CN108038136A - The method for building up and graph inquiring method of Company Knowledge collection of illustrative plates based on graph model - Google Patents

The method for building up and graph inquiring method of Company Knowledge collection of illustrative plates based on graph model Download PDF

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Publication number
CN108038136A
CN108038136A CN201711178658.1A CN201711178658A CN108038136A CN 108038136 A CN108038136 A CN 108038136A CN 201711178658 A CN201711178658 A CN 201711178658A CN 108038136 A CN108038136 A CN 108038136A
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China
Prior art keywords
graph
data
illustrative plates
node
knowledge collection
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CN201711178658.1A
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Chinese (zh)
Inventor
廖辰瀚
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Shanghai Wind Sound Enterprise Credit Credit Co Ltd
SHANGHAI WISDOM INFORMATION TECHNOLOGY Co Ltd
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Shanghai Wind Sound Enterprise Credit Credit Co Ltd
SHANGHAI WISDOM INFORMATION TECHNOLOGY Co Ltd
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Priority to CN201711178658.1A priority Critical patent/CN108038136A/en
Publication of CN108038136A publication Critical patent/CN108038136A/en
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90Details of database functions independent of the retrieved data types
    • G06F16/901Indexing; Data structures therefor; Storage structures
    • G06F16/9024Graphs; Linked lists
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/10Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting

Abstract

The invention discloses a kind of method for building up of the Company Knowledge collection of illustrative plates based on graph model, include the following steps:Model foundation step:Diagram data model is built, the diagram data model includes graph data structure module, figure mining algorithm module and query interface module;Sub-data recording step:Each dimension data of enterprise of sample is obtained, the dimension datas such as the enterprise shareholder, investments abroad, public sentiment, the administration of justice are recorded in the graph data structure module respectively by the diagram data model;Encapsulation step:Figure mining algorithm is packaged into the query interface module according to common application scenarios, relation graph model is returned to JSON forms.The invention also discloses a kind of graph inquiring method based on diagram data model.

