CN110347843A - A kind of Chinese tour field Knowledge Service Platform construction method of knowledge based map - Google Patents
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Abstract
A kind of Chinese tour field Knowledge Service Platform construction method of knowledge based map, including obtaining structuring tourism knowledge from existing Chinese encyclopaedia class knowledge base, knowledge fusion, crawl tour site page data, knowledge completion is carried out to entity Infobox attribute by Custom Attributes matching rule, tour field ontology is constructed using Stamford ontology modeling tool Prot é g é, it combines the tourism ontology of building that data are switched to RDF triplet format using D2RQ and obtains tour field knowledge mapping, the Neo4j chart database store tasks for knowledge base of travelling, wherein knowledge fusion task includes completing entity using the semantic similarity between improved deep learning Knowledge Representation Model BERT computational entity to be aligned, attribute fusion is carried out based on principle and statistical method, using majority voting algorithm It carries out triple and merges subtask.The present invention facilitates tourist to obtain one-stop comprehensive sex service.
Description
Technical field
The invention belongs to computer information processing fields, and in particular to a kind of Chinese tour field of knowledge based map is known
Know service platform construction method, this method has merged cognition calculating, knowledge representation and reasoning, information retrieval and extraction, natural language
A variety of subjects such as speech processing and semantic web, data mining and machine learning, it is semantic by being added for internet mass tourism data
(knowledge) makes data generate wisdom, completes again to knowledge, the transition process of most Zhongdao Intelligent Application Platform from data to information,
To improve the Tourist Experience of user, realize from information service to knowledge services, propagate the targets such as tourist culture.
Background technique
Knowledge mapping describes concept in objective world, entity and its relationship in the form of structuring, by the information of internet
It is expressed as the form closer to the human cognitive world, one kind is provided and preferably organizes, manages and understand internet mass information
Ability.
Ontology is the representation of knowledge basis of knowledge mapping, can be O={ C, H, P, A, I } with formalization representation, C is concept set
It closes, as transactional concept and event genus, H is the context relation set of concept, also referred to as Taxonomy knowledge, and P is to belong to
Property set, describe concept possessed by feature, A is regular collection, describe domain-planning, I is example collection, for describing reality
Body-attribute-value.With using deep learning as representative indicate study development, the table towards entity in knowledge mapping and relationship
Dendrography habit also achieves important progress.Entity and relationship are expressed as dense low-dimensional vector by representation of knowledge study, are realized
The distributed of entity and relationship is indicated, efficiently entity and relationship can be calculated, alleviate that knowledge is sparse, facilitates reality
Existing knowledge fusion, becomes the important method of knowledge mapping knowledge fusion and knowledge completion.Knowledge mapping is divided into world knowledge figure
Spectrum and two class of domain knowledge map, world knowledge map include the WordNet for describing English glossary layer semantic relation, to construct this
The form of body carries out the DBPedia of tissue to knowledge entry, and concept hierarchy and the wikipedia for merging WordNet are largely real
The YAGO of volume data, the Freebase etc. that use groups intelligent method is established, the research of Universal Chinese character knowledge mapping can trace back to
The HowNet project constructed using human-edited's mode, industry have OpenKG.CN, and Baidu is intimate, and search dog is known cube etc., academic
Boundary includes that Tsinghua University, Shanghai Communications University and Fudan University are established using Baidupedia, interaction encyclopaedia and Chinese wikipedia
Extensive knowledge mapping XLore, Zhishi.me and CN-DBpedia.Google issues knowledge graph spectral term in May, 2012
Mesh, and next-generation intelligent search engine is constructed based on this, indicate extensive knowledge in internet semantic search
Successful application.
