CN109284394A - A method of Company Knowledge map is constructed from multi-source data integration visual angle - Google Patents
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Abstract
The invention discloses a kind of method from multi-source data integration visual angle building Company Knowledge map, the building of knowledge mapping contains data acquisition, knowledge fusion and knowledge processing storing process, and designs the application searching system based on Company Knowledge map.The beneficial effects of the invention are as follows realized from building domain body and Karma modeling multi-source heterogeneous data integration angle can knowledge mapping with rapid build towards enterprise field, the speed for improving domain knowledge map construction simultaneously saves the cost of knowledge mapping building.Business data on business data and internet that government department stores is efficiently integrated and uses by the Company Knowledge map of building, isolated back end is fused in unified knowledge base, provides the business information services platform of a close friend, hommization for user.
Description
Technical field
The invention belongs to technical field of data processing, are related to a kind of from multi-source data integration visual angle building Company Knowledge map
Method.
Background technique
Knowledge mapping related information in the way of figure, by the structure of knowledge, standardization.Knowledge mapping building needs to integrate
Utilize the representation of knowledge (Knowledge Representation, KR), natural language processing (Natural Language
Process, NLP), machine learning (Machine Learning, ML), the methods of database (Data Base, DB) and technology.
With the continuous development of internet the relevant technologies, people experienced traditional document centric Web1.0 epoch and with user
In the Web2.0 epoch centered on content, Web3.0 has been increasingly becoming the repository of growing various web resources at present.
The Web3.0 epoch, by building people and all intelligible knowledge network of machine, are made full use of using knowledge interconnection as main target
The mankind are serviced with the internet data for excavating magnanimity.But data scale is big, abundance, type are complicated, variation is rapid etc.
All multiple features make excavation to data in internet and make full use of full of challenge.Knowledge mapping passes through deep semantic analysis
And data mining, be knowledge network by the internet data high-efficiency tissue of magnanimity, knowledge is scanned for intuitive way and
Show, while also providing important leverage for big data analysis, intelligent answer, personalized recommendation etc..
Currently, the research work for knowledge mapping building aspect is primarily present following problems: 1. many research work are all
Highlight a certain link in knowledge mapping building process in isolation, for example, in knowledge mapping expressing for knowledge, map storage
With the extraction of knowledge etc..2. the knowledge mapping in terms of state enterprise's big data is deficienter, the enterprise basis of many government departments
Data are only isolated presence, are not carried out interconnecting between data.The main body of these based data services is usually
People, some data are only read by machine once in a while.
Therefore, the knowledge mapping of " human-centred " towards enterprise field how is constructed, by the enterprise in government and internet
It uses to sparetime university's data efficient, isolated back end is fused in unified knowledge base, provides a friend for user
Good, hommization business information services platform is particularly important.
Summary of the invention
The purpose of the present invention is to provide a kind of method from multi-source data integration visual angle building Company Knowledge map, this hairs
Bright beneficial effect is to realize that the angle of multi-source heterogeneous data integration can be with rapid build from building domain body and Karma modeling
The Company Knowledge map of " human-centred ", improve domain knowledge map construction speed and save knowledge mapping building at
This.The Company Knowledge map of building can efficiently use enterprise's big data in government and internet, by what is isolated
Back end is fused in unified knowledge base, provides the business information services platform of a close friend, hommization for user.
The technical scheme adopted by the invention is that knowing from the processing storage of data acquisition, knowledge fusion and knowledge to construct enterprise
Know map；Wherein, data capture method are as follows: relational database of the business data collection a part from government, another part pass through
Spiders is constructed to extract Baidupedia, interact relevant company information data collection in encyclopaedia, data are stored using JSON format,
For the extraction of encyclopaedia class web data, a set of enterprise's crawler system based on WebMagic frame is constructed, by writing canonical
Expression formula obtains the company information data in webpage.Knowledge fusion method: the mainly multi-source heterogeneous structuring number being directed to
According to collection, the multi-source heterogeneous data integrating method based on ontology and Karma modeling is proposed, to the enterprise's associated data set got
It is analyzed and is arranged, extract the related notion of the ontologies such as entity class, object properties and data attribute, semi-automation constructs
Business entity's ontology, it is multi-source heterogeneous using building ontology and a kind of open source Integrated Development Tool Karma building Karma model realization
The fast integration of data with merge, unified publication is at RDF data；Knowledge processing method: enterprise is completed based on Jena inference engine
Upper bottom reasoning, missing classification completion, consistency detection and the big function of custom rule reasoning four in knowledge mapping, to existing
Knowledge carries out completion and amendment.Knowledge store method: persistent storage is carried out to knowledge mapping using Neo4j chart database, is led to
Building RDF2Neo4j interpreter is crossed, RDF triple data are imported in Neo4j chart database and are stored.