Description

The method for building up and graph inquiring method of Company Knowledge collection of illustrative plates based on graph model
Technical field
The invention belongs to field of computer technology, more particularly to a kind of Company Knowledge collection of illustrative plates method for building up based on graph model And querying method.
Background technology
Diagram data model stores data with figure, is closest to a kind of high performance data structure side for being used to store data One of formula.One figure is made of countless node and relation, and simplest figure is single node, and a record, have recorded Attribute.One node can include some attributes, data are connected with relation be distributed on different nodes be only it is significant 's.Relation is by node organization into arbitrary structure, it is allowed to a figure is organized into a list, a map, or one by The entity of the structure composition complexity of relation highlights correlations.But existing diagram data model mostly just establish and express two nodes it Between relation, it can not meet the structure and difficulty of the relationship expression between three or more than three nodes.
The search of diagram data pattern query is usually using Dijkstra shortest path firsts.Shortest path first is that one kind asks single Source, the shortest path without negative power, it has the characteristics that timeliness is preferable.Time complexity used in shortest path first is O (V*V+ E), if diagram data model source point is reachable, O (V*lgV+E*lgV)=>O(E*lgV).If diagram data model is sparse graph, E=V*V/lgV at this time, so the time complexity of algorithm can be O (V^2).If diagram data model is Fibonacci heap make it is excellent First queue, then Algorithms T-cbmplexity is O (V*lgV+E).But the algorithm practical application is difficult, in processing node and side quantity During larger complex figure, perform poor on time complexity.
In order to overcome drawbacks described above of the prior art, the present invention proposes a kind of enterprise's incidence relation based on graph model Topological method for building up and querying method.The present invention carries out data query using Traversal, and Traversal algorithms are from some Beginning node starts a query at node associated with it, is that node and relation establish index, inquiry can be made more efficient.
The content of the invention
The present invention proposes a kind of Company Knowledge collection of illustrative plates method for building up based on graph model, includes the following steps:
Model foundation step:Diagram data model is built, the diagram data model includes graph data structure module, figure is excavated and calculated Method module and query interface module;
Sub-data recording step:Enterprise's incidence relation data of sample are obtained, by the diagram data model respectively by the enterprise Industry incidence relation data record is in the graph data structure module;
Interface encapsulation step:Figure mining algorithm is packaged into the query interface module according to common application scenarios, with JSON forms return to relation graph model.
In the method for building up of Company Knowledge collection of illustrative plates proposed by the present invention based on graph model, the graph data structure module bag Include:Node, relation and attribute.
In the method for building up of Company Knowledge collection of illustrative plates proposed by the present invention based on graph model, the node includes:Natural person With mechanism.
In the method for building up of Company Knowledge collection of illustrative plates proposed by the present invention based on graph model, the relation includes:Legal generation Table people, lineal relative, tenure and investment.
In the method for building up of Company Knowledge collection of illustrative plates proposed by the present invention based on graph model, the attribute includes:Entity is noted Volume time, registered capital etc..
The invention also provides a kind of graph inquiring method based on the diagram data model, include the following steps:
Step 1:The figure mining algorithm module uses Traversal algorithms, defines Paths parameters;
Step 2:Node to be checked is inputted, according to node to be checked by dijkstra's algorithm to the diagram data model In single source shortest path scan for;
Wherein, dijkstra's algorithm is described as follows:
1. arcs is made to represent the weights on arc.If arc is not present, it is just infinite to put arcs.S is the slave v found Terminal set, original state is empty set.So, the length being likely to be breached from V to remaining upper each vertex V [i] of figure Initial value is D=arcs [Locate Vex (G, V [i])], V [i] ∈ V;
2. select V [j] so that D [j]=Min D | V [i] ∈ V-S };
3. change the shortest path length on any vertex into set V-S from V.
Step 3:Node to be checked is inputted, according to node to be checked by Kruskal algorithms in the diagram data model Minimum spanning tree scan for;
Arthmetic statement is as follows:
1. input:One weighting connected graph, wherein vertex set is V, line set E;
2. initialization:Vnew={ x }, wherein x are any node (starting point) in set V, and Enew={ }, is sky;
3. following operation is repeated, until Vnew=V:
A. the side of weights minimum is chosen in set E<u,v>, wherein u is the element in set Vnew, and v is not in Vnew Among set, and v ∈ V (if there is have it is a plurality of meet aforementioned condition i.e. have identical weights side, then can arbitrarily choose it One of);
B. v is added in set Vnew, will<u,v>Side is added in set Enew;
4. output:Obtained minimum spanning tree is described using set Vnew and Enew.
The beneficial effects of the present invention are:The present invention is built by establishing diagram data model according to the data in Company Knowledge storehouse Vertical finance between enterprise, natural person, the relation such as be subordinate to, employ, and with two or three above enterprises of graphic software platform and enterprise Relation between industry, enterprise and natural person, natural person and natural person, discloses a kind of new firms incidence relation topological diagram and establishes And inquiry system.
By the present invention in that carry out data query with breadth First Traversal, breadth First Traversal algorithms are from one A little start nodes start a query at node associated with it, are that node and relation establish index, inquiry can be made more efficient.
Brief description of the drawings
Fig. 1 is the flow chart of the Company Knowledge collection of illustrative plates method for building up of the invention based on graph model.
Fig. 2 is the diagram data model logic view of the embodiment of the present invention.
Fig. 3 is the flow chart of the Company Knowledge collection of illustrative plates querying method based on graph model.
Fig. 4 is the diagram data model ergod searching algorithm view of the embodiment of the present invention.
Fig. 5 is the diagram data model indexed view of the embodiment of the present invention.
Fig. 6 is that the diagram data model business model of the embodiment of the present invention designs.
