incidence relation structure knowledge mapping it
Afterwards, further include:
The knowledge mapping is shown.
8. according to the method described in claim 1, it is characterized in that, described according in the multiple knowledge content of text segment
Knowledge meta-instance, the knowledge meta-instance attribute and the knowledge meta-instance between incidence relation structure knowledge mapping it
Afterwards, further include:
The inquiry instruction of user is received, knowledge meta-instance is carried in the inquiry instruction;
According to the Knowledge Element Query By Example knowledge mapping, the attribute of the knowledge meta-instance, the knowledge meta-instance are obtained
Incidence relation, the corresponding knowledge content of text segment of the knowledge meta-instance and with the knowledge meta-instance have incidence relation
The corresponding knowledge content of text segment of association knowledge meta-instance;
By the attribute of the knowledge meta-instance, the incidence relation of the knowledge meta-instance, the corresponding knowledge of the knowledge meta-instance
Content of text segment and have in the corresponding knowledge text of the association knowledge meta-instance of incidence relation with the knowledge meta-instance
Hold segment to be shown.
9. a kind of knowledge mapping builds system, which is characterized in that including:
Acquisition module, the corpus information at least one field for obtaining knowledge mapping to be built, the corpus information include:
Multiple knowledge content of text segments;
Participle and part-of-speech tagging module are obtained for carrying out participle and part-of-speech tagging to the knowledge content of text segment
Keyword in the knowledge content of text segment;
Matching module obtains the knowledge text for matching the keyword with domain body according to default rule
Association between the attribute and the knowledge meta-instance of knowledge meta-instance, the knowledge meta-instance in this contents fragment is closed
System;The domain body includes:Field theme, at least one model that field theme includes, the attribute and model of model it
Between incidence relation;The model includes at least one knowledge meta-instance;
Module is built, for according to knowledge meta-instance, the knowledge meta-instance in the multiple knowledge content of text segment
Incidence relation between attribute and the knowledge meta-instance builds knowledge mapping.
10. system according to claim 9, which is characterized in that the matching module includes:
First matching unit, for will be at least one of knowledge content of text segment keyword and the domain body
Field theme matched, determine the field theme of the knowledge content of text segment;
Second matching unit, for by least one of knowledge content of text segment keyword according to default rule with
Model included by corresponding field theme is matched, and is determined and the model of the knowledge content of text fragment match and institute
State the knowledge meta-instance of knowledge content of text segment;
Third matching unit, for by least one of knowledge content of text segment keyword according to default rule with
The attribute of corresponding model is matched, and determines the attribute of knowledge meta-instance in the knowledge content of text segment;
Determination unit is used for knowledge meta-instance in the incidence relation between binding model and the knowledge content of text segment
Attribute determines the incidence relation between knowledge meta-instance.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710220269.4A CN108694177B (en) | 2017-04-06 | 2017-04-06 | Knowledge graph construction method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710220269.4A CN108694177B (en) | 2017-04-06 | 2017-04-06 | Knowledge graph construction method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108694177A true CN108694177A (en) | 2018-10-23 |