Further, the workflow of enterprise's crawler system: the first step, the business data provided for government are parsed,
Extract business entity's title therein.Unified api interface, the initial URL of automatic Mosaic are provided using Baidupedia；
Second step, downloader use Apache HttpClient to ask as initial URL initiation of the download component to offer
It asks, obtains web object Page；
Third step, page parsing device parse webpage using the process method in pageProcessor, use
Jsoup parses html page into dom tree, extracts the new seed of useful information resources and discovery by CSS Selector
URL, it is main to extract three parts such as entry title, InfoBox and entry general introduction for enterprise's entry；
4th step, scheduler are responsible for managing URL and deduplication operation to be captured；
5th step, the web data that the processing of pipeline device is extracted, main includes saving data to file or database etc..
Further, the Karma modeling method based on ontology:
The first step is to import ontology and multi-source heterogeneous structured data sets, supports that the data format imported includes electronic watch
Lattice, relational database, XML, CSV, JSON etc.；
Second step is cleaning code data, it is ensured that the integrality of data format and content；
Third step is the semantic type that data are arranged and arrange, and imports after ontology, needs between ontology and different data sources
Semantic mapping is established, solves the problems, such as the Semantic Heterogeneous such as one justice of polysemy or more words；
4th step refers to the relationship determined between semantic type, constructs node according to ontology and the data column semantic type of setting
Between semantic association figure.
Further, knowledge processing passes through the enterprise RDF to business entity's ontology and integration release using Jena inference engine
Data carry out the next reasoning, missing classification completion, consistency detection and custom rule reasoning, complete knowledge completion and repair
Just.Specific method: 1. introducing RDFS inference machine, is differentiated between concept using subClassOf keyword in RDFS with the presence or absence of upper
The next relationship；2. introducing OWL inference machine does integrality reasoning to individual classification, the missing classification of the individual is supplemented；3. passing through
The inconsistency for the validate interface detection body that Jena is provided generates examining report and prints the specific letter of inconsistent example
Breath；4. describing user's custom rule using SWRL (Semantic Web Rule Language), user is by defining reasoning
Rule base carrys out implementation rule reasoning.
Further, specific step is as follows for knowledge store method:
RDF file is parsed using JenaAPI, subject, predicate and the object in each triple is obtained, triple is encapsulated
For object；RDF2Neo4j interpreter is constructed, is belonged to the value that the subject of RDF is mapped to node Node class using Cypher sentence
Property value, predicate are mapped to the value attribute value of relationship Property class, and object is mapped to the value attribute value of node Node class.
If there is the identical situation of the value value of multiple nodes, then same node is fused to.The user name of specified Neo4j, password,
Triple object set after mapping is imported Neo4j database server using Neo4j API by the parameters such as IP and port.
Further, knowledge based map and visualization technique design companies knowledge mapping application searching system, comprising: be 1.
System brief introduction: illustrating the major function and feature of system, introduces domain body building, Karma modeling, chart database and data set
Source etc.；2. enterprise and corporate entity's inquiry: providing the query function for business entity or corporate entity；3. relational query:
It provides to two different business entity's relation path inquiries, and shows enterprise's node associated therewith simultaneously；4. business data
Analysis statistics: the map for providing enterprise object region shows the analysis statistics of function and business data, and graph visualization can be used
Mode show business data.
Detailed description of the invention
Fig. 1 is a kind of Company Knowledge map construction method at multi-source data integration visual angle；
Fig. 2 is enterprise's crawler frame based on WebMagic；
Fig. 3 is the multi-source data integration method modeled using ontology and Karma；
Fig. 4 is the Neo4j storage scheme of knowledge mapping RDF data；
Fig. 5 is Company Knowledge application of the graphic chart system.
Specific embodiment
The present invention is described in detail With reference to embodiment.
Enterprise's big data field of Government of the present invention proposes a kind of real based on Domain Ontology Modeling and Karma modeling
The knowledge mapping construction method at existing multi-source heterogeneous data integration visual angle, this method are processed from data acquisition, knowledge fusion and knowledge
Storage building Company Knowledge map and Company Knowledge application of the graphic chart system, answer user and developer by feature-rich
With interface, the basic data of government department is easily used, really realizes interconnecting for data between different departments.To government
The fusion and application of department fundamentals data are of great significance.