Compared with world knowledge map, the building research of domain knowledge map is relatively fewer, and domain knowledge map is called row
Industry knowledge mapping or vertical knowledge mapping can regard the domain knowledge based on semantic technology as towards a certain specific area
There is stringent and abundant data pattern in library, so depth, knowledge to the domain knowledge because it is constructed based on industry data
Accuracy has higher requirement.Britain's British Museum is semantic by combining semantic technology to carry out collection Various types of data resource
Tissue provides knowledge services by modes such as semanteme refinement, multimedia resource marks；British Broadcasting Corporation BBC [Kobilarov
Et al, 2009] ontologies are defined in plates such as its music, sport wild animals, converts news in machine readable letter
Breath source carries out Content Management and automatically generates with report.The utilization of domestic fields knowledge mapping technology has Shanghai Library to use for reference the U.S.
Congress number frame BibFrame [Kroeger et al, 2013] beats the resource constructions knowledge hierarchy such as family tree, famous person, manuscript
It makes family tree service platform and provides ancient books evidence-based service for researchers；The Chinese Academy of Agricultural Sciences then focuses on rice subdivision field, integration
The industry resources such as paper, patent, news construct rice knowledge mapping, provide industry professional knowledge service for researcher
Platform.
China's tourist industry informatization has had more than 30 years history, but is specific to the Chinese knowledge of tour field
Map also lacks very much, seriously hinders the development and succession of China's tourist culture.And existing Chinese domain knowledge map exists
Data pattern towards different field is different, and application demand is also different, instructs without a set of general standards and specifications
The problems such as building.
In conclusion there is an urgent need to construct knowledge based map Chinese tourism Knowledge Service Platform come tissue, management and
Using the magnanimity such as food, place, row, sightseeing, shopping and entertainment tourism knowledge data, tourist is facilitated to obtain one-stop comprehensive sex service, while also more
Tourist culture is propagated well, and tourist industry is finally made to move towards tourism knowledge services from travel information service.
Summary of the invention
It is an object of the invention to be directed to above-mentioned the problems of the prior art, a kind of Chinese trip of knowledge based map is provided
Domain knowledge service platform construction method is swum, structuring tourism knowledge, knowledge are obtained from existing Chinese encyclopaedia class knowledge base
It merges, crawl tour site page data, knowledge benefit is carried out to entity Infobox attribute by Custom Attributes matching rule
Entirely, combine the tourism ontology of building will using Stamford ontology modeling tool Prot é g é building tour field ontology, using D2RQ
Data switch to RDF triplet format and obtain the Neo4j chart database store tasks of tour field knowledge mapping, knowledge base of travelling.
To achieve the goals above, the technical solution adopted by the present invention the following steps are included:
S1, knowledge acquisition: structuring tourism knowledge is obtained from existing Chinese encyclopaedia class knowledge base；
S2, knowledge fusion: first complete using the semantic similarity between deep learning Knowledge Representation Model BERT computational entity
It is aligned at entity, then attribute fusion is carried out based on principle and statistical method, triple is finally carried out using majority voting algorithm and is melted
It closes；
S3, tour site page data is crawled, knowledge benefit is carried out to entity Infobox attribute by attributes match rule
Entirely；
S4, ontological construction: tour field ontology is constructed using Stamford ontology modeling tool Prot é g é；
S5, it data is switched into RDF triplet format using D2RQ combination tour field ontology obtains tour field knowledge graph
Spectrum；
S6, data storage: by the storage of tour field knowledge mapping into Neo4j chart database；
S7, building tourism Knowledge Service Platform.
The step S1 is completed especially by following procedure: being obtained under the classification of existing Chinese encyclopaedia class knowledge base
Entity structure knowledge, the Chinese encyclopaedia class knowledge base include Zhishi.me, CN-DBpedia, and classification includes " trip
Trip ", " sightseeing ", " playing ", entity structure knowledge includes sight spot, scenic spot, historic site, city, personage, historical relic, structural knowledge
Triple data in the middle include entity name, entity brief introduction, entity Infobox attribute, entity picture；
It is final define tour field entity attributes include Chinese, the open hour, foreign language title, ticket price,
Reason position, age, literary safeguarding grades not, duration of suggesting playing, be suitable for play season, affiliated city, value, name, date of birth, go
Generation time, nationality, nickname, achievement, works, age, nationality and native place.