| CN108694177B CN108694177B (en) | 2022-02-18 |
Family
ID=63842733
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710220269.4A Active CN108694177B (en) | 2017-04-06 | 2017-04-06 | Knowledge graph construction method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108694177B (en) |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109815340A (en) * | 2019-01-17 | 2019-05-28 | 云南师范大学 | A kind of construction method of national culture information resources knowledge mapping |
| CN109947952A (en) * | 2019-03-20 | 2019-06-28 | 武汉市软迅科技有限公司 | Search method, device, equipment and storage medium based on english knowledge map |
| CN109977291A (en) * | 2019-03-20 | 2019-07-05 | 武汉市软迅科技有限公司 | Search method, device, equipment and storage medium based on physical knowledge map |
| CN109977419A (en) * | 2019-04-09 | 2019-07-05 | 福建奇点时空数字科技有限公司 | A kind of knowledge mapping building system |
| CN110119814A (en) * | 2019-04-29 | 2019-08-13 | 武汉开目信息技术股份有限公司 | Knowledge rule modeling and inference method based on object relationship chain |
| CN110489561A (en) * | 2019-07-12 | 2019-11-22 | 平安科技（深圳）有限公司 | Knowledge mapping construction method, device, computer equipment and storage medium |
| CN110543574A (en) * | 2019-08-30 | 2019-12-06 | 北京百度网讯科技有限公司 | knowledge graph construction method, device, equipment and medium |
| CN110674360A (en) * | 2019-09-27 | 2020-01-10 | 厦门美亚亿安信息科技有限公司 | Method and system for constructing data association graph and tracing data |
| CN110929037A (en) * | 2019-09-29 | 2020-03-27 | 珠海格力电器股份有限公司 | Knowledge graph construction method and device, terminal and storage medium |
| CN110955764A (en) * | 2019-11-19 | 2020-04-03 | 百度在线网络技术（北京）有限公司 | Scene knowledge graph generation method, man-machine conversation method and related equipment |
| CN111104505A (en) * | 2019-12-30 | 2020-05-05 | 浙江阿尔法人力资源有限公司 | Information prompting method, device, equipment and storage medium |
| CN111159431A (en) * | 2019-12-30 | 2020-05-15 | 深圳Tcl新技术有限公司 | Knowledge graph-based information visualization method, device, equipment and storage medium |
| CN113051374A (en) * | 2021-06-02 | 2021-06-29 | 北京沃丰时代数据科技有限公司 | Text matching optimization method and device |
| CN113569051A (en) * | 2020-04-29 | 2021-10-29 | 北京金山数字娱乐科技有限公司 | Knowledge graph construction method and device |
| CN114579765A (en) * | 2022-03-07 | 2022-06-03 | 四川大学 | Network shooting range weapon base construction method based on open source information analysis |
| CN117408338A (en) * | 2023-12-14 | 2024-01-16 | 神州医疗科技股份有限公司 | Method and system for constructing knowledge graph of traditional Chinese medicine decoction pieces based on Chinese pharmacopoeia |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070208693A1 (en) * | 2006-03-03 | 2007-09-06 | Walter Chang | System and method of efficiently representing and searching directed acyclic graph structures in databases |
| US20130096944A1 (en) * | 2011-10-13 | 2013-04-18 | The Board of Trustees of the Leland Stanford, Junior, University | Method and System for Ontology Based Analytics |
| CN103488724A (en) * | 2013-09-16 | 2014-01-01 | 复旦大学 | Book-oriented reading field knowledge map construction method |
| CN103699663A (en) * | 2013-12-27 | 2014-04-02 | 中国科学院自动化研究所 | Hot event mining method based on large-scale knowledge base |
| CN103886046A (en) * | 2014-03-11 | 2014-06-25 | 中国信息安全测评中心 | Automatic semanteme extraction method for Web data exchange |
| CN105893551A (en) * | 2016-03-31 | 2016-08-24 | 上海智臻智能网络科技股份有限公司 | Method and device for processing data and knowledge graph |
| CN106095858A (en) * | 2016-06-02 | 2016-11-09 | 海信集团有限公司 | A kind of audio video searching method, device and terminal |
| CN106250412A (en) * | 2016-07-22 | 2016-12-21 | 浙江大学 | The knowledge mapping construction method merged based on many source entities |
-
2017
- 2017-04-06 CN CN201710220269.4A patent/CN108694177B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070208693A1 (en) * | 2006-03-03 | 2007-09-06 | Walter Chang | System and method of efficiently representing and searching directed acyclic graph structures in databases |