In a first aspect, the invention proposes a kind of knowledge mapping construction method based on multi-source heterogeneous data fast integration,
The process of this method is as shown in Figure 1, the building of knowledge mapping can be divided into data acquisition, knowledge fusion, knowledge processing storage three
A part.Relational database of the data set a part from government, another part extract Baidupedia by building spiders
In relevant company information data collection, data using JSON format store, for the extraction of encyclopaedia class web data.
A set of enterprise's crawler system based on WebMagic frame is constructed, obtains webpage by writing regular expression
The general frame of the data of middle needs, crawler system is as shown in Figure 2.The frame mainly includes following four component: downloader, page
Face resolver, scheduler, conduit assembly.In crawler container, these component organizations are got up, by interacting and process
The execution of change completes data pick-up according to specific requirements.
The workflow of enterprise's crawler system: the first step, the business data provided for government are parsed, and are extracted wherein
Business entity's title.Unified api interface, the initial URL of automatic Mosaic are provided using Baidupedia.
Second step, downloader use Apache HttpClient to ask as initial URL initiation of the download component to offer
It asks, obtains web object Page.
Third step, page parsing device parse webpage using the process method in pageProcessor, use
Jsoup parses html page into dom tree, extracts the new seed of useful information resources and discovery by CSS Selector
URL.It is main to extract three parts such as entry title, InfoBox and entry general introduction for enterprise's entry.
4th step, scheduler are responsible for managing URL and deduplication operation to be captured.
5th step, pipeline device are responsible for the web data that processing is extracted, and main includes saving data to file or database etc..
The mainly multi-source heterogeneous structured data sets that knowledge fusion is directed to, propose based on business entity's ontology and
Karma models the method realizing multi-source heterogeneous data integration and merging, and work step is as shown in Figure 3.
The data set got is analyzed and arranged, the ontologies such as entity class, object properties and data attribute are extracted
Related notion, complete business entity's ontology building.
The first step of Karma modeling is to import the structured data sets of ontology and multi-data source, and this method is applicable in importing
Data format includes electrical form, relational database, XML, CSV, JSON etc..For MySQL data source, pass through specified database
The parameters such as URL, database-name, user name, the password of server import related data, wherein can set importing line number and
The information such as character encoding format.
The second step of Karma modeling is cleaning code data, it is ensured that data format and content intact.
The third step of Karma modeling refers to fixed number according to the semantic type of column.It imports after ontology, in ontology and different data
Semantic mapping is established between source, solves the problems, such as the Semantic Heterogeneous such as one justice of polysemy or more words.Karma uses condition random field
CRF (Condition Random Field) model goes the data type for learning to propose based on previous user, according to ontology sum number
The semantic type of the mapping relations and field between different data is identified according to field, so that the field for unallocated semanteme recommends semanteme
Type.
4th step of Karma modeling refers to the relationship determined between semantic type.Karma passes through steiner tree Steiner
Tree algorithm calculates the minimum tree of all semantic relations between connection data source and Ontological concept.
Issue R2RML (RDB to RDF mapping) model and unified RDF data.It can be with rapid build using R2RML model
Ontology and data column between Semantic mapping, thus improve large data sets at efficiency.The RDF data of publication is advised with grammer
Model, semantic clearly characteristic, are one of expression ways of knowledge mapping.Developer can carry out knowledge to unified RDF data
Reasoning can also carry out completion and amendment to the instance data in knowledge mapping to excavate tacit knowledge.
Knowledge processing is mainly further perfect to the RDF data of publication.The present invention is carried out using the inference engine of Jena
Ontology inference mainly comprises the steps that 1. Model is the data structure of Jena core, first has to be to use model factory
The relevant information in FactoryModel class creation of knowledge library, this includes ontology and RDF triple data.2. being registered by inference machine
Device ReasonerRegister class constructs specific inference machine, it is bound with model object (Model), generating has reasoning function
The model object (InfModel) of energy.3. being carried out using JenaAPI to established data model according to actual business demand
Reasoning and calculating.
The ontology and RDF triple of description Upper Concept are mainly contained for the knowledge mapping of multi-source data integration building
Data, express in specific area between concept the existing direct relation between example, but for implying in knowledge mapping
Information can not be obtained directly by simple queries.
Present invention is primarily based on Jena inference engines to complete the upper of enterprise's RDF data of business entity's ontology and integration release
The next reasoning, missing classification completion, consistency detection and the big function of custom rule reasoning four, carry out completion to existing knowledge
And amendment.Specific method: 1. introducing RDFS inference machine, differentiates whether deposit between concept using subClassOf keyword in RDFS
In hyponymy；2. introducing OWL inference machine does integrality reasoning to individual classification, the missing classification of the individual is supplemented；3. leading to
The inconsistency for crossing the validate interface detection body of Jena offer generates examining report and prints the specific of inconsistent example
Information；4. describing user's custom rule using SWRL (Semantic Web Rule Language), user is pushed away by definition
Reason rule base carrys out implementation rule reasoning.