The specific implementation procedure of three parts is as follows in the step S2:
1) entity alignment is completed using the semantic similarity between deep learning Knowledge Representation Model BERT computational entity
Step include: firstly, using Google issue BERT Chinese language model, by its fine-tuning finely tune the stage set
Set the layer second from the bottom acquisition entity term vector that parameter obtains output layer；Then, it is calculated according to the entity term vector of acquisition different
COS distance between entity, i.e. semantic similarity；Finally, reaching entity alignment according to semantic similarity by setting threshold value
Purpose；
2) two methods can be selected by carrying out attribute fusion based on principle and statistical method, and a kind of method is from existing Chinese
Tourist entity Infobox attribute is obtained in encyclopaedic knowledge library, is known by using Python redaction rule and statistics difference
Know the different names expression of the same attribute in library, it is final to determine entity Infobox property content；Another method is by entity
Triple relationship is regarded as with attribute, is classified as Relation extraction problem, and attribute is carried out by support vector machines, text mining algorithm and is melted
It closes；
3) when carrying out triple fusion using majority voting algorithm, after entity alignment, attribute fusion, to entity triple
In the data comprising same entity and attribute carry out triple fusion, each attribute is determined by majority voting algorithm unique
Attribute value.
The step S3 is completed by following procedure: crawling the tour site page and Baidupedia, interaction encyclopaedia, Chinese
Wikipedia data carry out knowledge completion to the part that attribute knowledge in entity lacks by attributes match rule.
The step S4 is completed by following procedure: being concluded to the entity in tour field data, attribute, relationship
Summarize, determine the related notion of tour field and the hierarchical structure of classification, define entity attribute and value range, and according to
Upper knowledge carries out modeling and summarizes tourism map schema model out, big using top-down body constructing method combination Stamford
Body constructing method is constructed using ontology modeling tool Prot é g é and completes tour field ontology.
The step S5 is completed by following procedure: the R2RML standard formulated according to the RDB2RDF work group of W3C,
The data in database are mapped on customized tour field ontology by editing and being arranged mapping ruler, use D2RQ work
Tourism data in relevant database, is converted into the data of RDF format, obtains tour field knowledge mapping by tool.
It combines the tour field ontology of building that data are switched to RDF triplet format using D2RQ, obtains tour field and know
Know map to realize by following procedure: firstly, the structuring that will acquire triple form is traveled, knowledge is corresponding by designing
Database table structure is stored into relevant database；Secondly, operation order generates the mapping text of default using D2RQ tool
Part modifies mapped file according to the tourism ontology of definition and completes the tour field ontology phase for database table being mapped to building completion
In the class answered；Finally, data are switched to RDF format to obtain tour field knowledge mapping by operation order using D2RQ tool.
The step S6 is completed by following procedure: being imported Neo4j chart database by downloading RDF and is extended jar packet, repairs
Change Neo4j configuration file and creation namespace prefix, is imported into the tour field knowledge mapping of RDF format using order line
Neo4j chart database is completed tour field knowledge mapping storage to the process in Neo4j chart database.
The step S7 tour field knowledge mapping store complete on the basis of, from the background using Java programming language and
Tourism knowledge is built using the visualization component of JSP dynamic web page technique and D3.js data-driven in SpringMVC framework, foreground
Service platform.