| US20130096944A1 (en) * | 2011-10-13 | 2013-04-18 | The Board of Trustees of the Leland Stanford, Junior, University | Method and System for Ontology Based Analytics |
| CN103488724A (en) * | 2013-09-16 | 2014-01-01 | 复旦大学 | Book-oriented reading field knowledge map construction method |
| CN103699663A (en) * | 2013-12-27 | 2014-04-02 | 中国科学院自动化研究所 | Hot event mining method based on large-scale knowledge base |
| CN103886046A (en) * | 2014-03-11 | 2014-06-25 | 中国信息安全测评中心 | Automatic semanteme extraction method for Web data exchange |
| CN105893551A (en) * | 2016-03-31 | 2016-08-24 | 上海智臻智能网络科技股份有限公司 | Method and device for processing data and knowledge graph |
| CN106095858A (en) * | 2016-06-02 | 2016-11-09 | 海信集团有限公司 | A kind of audio video searching method, device and terminal |
| CN106250412A (en) * | 2016-07-22 | 2016-12-21 | 浙江大学 | The knowledge mapping construction method merged based on many source entities |
Non-Patent Citations (9)
| Title |
|---|
| JIANDONG LI 等: "A Rule-Based Knowledge Discovery Engine Embedded Semantic Graph Knowledge Repository for Retail Business", 《ADVANCED CLOUD AND BIG DATA》 * |
| 吴运兵 等: "知识图谱学习和推理研究进展", 《小型微型计算机系统》 * |
| 周宇 等: "面向关联数据的高校机构知识库构建方法研究", 《图书情报工作》 * |
| 张德政 等: "基于本体的中医知识图谱构建", 《情报工程》 * |
| 朱游娟: "初中数学问题的全知识图谱设计与实现", 《中国优秀硕士学位论文全文数据库 社会科学Ⅱ辑》 * |
| 熊晶 等: "甲骨文知识图谱构建中的实体关系发现研究", 《计算机工程与科学》 * |
| 王巍巍 等: "双语影视知识图谱的构建研究", 《北京大学学报(自然科学版》 * |
| 胡芳槐: "基于多种数据源的中文知识图谱构建方法研究", 《中国博士学位论文全文数据库 信息科技辑》 * |
| 黄小燕: "基于潜在语义关系的更年期综合症知识图库的构建及其应用研究", 《中国优秀硕士学位论文全文数据库 医药卫生科技辑》 * |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109815340A (en) * | 2019-01-17 | 2019-05-28 | 云南师范大学 | A kind of construction method of national culture information resources knowledge mapping |
| CN109947952A (en) * | 2019-03-20 | 2019-06-28 | 武汉市软迅科技有限公司 | Search method, device, equipment and storage medium based on english knowledge map |
| CN109977291A (en) * | 2019-03-20 | 2019-07-05 | 武汉市软迅科技有限公司 | Search method, device, equipment and storage medium based on physical knowledge map |
| CN109977419A (en) * | 2019-04-09 | 2019-07-05 | 福建奇点时空数字科技有限公司 | A kind of knowledge mapping building system |
| CN109977419B (en) * | 2019-04-09 | 2023-04-07 | 厦门美域中央信息科技有限公司 | Knowledge graph construction system |
| CN110119814B (en) * | 2019-04-29 | 2022-04-29 | 武汉开目信息技术股份有限公司 | Knowledge rule modeling and reasoning method based on object relation chain |
| CN110119814A (en) * | 2019-04-29 | 2019-08-13 | 武汉开目信息技术股份有限公司 | Knowledge rule modeling and inference method based on object relationship chain |
| CN110489561A (en) * | 2019-07-12 | 2019-11-22 | 平安科技（深圳）有限公司 | Knowledge mapping construction method, device, computer equipment and storage medium |
| CN110543574B (en) * | 2019-08-30 | 2022-05-17 | 北京百度网讯科技有限公司 | Knowledge graph construction method, device, equipment and medium |
| CN110543574A (en) * | 2019-08-30 | 2019-12-06 | 北京百度网讯科技有限公司 | knowledge graph construction method, device, equipment and medium |
| CN110674360A (en) * | 2019-09-27 | 2020-01-10 | 厦门美亚亿安信息科技有限公司 | Method and system for constructing data association graph and tracing data |
| CN110929037A (en) * | 2019-09-29 | 2020-03-27 | 珠海格力电器股份有限公司 | Knowledge graph construction method and device, terminal and storage medium |
| CN110955764A (en) * | 2019-11-19 | 2020-04-03 | 百度在线网络技术（北京）有限公司 | Scene knowledge graph generation method, man-machine conversation method and related equipment |
| CN111104505A (en) * | 2019-12-30 | 2020-05-05 | 浙江阿尔法人力资源有限公司 | Information prompting method, device, equipment and storage medium |
| CN111159431A (en) * | 2019-12-30 | 2020-05-15 | 深圳Tcl新技术有限公司 | Knowledge graph-based information visualization method, device, equipment and storage medium |
| CN111104505B (en) * | 2019-12-30 | 2023-08-25 | 浙江阿尔法人力资源有限公司 | Information prompting method, device, equipment and storage medium |
| CN113569051A (en) * | 2020-04-29 | 2021-10-29 | 北京金山数字娱乐科技有限公司 | Knowledge graph construction method and device |