Fig. 7 is the diagram data model Shortest Path Searching Algorithm example of the embodiment of the present invention.
Fig. 8 is the diagram data model ergod algorithm example of the embodiment of the present invention.
Fig. 9 is that the realization of the embodiment of the present invention inquires about all natural person or machines for having direct relation with it according to individual enterprise Structure
Figure 10 be the realization of the embodiment of the present invention according to Liang Ge enterprises, inquire about and specify in step-length the relevant pass of institute therebetween System.
Figure 11 be the realization of the embodiment of the present invention according to a collection of enterprise, inquire about specify this batch of enterprise in step-length mutual Incidence relation.
Embodiment
With reference to specific examples below and attached drawing, the present invention is described in further detail.The process of the implementation present invention, Condition, experimental method etc., in addition to the following content specially referred to, are among the general principles and common general knowledge in the art, this hair It is bright that content is not particularly limited.
The Company Knowledge collection of illustrative plates method for building up of the invention based on graph model is shown in Fig. 1, specifically comprises the following steps:
Model foundation step:Diagram data model is built, the diagram data model includes graph data structure module, figure is excavated and calculated Method module and query interface module;
Sub-data recording step:The Company Knowledge spectrum data of sample is obtained, by the diagram data model respectively by the enterprise Industry knowledge mapping data record is in the graph data structure module;
Interface encapsulation step:Figure mining algorithm is packaged into the query interface module according to common application scenarios, with JSON forms return to relation graph model.
Diagram data model logic view in the embodiment of the present invention is shown in Fig. 2.In the present embodiment, graph data structure mould Block includes:Node, relation and attribute.Its interior joint is arranged to include:Natural person and mechanism;Relation is arranged to include:Method Determine representative, lineal relative, tenure and investment;Attribute includes:Hour of log-on, legal representative, registered capital, subscribe, amount paid in gold Volume etc..Node and relation have corresponding attribute respectively, and then carry out tissue by relation between node and node.
As shown in fig. 6, the node in the present embodiment includes title and attribute;Relation includes name attribute, it is with direction. Included in the present embodiment comprising 4 nodes:Natural person first, company A, company B, company C, relation includes investment relation, tenure is closed System.Natural person's first and company A are investment relation, are tenure relation with company B;Company A is respectively investment with company B and company C Relation, company B and company C is investment relation.By using above-mentioned node and relation can normally describe enterprise with enterprise, from So incidence relation between people and enterprise, and by inquiring about to generate corresponding graph inquiring result.
Embodiment 1
In the incidence relation of one group of enterprise of addition, each dimension collection of illustrative plates number of the typing enterprise first into topological structure According to.The spectrum data of the enterprise includes:Organization names, its legal representative, investor, debt or credits side, branch, confession Answer chain relation, litigious relation, guarantee, public sentiment reference, document reference etc..In Data Input Process, relationship entity is recorded in figure In the node of data model, by the relation record of inter-entity in side, the feature of entity in itself is recorded in nodal community.
Then, the relation between entity and entity is extracted by semantic analytic technique according to the data obtained in real time Come, formed using the time, entity, relation as staple event.Finally, by with the feature of entity in itself in data on stock storehouse Matching, node, side and the attribute non-structured industry and commerce, the administration of justice, public sentiment, finance being mapped in Company Knowledge collection of illustrative plates.
Enterprise's incidence relation topological graph querying method of the invention based on graph model is shown in Fig. 3, it includes:
Step 1:The figure mining algorithm module uses Traversal algorithms, defines Paths parameters, Depth parameters, returns Return number of nodes parameter;
Step 2:Node to be checked is inputted, according to node to be checked by Kruskal algorithms in the diagram data model Network scan for, time complexity is O (e^2), and complexity is O (eloge) after being optimized using Union-find Sets, and in net Side number is related, the minimum spanning tree suitable for seeking the sparse net in side;
Step 3:Using dijkstra's algorithm, scanned for generated in step 2 in minimum spanning tree, generate figure Change query result.
Diagram data model ergod searching algorithm view is shown in Fig. 4.The present invention is calculated using breadth First Traversal Method, the pseudo-code of the algorithm are as follows:
Input:A graph G and a vertex v of G.
Output:The closest vertex to v satisfying some conditions,or null if no sucha vertex exists.
Fig. 7 and Fig. 8 uses Shortest Path Searching Algorithm example, and algorithm is as follows:
1function Dijkstra(G,w,s)
2for each vertex v in V [G] // initialization
3d[v]:=infinity//d [v] stores other vertex to the beeline of starting point s
4previous[v]:=undefined//previous [v] stores the preposition node on all vertex
5d[s]:=0
6S:=empty set
7Q:=set of all vertices
8while Q is not an empty set//Dijkstra algorithm Zhu Body
9u:=Extract_Min (Q) //Extract_Min () generally realizes O (logN) using most rickle
10S:=S union { u }
11for each edge(u,v)outgoing from u
12if d[v]>The d [v] of its adjacent node of d [u]+w (u, v) // updated according to the d [u] of present node u
13d[v]:=d [u]+w (u, v)
14previous[v]:=u
Diagram data model indexed view is shown in Fig. 5.Index is generated according to the hash mapping on node and side. , can be with random challenge node and the side for the once relation being connected directly with the node according to the index.
Embodiment 2
The present invention preferably inquires about all natural person or mechanisms for having direct relation with it according to individual enterprise.
Embodiment 3
The present invention inquires about preferably according to Liang Ge enterprises and specifies in step-length that institute is relevant therebetween.
Embodiment 4
The preferred embodiment of the present invention is according to a collection of enterprise, inquires about and specifies the association that this batch of enterprise is mutual in step-length to close System.
The protection content of the present invention is not limited to above example.Without departing from the spirit and scope of the invention, originally Field technology personnel it is conceivable that change and advantage be all included in the present invention, and using appended claims as protect Protect scope.