Compared with prior art, by the present invention in that with improved deep learning Knowledge Representation Model BERT computational entity
Between semantic similarity complete entity alignment, compare test with other Knowledge Representation Models, entity alignment accuracy rate is most
It is high.By carrying out induction and conclusion to the entity (concept) in tour field data, attribute, relationship, it is determined that the phase of tour field
The hierarchical structure for closing concept and classification defines entity attribute and value range, and carries out modeling according to the above knowledge and summarize
Tourism map schema model out, new classification relationship is defined in conjunction with tourism industry feature, using top-down ontology structure
The ontological construction " seven footworks " of construction method combination Stanford University is constructed using ontology modeling tool Prot é g é and completes tourism neck
Domain ontological construction is to current domain body building shortage system, general, engineering implementation method, codes and standards
Once benefit our pursuits.The present invention uses tour field knowledge base RDF triple Neo4j chart database storage scheme, Ke Yichong
The support of the more perfect figure query language and algorithm provided using primary knowledge store medium (Neo4j) itself is provided.It is based on
The tourism Knowledge Service Platform of this method building can be calculated in conjunction with figure excavation and knowledge reasoning is energized, and travel industry takes from information
Knowledge services are moved towards in business.
Detailed description of the invention
The flow diagram of Fig. 1 construction method of the present invention；
Fig. 2 knowledge fusion stage entity of the present invention is aligned implementation process schematic diagram；
Fig. 3 present invention tourism knowledge mapping knowledge Modeling schema model schematic.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and embodiments.
Referring to Fig. 1, the present invention is based on the Chinese tour field Knowledge Service Platform construction method of knowledge mapping, including it is following
Step:
S1: structuring tourism knowledge knowledge acquisition: is obtained from existing Chinese encyclopaedic knowledge class knowledge base；
From existing Chinese encyclopaedia class knowledge base Zhishi.me, CN-DBpedia, (its official website provides free RDF triple lattice
The downloading of formula data, data include Baidupedia, interaction encyclopaedia and Chinese wikipedia knowledge) " tourism ", " sightseeing ", " playing "
Obtaining entity structures knowledge, the structural knowledges such as sight spot, scenic spot, historic site, city, personage, historical relic under equal classification includes: entity
The triples data such as title, entity brief introduction (Abstracts), entity Infobox attribute, entity picture.
It is final define tour field entity attributes include: Chinese, the open hour, foreign language title, ticket price,
Reason position, age, literary safeguarding grades not, duration of suggesting playing, be suitable for play season, affiliated city, value, name, date of birth, go
Generation time, nationality, nickname, achievement, works, age, nationality, native place.
S2: knowledge fusion: knowledge fusion process includes three parts, is using improved deep learning knowledge table respectively
Semantic similarity between representation model BERT computational entity completes entity alignment, carries out attribute using based on principle and statistical method
It merges and carries out triple fusions based on most (Majority Voting) algorithms of voting using a kind of.
1. being completed using the semantic similarity between improved deep learning Knowledge Representation Model BERT computational entity real
Body alignment；
Entity alignment realization process of the present invention is as shown in Fig. 2, firstly, the entity got during S1 is arranged to text
Data set is constituted in document, and the BERT Chinese language model of Google publication is used under Linux platform Tensorflow environment
It is held as service (Server), obtains the layer second from the bottom of output layer in its fine tuning (fine-tuning) stage setting parameter,
The client (Client) of windows platform, which holds, obtains entity term vector；Secondly, calculating different realities according to the entity term vector of acquisition
COS distance between body, i.e. semantic similarity；Finally, achieving the purpose that entity is aligned according to semantic similarity.
2. carrying out attribute fusion based on principle and statistical method；
Tourist entity Infobox attribute is obtained from existing Chinese encyclopaedic knowledge library, is write by using Python
Same attribute in regular (regular expression) knowledge base different with statistics different names expression (such as: the date of birth with out
The raw time), it is final to determine entity Infobox property content.Such as: have for " geographical location " the attribute value description at " temple Ling Gu "
At about 1.5 kms, at 1.5 kilometers of the Nanjing Zhongshan Tomb east and Nanjing to the east of the Zhongshan Tomb, according to accuracy principle
With most of principles select second as attribute value.