| CN113051374A (en) * | 2021-06-02 | 2021-06-29 | 北京沃丰时代数据科技有限公司 | Text matching optimization method and device |
| CN114579765B (en) * | 2022-03-07 | 2023-08-15 | 四川大学 | Network shooting range weapon library construction method based on open source information analysis |
| CN114579765A (en) * | 2022-03-07 | 2022-06-03 | 四川大学 | Network shooting range weapon base construction method based on open source information analysis |
| CN117408338A (en) * | 2023-12-14 | 2024-01-16 | 神州医疗科技股份有限公司 | Method and system for constructing knowledge graph of traditional Chinese medicine decoction pieces based on Chinese pharmacopoeia |
| CN117408338B (en) * | 2023-12-14 | 2024-03-12 | 神州医疗科技股份有限公司 | Method and system for constructing knowledge graph of traditional Chinese medicine decoction pieces based on Chinese pharmacopoeia |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108694177B (en) | 2022-02-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108694177A (en) | Knowledge mapping construction method and system | |
| Berman | Principles of big data: preparing, sharing, and analyzing complex information | |
| Pedersen et al. | DanNet: the challenge of compiling a wordnet for Danish by reusing a monolingual dictionary | |
| Ulrich et al. | Metadata repository for improved data sharing and reuse based on HL7 FHIR. | |
| US20140280314A1 (en) | Dimensional Articulation and Cognium Organization for Information Retrieval Systems | |
| JP2008276550A (en) | Document retrieval system and document retrieval method | |
| CA2747669A1 (en) | Method and system for validation of claims against policy with contextualized semantic interoperability | |
| KR101799448B1 (en) | Providing system for classification of diseases using questions and answer | |
| CN103544321A (en) | Data processing method and device for micro-blog emotion information | |
| CN110189802A (en) | Biaxial stress structure cohort study information system based on index storage model | |
| TWI735380B (en) | Natural language processing method and computing apparatus thereof | |
| CN113409907A (en) | Intelligent pre-inquiry method and system based on Internet hospital | |
| CN109299238B (en) | Data query method and device | |
| Gašpar et al. | Measuring Terminology Consistency in Translated Corpora: Implementation of the Herfindahl-Hirshman Index | |
| Burrows | The History and Provenance of Manuscripts in the Collection of Sir Thomas Phillipps: New Approaches to Digital Representation | |
| Richesson et al. | Heterogeneous but “standard” coding systems for adverse events: Issues in achieving interoperability between apples and oranges | |
| CN112349367A (en) | Method and device for generating simulation medical record, electronic equipment and storage medium | |
| Houssein et al. | Semantic protocol and resource description framework query language: a comprehensive review | |
| Afzaal et al. | Syntactic complexity in translated eHealth discourse of COVID-19: A comparable parallel corpus approach | |
| Darmody et al. | An introduction to computerised analysis of qualitative data | |
| TWI281111B (en) | System and method for identifying corresponding translation | |
| Haider et al. | Library Cataloging, Classification, and Metadata Research: A Bibliography of Doctoral Dissertations—A Supplement, 2022-2023 | |
| Rodríguez-Ferreiro et al. | Semantic domain and grammatical class effects in the picture–word interference paradigm | |
| Picht | The seven pillars of terminology | |
| Grogan et al. | Nursing documentation in digital solutions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230609 Address after: 3007, Hengqin international financial center building, No. 58, Huajin street, Hengqin new area, Zhuhai, Guangdong 519031 Patentee after: New founder holdings development Co.,Ltd. Patentee after: BEIJING FOUNDER ELECTRONICS Co.,Ltd. Address before: 100871, Beijing, Haidian District, Cheng Fu Road, No. 298, Zhongguancun Fangzheng building, 9 floor Patentee before: PEKING UNIVERSITY FOUNDER GROUP Co.,Ltd. Patentee before: BEIJING FOUNDER ELECTRONICS Co.,Ltd. |