Knowledge store, for relationship between data complexity and dynamic change the problems such as, it is contemplated that the extension of knowledge mapping
And maintenance, the present invention carry out persistent storage to knowledge mapping using Neo4j chart database.Neo4j chart database has epistasis
The features such as capable of, easily extending, affairs, backstage is supported to visualize, can effectively organize, stores and update dynamic data and its pass
Connection, and efficient ergodic algorithm is provided and supports multilayer complex query, play a significant role in terms of knowledge store and Knowledge representation.
Therefore, it is proposed to which the scheme that the RDF data of knowledge mapping is persisted to Neo4j chart database is as shown in Figure 4.
The RDF data of multi-source heterogeneous data publication is integrated based on domain body and Karma modeling, the present invention passes through building
RDF triple data are imported in Neo4j chart database and are stored by RDF2Neo4j interpreter, the specific steps are as follows:
RDF file is parsed using JenaAPI, subject, predicate and the object in each triple is obtained, triple is encapsulated
For object.
RDF2Neo4j interpreter is constructed, the subject of RDF is mapped to the value of node Node class using Cypher sentence
Attribute value, predicate are mapped to the value attribute value of relationship Property class, and object is mapped to the value attribute of node Node class
Value.If there is the identical situation of the value value of multiple nodes, then same node is fused to.
The parameters such as user name, password, IP and the port of specified Neo4j, using Neo4j API by the triple pair after mapping
As set imports Neo4j database server.
Knowledge based map and visualization technique design companies knowledge mapping application searching system, comprising: 1. SYSTEM SUMMARY:
The major function and feature of elaboration system introduce domain body building, Karma modeling, chart database and data set source etc.；
2. enterprise and corporate entity's inquiry: providing the query function for business entity or corporate entity；3. relational query: providing to two
A difference business entity's relation path inquiry, and show enterprise's node associated therewith simultaneously；4. enterprise data analysis counts:
The map for providing enterprise object region shows the analysis statistics of function and business data, and the mode of graph visualization can be used to show
Show business data.
As shown in figure 5, the general frame of system follows the design philosophy of three-tier architecture, it is followed successively by data query from top to bottom
Layer, Business Logic, presentation layer divide business scope according to the thought of " strong cohesion, weak coupling ".
System integrally uses B/S structure, and rear end uses Spring Boot framework establishment micro services, provides RESTful and connect
Mouthful.Front end constructs visualization interface using HTML5 and Echarts the relevant technologies, and database uses Neo4j chart database.Pass through
It constructs interpreter and RDF data is imported into Neo4j chart database, realize the storage and visualization of RDF data.
Data query layer mainly use Spring Data module operate Neo4j chart database, and write Cypher sentence with
Chart database interacts, and completes the statistical query to business data.
What Business Logic was substantially carried out is the processing of data, by calling data query layer further to the data of return
Encapsulation, completes the statistical analysis and format specification of data.The correlation function completed as needed carries out writing for service logic, and
Data after encapsulation are passed into presentation layer, data interchange format uses JSON.
Presentation layer, which will receive data and pass to front end, to be rendered, end page before utilizing Echarts component and HTML5 to realize
The visualization in face.
The above is only not to make limit in any form to the present invention to better embodiment of the invention
System, any simple modification that embodiment of above is made according to the technical essence of the invention, equivalent variations and modification,
Belong in the range of technical solution of the present invention.
Claims (6)
1. a kind of method from multi-source data integration visual angle building Company Knowledge map, it is characterised in that: based on Ontology Modeling and
Karma modeling realizes that the visual angle rapid build Company Knowledge map of multi-source heterogeneous data integration can be divided into data acquisition, knowledge
Fusion and knowledge processing storage；
Wherein, data capture method are as follows: relational database of the data set a part from government, another part pass through building webpage
Crawler extracts relevant company information data in Baidupedia, and data are stored using JSON format, for encyclopaedia class web data
Extraction, construct a set of enterprise's crawler system based on WebMagic frame, obtained in webpage by writing regular expression
The company information data needed, crawler system frame include following four component: downloader, page parsing device, scheduler, pipeline
Component gets up these component organizations in Spider container, by interacting the execution with procedure, according to specific
Demand completes data pick-up；
Knowledge fusion method: for multi-source heterogeneous structured data sets, the data integration based on business entity's ontology is proposed
Method is analyzed and is arranged to the data set got, and it is related to data attribute ontology to extract entity class, object properties
Concept, complete enterprise's domain body building, using ontological construction Karma model carry out multi-source data fast integration with melt
It closes；
Knowledge processing method: the upper the next reasoning of enterprise's RDF data of inference engine completion business entity's ontology and integration release,
Classification completion, consistency detection and the big function of custom rule reasoning four are lacked, completion and amendment are carried out to existing knowledge；
Knowledge store method: persistent storage is carried out to knowledge mapping using Neo4j chart database, by constructing RDF2Neo4j
RDF triple data are imported in Neo4j chart database and are stored by interpreter.