3. carrying out triple fusion based on most ballot (Majority Voting) algorithms using a kind of；
Be aligned in above-mentioned entity, after attribute fusion, to include in entity triple same entity and attribute data into
The fusion of row triple determines unique attribute value to each attribute by majority ballot (Majority Voting) algorithm.Example
Such as: the attribute value of " building age " attribute in " Xi'an clock tower " entity is described in Baidupedia, interaction encyclopaedia, Chinese Wiki
In encyclopaedia be respectively bright Hong Wushi 7 years (1384), Ming Hongwu 17 years (1384), it is bright, according to majority voting algorithm, I
Finally determine unique triple data (Xi'an clock tower, build the age, Ming Hongwu 17 years (1384)).
S3: crawling tour site page data, is known by Custom Attributes matching rule entity Infobox attribute
Know completion.The tour site page and Baidupedia, interaction encyclopaedia, Chinese wikipedia text data are crawled, customized category is passed through
Property matching rule (regular expression) in entity attribute knowledge lack part carry out knowledge completion.Such as " to scenic spot
Reason position " attribute carry out completion when canonical matching template be " (be located at | be seated | be located at | position exists) [^, | ^.]+", to people
Canonical matching template when " nickname " attribute of object carries out completion be " (person | be commonly called as | original name | also known as | also name | also known as | pseudonym |
Assumed name) [^, | ^.]+".
S4: tour field ontology ontological construction: is constructed using Stamford ontology modeling tool Prot é g é
The present invention is by carrying out induction and conclusion to the entity (concept) in tour field data, attribute, relationship, it is determined that trip
The related notion in trip field and the hierarchical structure of classification, define entity attribute and value range, and according to the above knowledge into
Row modeling summarizes tourism map schema model out, then using top-down body constructing method combination Stanford University
Ontological construction " seven footworks " is constructed using ontology modeling tool Prot é g é and completes tour field ontology.Specific top layer is tourism,
Determine three big second level classifications: scenic spot, city, personage, second level class includes: to know, mode of transportation, scenic spot, drink food, shelter again now
Place, amusement, sightseeing, study；Attribute value type includes integer type (int), character string (string), date type (date) etc.；
Relationship between entity is in original four kinds of fundamental relations (part-of: part and whole relationship, kind-of: parent and son
Relationship, instance-of between class: relationship, attribute-of between class and example: the attribute of class, including object category
Property and data attribute) basis on, according to tour field ontology task need and specific feature define some other relationship, have
Body includes following situations:
1.birth-of: personage's date of birth is defined, can be used for age and the question and answer of reasoning personage；
2.time-of: duration of playing is suggested in definition, is tourist's one of problem the most deeply concerned；
3.specialties-of: it defines local featured delicious food and recommends, can be used for question and answer and diet semantic search；
4.accprice-of: defining lodging price, is equally tourist's one of problem the most deeply concerned.
Tourism map schema model of the invention is as shown in Figure 3, it is determined that tourist map composes the big second level classification of schema tri-:
Relationship between scenic spot, city, personage and three illustrates part attribute and attribute value datatype.
S5: it combines the tourism ontology of building that data are switched to RDF triplet format using D2RQ and obtains tourism knowledge mapping.
The acquisition of tour field knowledge mapping of the present invention is realized especially by following procedure:
According to the R2RML standard that the RDB2RDF work group of W3C formulates, by editing and being arranged mapping ruler data
Data in library are mapped on the tour field ontology of oneself definition.Table name in database corresponds to the concept in knowledge mapping,
Column name corresponds to attribute, and train value corresponds to attribute value, constrains corresponding relationship between table.Specifically used D2RQ tool, by relevant database
In tourism data be converted into the data of RDF format, to obtain tour field knowledge mapping.Wherein combined using D2RQ tool
The tourism ontology of building by data switch to RDF triplet format obtain tourism knowledge mapping by following procedure realization:
Firstly, the structuring tourism knowledge of (entity, attribute, the attribute value) triple form that will acquire passes through design pair
The database table structure answered is stored into relevant database；
Secondly, operation order generates the mapped file of default using D2RQ tool, reflected according to the modification of the tourism ontology of definition
File is penetrated to complete to be mapped to database table in the corresponding class of tour field ontology that building is completed；
Finally, data are switched to RDF format to obtain tour field knowledge mapping by operation order using D2RQ tool.