Claims (9)

1. a kind of method for building up of the Company Knowledge collection of illustrative plates based on graph model, it is characterised in that include the following steps:
Model foundation step:Diagram data model is built, the diagram data model includes graph data structure module, figure mining algorithm mould Block and query interface module;
Sub-data recording step:Enterprise's incidence relation data of sample are obtained, respectively close the enterprise by the diagram data model Connection relation data is recorded in the graph data structure module;
Interface encapsulation step:Figure mining algorithm is packaged into the query interface module according to common application scenarios, with JSON lattice Formula returns to relation graph model.
2. the method for building up of the Company Knowledge collection of illustrative plates based on graph model as claimed in claim 1, it is characterised in that the figure number Include according to construction module:Node, relation and attribute.
3. the method for building up of the Company Knowledge collection of illustrative plates based on graph model as claimed in claim 2, it is characterised in that the node Including:Natural person and mechanism.
4. the method for building up of the Company Knowledge collection of illustrative plates based on graph model as claimed in claim 2, it is characterised in that the relation Including:Legal representative, lineal relative, tenure and investment.
5. the method for building up of the Company Knowledge collection of illustrative plates based on graph model as claimed in claim 2, it is characterised in that the attribute Including:Registers entities time, registered capital.
6. the method for building up of the Company Knowledge collection of illustrative plates based on graph model as claimed in claim 1, it is characterised in that various dimensions, The association and inquiry of isomeric data, including:Guarantee, lawsuit, public sentiment, administrative penalty, wealth between equity, investment, tenure, enterprise Tax, supply chain, inlet and outlet, bidding, intellectual property, pledge.
A kind of 7. graph inquiring method of the Company Knowledge collection of illustrative plates based on graph model, it is characterised in that include the following steps:
Step 1:The figure mining algorithm module uses Traversal algorithms, defines Paths parameters;
Step 2:Node to be checked is inputted, according to node to be checked by dijkstra's algorithm in the diagram data model Single source shortest path scans for;
Step 3:Input node to be checked, according to node to be checked by Kruskal algorithms in the diagram data model most Small spanning tree scans for.
8. the graph inquiring method of the Company Knowledge collection of illustrative plates based on graph model as claimed in claim 7, it is characterised in that Dijkstra's algorithm is described as follows:
Arcs is made to represent the weights on arc;If arc is not present, it is just infinite to put arcs;S is the terminal of the slave v found Set, original state is empty set;The initial value for the length being likely to be breached from V to remaining upper each vertex V [i] of figure is D= Arcs [Locate Vex (G, V [i])], V [i] ∈ V;
Select V [j] so that D [j]=Min D | V [i] ∈ V-S };
Change the shortest path length on any vertex into set V-S from V.
9. the graph inquiring method of the Company Knowledge collection of illustrative plates based on graph model as claimed in claim 7, it is characterised in that Kruskal arthmetic statements are as follows:
Input:One weighting connected graph, wherein vertex set is V, line set E;
Initialization:Vnew={ x }, wherein x are any node (starting point) in set V, and Enew={ }, is sky;
Following operation is repeated, until Vnew=V:
A. the side of weights minimum is chosen in set E<u,v>, wherein u is the element in set Vnew, and v does not gather in Vnew In the middle, and v ∈ V;
B. v is added in set Vnew, will<u,v>Side is added in set Enew;
Output:Obtained minimum spanning tree is described using set Vnew and Enew.
CN201711178658.1A 2017-11-23 2017-11-23 The method for building up and graph inquiring method of Company Knowledge collection of illustrative plates based on graph model Pending CN108038136A (en)

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CN109726203A (en) * 2018-12-20 2019-05-07 四川新网银行股份有限公司 A kind of date storage method of reconstruct image
CN109785144A (en) * 2019-01-18 2019-05-21 国家电网有限公司 A kind of assets classes method, apparatus, equipment and medium
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CN110209834A (en) * 2019-04-19 2019-09-06 广东省智能制造研究所 A kind of hypergraph construction method for manufacturing industry process equipment Information Atlas
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CN110232078A (en) * 2019-04-26 2019-09-13 上海生腾数据科技有限公司 A kind of enterprise group's Relation acquisition method and system
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