2. existing according to a kind of method from multi-source data integration visual angle building Company Knowledge map, feature described in claim 1
In: the workflow of enterprise's crawler system: the first step, the Some Enterprises data provided for government are parsed, and are extracted
Business entity's title therein provides unified api interface, the initial URL of automatic Mosaic using Baidupedia；
Second step, downloader use Apache HttpClient to initiate request as initial URL of the download component to offer, obtain
Take web object Page；
Third step, page parsing device parse webpage using the process method in pageProcessor, use jsoup
Html page is parsed into dom tree, the new seed URL of useful information resources and discovery, needle are extracted by CSS Selector
To enterprise's entry of Baidupedia, main entry title, InfoBox and the entry of extracting summarizes three parts；
4th step, scheduler are responsible for managing URL and deduplication operation to be captured；
5th step, pipeline device are responsible for the web data that processing is extracted, and main includes saving data to file or database.
3. existing according to a kind of method from multi-source data integration visual angle building Company Knowledge map, feature described in claim 1
In: the modeling method of the Karma:
The first step is the business entity's ontology for importing building and multi-source heterogeneous structured data sets, supports the data format imported
Including electrical form, relational database, XML, CSV, JSON；
Second step is cleaning code data, it is ensured that the integrality of data format and content；
Third step is the semantic type that data are arranged and arrange, and imports after ontology, needs to establish between ontology and different data sources
Semantic mapping solves the problems, such as polysemy or the adopted Semantic Heterogeneous of more words one；
4th step refers to the relationship determined between semantic type, according between ontology and the data column semantic type of setting building node
Semantic association figure.
4. existing according to a kind of method from multi-source data integration visual angle building Company Knowledge map, feature described in claim 1
In: it is described that the next reasoning on knowledge mapping, missing classification completion, consistency detection and customized are carried out using Jena inference engine
Rule-based reasoning carries out knowledge completion and modified specific method: 1. introducing RDFS inference machine, is closed using subClassOf in RDFS
Key word carries out hyponymy reasoning between concept；Integrality reasoning, completion individual are done to individual classification 2. introducing OWL inference machine
Missing classification；3. generating examining report by the inconsistency of the Jena validate detection body provided and printing different
Cause the specifying information of example；4. describing user's custom rule using SWRL (Semantic Web Rule Language), use
Family is by defining inference rule library come implementation rule reasoning.
5. existing according to a kind of method from multi-source data integration visual angle building Company Knowledge map, feature described in claim 1
In: specific step is as follows for the knowledge store method:
RDF file is parsed using JenaAPI, obtains subject, predicate and the object in each triple, triple is encapsulated as pair
As；
RDF2Neo4j interpreter is constructed, the subject of RDF is mapped to the value attribute of node Node class using Cypher sentence
Value, predicate are mapped to the value attribute value of relationship Property class, and object is mapped to the value attribute value of node Node class, such as
There is the identical situation of value value of multiple nodes in fruit, then is fused to same node；
User name, password, IP and the port parameter of specified Neo4j, using Neo4j API by the triple object set after mapping
Import Neo4j database server.
6. existing according to a kind of method from multi-source data integration visual angle building Company Knowledge map, feature described in claim 1
In knowledge based map and visualization technique design companies knowledge mapping application searching system, comprising: 1. SYSTEM SUMMARY: illustrating
The major function and feature of system introduce domain body building, Karma modeling, chart database and data set source；2. enterprise
It is inquired with corporate entity: the query function for business entity or corporate entity is provided；3. relational query: providing to two differences
The inquiry of business entity's relation path, and show enterprise's node associated therewith simultaneously；4. enterprise data analysis counts: providing enterprise
The map of industry distributed areas shows the analysis statistics of function and business data, and enterprise's number is shown by the way of graph visualization
According to.
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