S6: by tourism knowledge base storage into Neo4j chart database.
Knowledge base of travelling in present invention storage is realized into Neo4j chart database especially by following procedure:
Neo4j chart database, which is imported, by downloading RDF extends jar packet, modification Neo4j configuration file and creation NameSpace
The tour field knowledge base of RDF format is imported into Neo4j chart database in Neo4j console operating instruction by prefix, and completing will
Knowledge base of travelling is stored to the process in Neo4j chart database.
S7: building tourism Knowledge Service Platform on the basis of knowledge base storage of travelling is completed.
The present invention building for Knowledge Service Platform of tourism is realized especially by following procedure:
Made from the background using Java programming language and SpringMVC framework, foreground on the basis of knowledge base storage of travelling is completed
Tourism Knowledge Service Platform is built with the visualization component of JSP dynamic web page technique and D3.js data-driven.
So far, a kind of Chinese tourism Knowledge Service Platform construction method of knowledge based map is fully completed.
By adding semantic (knowledge) for internet mass tourism data, so that data is generated wisdom, complete from data to letter
Breath arrives the transition process of knowledge, most Zhongdao Intelligent Application Platform again, realizes from information service to knowledge services, propagates tourist culture
Etc. targets.
It should be noted that a kind of Chinese tourism Knowledge Service Platform structure of knowledge based map provided by the above embodiment
Construction method, only just above-mentioned each functional steps are illustrated, and can according to need in practical application and carry out above-mentioned steps
Combination is rearranged to complete corresponding function, the purpose that details is introduced in specific embodiment is not the model for limiting claims
It encloses, and is to aid in and understands the method for the invention.Usual skill is all in spirit of the invention in technical field of the present invention
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (9)
1. a kind of Chinese tour field Knowledge Service Platform construction method of knowledge based map, which is characterized in that comprising steps of
S1, knowledge acquisition: structuring tourism knowledge is obtained from existing Chinese encyclopaedia class knowledge base；
S2, knowledge fusion: it is first completed using the semantic similarity between deep learning Knowledge Representation Model BERT computational entity real
Body alignment, then attribute fusion is carried out based on principle and statistical method, triple fusion is finally carried out using majority voting algorithm；
S3, tour site page data is crawled, knowledge completion is carried out to entity Infobox attribute by attributes match rule；
S4, ontological construction: tour field ontology is constructed using Stamford ontology modeling tool Prot é g é；
S5, it data is switched into RDF triplet format using D2RQ combination tour field ontology obtains tour field knowledge mapping；
S6, data storage: by the storage of tour field knowledge mapping into Neo4j chart database；
S7, building tourism Knowledge Service Platform.
2. the Chinese tour field Knowledge Service Platform construction method of knowledge based map according to claim 1, special
Sign is that the step S1 is completed especially by following procedure: obtaining under the classification of existing Chinese encyclopaedia class knowledge base
Entity structure knowledge, the Chinese encyclopaedia class knowledge base include Zhishi.me, CN-DBpedia, and classification includes " trip
Trip ", " sightseeing ", " playing ", entity structure knowledge includes sight spot, scenic spot, historic site, city, personage, historical relic, structural knowledge
Triple data in the middle include entity name, entity brief introduction, entity Infobox attribute, entity picture；
The final tour field entity attributes that define include Chinese, open hour, foreign language title, ticket price, geographical position
Set, the age, literary safeguarding grades not, duration of suggesting playing, be suitable for play season, affiliated city, value, name, date of birth, it is dead when
Between, nationality, nickname, achievement, works, age, nationality and native place.
3. the Chinese tour field Knowledge Service Platform construction method of knowledge based map according to claim 1, special
Sign is that the specific implementation procedure of three parts is as follows in the step S2:
1) the step of entity is aligned is completed using the semantic similarity between deep learning Knowledge Representation Model BERT computational entity
It include: firstly, the BERT Chinese language model issued using Google, is joined by finely tuning stage setting in its fine-tuning
The layer second from the bottom that number obtains output layer obtains entity term vector；Then, different entities are calculated according to the entity term vector of acquisition
Between COS distance, i.e. semantic similarity；Finally, reaching the mesh of entity alignment according to semantic similarity by setting threshold value
's；
2) two methods can be selected by carrying out attribute fusion based on principle and statistical method, and a kind of method is from existing Chinese encyclopaedia
Tourist entity Infobox attribute is obtained in knowledge base, by using Python redaction rule and the different knowledge bases of statistics
In same attribute different names expression, it is final to determine entity Infobox property content；Another method is by entity and category
Property regard triple relationship as, be classified as Relation extraction problem, pass through support vector machines, text mining algorithm carry out attribute fusion；
3) when carrying out triple fusion using majority voting algorithm, after entity alignment, attribute fusion, to being wrapped in entity triple
Triple fusion is carried out containing the data of same entity and attribute, unique attribute is determined to each attribute by majority voting algorithm
Value.
4. the Chinese tour field Knowledge Service Platform construction method of knowledge based map according to claim 1, special
Sign is that the step S3 is completed by following procedure: crawling the tour site page and Baidupedia, interaction encyclopaedia, Chinese
Wikipedia data carry out knowledge completion to the part that attribute knowledge in entity lacks by attributes match rule.
5. the Chinese tour field Knowledge Service Platform construction method of knowledge based map according to claim 1, special
Sign is that the step S4 is completed by following procedure: being concluded to the entity in tour field data, attribute, relationship
Summarize, determine the related notion of tour field and the hierarchical structure of classification, define entity attribute and value range, and according to
Upper knowledge carries out modeling and summarizes tourism map schema model out, big using top-down body constructing method combination Stamford
Body constructing method is constructed using ontology modeling tool Prot é g é and completes tour field ontology.
6. the Chinese tour field Knowledge Service Platform construction method of knowledge based map according to claim 1, special
Sign is that the step S5 is completed by following procedure: the R2RML standard formulated according to the RDB2RDF work group of W3C,
The data in database are mapped on customized tour field ontology by editing and being arranged mapping ruler, use D2RQ work
Tourism data in relevant database, is converted into the data of RDF format, obtains tour field knowledge mapping by tool.
7. the Chinese tour field Knowledge Service Platform construction method of knowledge based map according to claim 6, special
Sign is, combines the tour field ontology of building that data are switched to RDF triplet format using D2RQ, obtains tour field knowledge
Map is realized by following procedure: firstly, the structuring that will acquire triple form travels knowledge by designing corresponding number
According to database table structure storage into relevant database；Secondly, operation order generates the mapped file of default using D2RQ tool,
It is corresponding that the tour field ontology that database table is mapped to building completion by mapped file completion is modified according to the tourism ontology of definition
Class on；Finally, data are switched to RDF format to obtain tour field knowledge mapping by operation order using D2RQ tool.
8. the Chinese tour field Knowledge Service Platform construction method of knowledge based map according to claim 1, special
Sign is that the step S6 is completed by following procedure: importing Neo4j chart database by downloading RDF and extends jar packet, repairs
Change Neo4j configuration file and creation namespace prefix, is imported into the tour field knowledge mapping of RDF format using order line
Neo4j chart database is completed tour field knowledge mapping storage to the process in Neo4j chart database.
9. the Chinese tour field Knowledge Service Platform construction method of knowledge based map according to claim 1, special
Sign is that step S7 uses Java programming language and SpringMVC on the basis of tour field knowledge mapping stores completion from the background
Tourism Knowledge Service Platform is built using the visualization component of JSP dynamic web page technique and D3.js data-driven in framework, foreground.
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