CN102646111A - Knowledge base-based fast construction method of common correlation information query tree - Google Patents
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Abstract
The invention relates to a knowledge base-based fast construction method of a common correlation information query tree. The knowledge base-based fast construction method comprises the following steps of: (1) constructing a correlation information logic tree structure of a business main body by taking the business main body as a root node according to business relations among various data in target data; (2) recording datasheets to which a father node and child nodes point, as well as correlation fields among the datasheets according to the correlation information logic tree structure; (3) constructing a knowledge base of the correlation information tree structure according to information of the correlation fields among the datasheets of the tree-like structure; (4) constructing a correlation information query function according to the knowledge base of the correlation information tree structure, and realizing the common correlation information query with a loose coupling degree in a recursive manner; and (5) reconstructing the correlation information of the business main body according to the correlation information logic tree structure. The method is suitable for all one-to-one table correlation business requirements; and by adopting the method, the construction efficiency of a complex business information extraction function can be greatly improved, and the fast adaptation to the adjustment of the business requirements can be realized.
Description
Technical field
The present invention relates to a kind of search method of related information, particularly relate to a kind of fast construction method of setting based on the generic associative information inquiry of knowledge base.
Background technology
Information retrieval; Be that Information Retrieval (IR) is a science of being devoted to realize that high capacity information is effectively stored and obtained; In practical application, it is huge that high capacity information is embodied in data volume on the one hand, is embodied in incidence relation complicacy between data on the other hand.The Internet era, public information towards the public not only quantitatively is magnanimity property, relation is complicated between various information, and often these relations all are that implicit expression exists, and needs further to excavate to find.The information fast and high quality retrieval technique demand of cybertimes is urgent, and various intelligent retrievals are technological, especially artificial intelligence technology is arisen at the historic moment.The fast development of technology is used, and has expedited the emergence of internet giants such as Google, Baidu, finds quickly and accurately that from magnanimity information implicit, valuable information becomes possibility.This type of comprehensive information retrieval is through subjects theory such as comprehensive utilization mathematics, computer science, kybernetics, psychology, philosophy; Technology such as problem solving, expert system, machine learning, pattern-recognition, automatic theorem proving, natural language understanding, artificial neural network finally realize the artificial intelligence retrieval.Current, mainly concentrate on intelligent search method research towards the information search of internet based on ontology, neural network, genetic algorithm, natural language understanding and ID3 algorithm etc.
With the closely-related knowledge base technology of artificial intelligence except in expert system, having a wide range of applications; In information retrieval positive exploration is arranged also; Wait clear grade of the Chinese and be directed against Chinese web page text message characteristic; Proposition has also made up a webpage automatic indexing and an automatic classification system (2004) based on knowledge base, has improved automaticity and intelligent degree that webpage is handled; Qiu Jun equality has been studied the intelligent searching engine research (2006) based on KBS, through knowledge processing and the understandability that improves search engine, and its query and search ability is provided; The intelligence that Zhang Po etc. utilize the ontology knowledge storehouse to study Web service under the Internet environment is found (2005).
Tree construction is having a wide range of applications aspect information organization and the expression; Aspect fault diagnosis; Li Deying etc. utilize tree construction to study the expression directly perceived and the diagnosis (1998) of station boiler fault knowledge; Kong Fansen etc. have studied based on the automobile failure diagnosis of fault tree knowledge (2001); Bai Jianshe etc. have studied the transformer station's fault diagnosis (2004) based on decision tree, and Wang Yi etc. will set the fault of electric locomotive of introducing that arrives and diagnose (2004), and Zhu Zhaohui etc. utilize fault tree to carry out dam safety diagnosis (2006); At information extraction and management aspect, Hu Dongdong, Wang Xian etc. are based on tree construction; Studied the discovery of Web data recording and separated out, and, target pages has been searched for and analyzed (2004 automatically according to corresponding decimation rule; 2007), Zhuo Xiaojun etc. are incorporated into tree construction in the product data management, have improved work efficiency (2006) to a certain extent; Bringing up time etc. utilizes binary tree structure to carry out the research of concealed channel searching method; Construct a kind of binary tree information flow tree that can describe and write down the statement information flow and be easy to realize, effectively realized the directviewing description and the express-analysis (2005) of information flow, Xiao Zhenyu etc. have studied a kind of distributed data distribution method based on tree construction; Adopt the continuous-flow type transmission mode to carry out data transmission, overcome the deficiency (2010) of parts of traditional data distributing method.
The main magnanimity information towards the public of above-mentioned information retrieval; The characteristics of this type of information retrieval are loose, approximate finding the solution, and towards the then often accurately retrieval of requirement of application of concrete industry, its information at first will be passed through screening, classification; Meeting under the prerequisite of practical application request; The refining of guarantee information again, succinct, the convenient use is so this type of achievement in research can not satisfy the quick structure requirement of current industry related information query function.Simultaneously, existing tree construction theory mainly concentrates on information organization and data analysis research, and quick related information inquiry is had certain evocation.And in all sector applications of objective world, always can summarize one or several business divisions, and direct or indirect relation can take place with the mode and the business division of tree node in the out of Memory that is associated with it.So; Theoretically, utilize the incidence relation between all kinds of business information of KBM and business division, and introduce tree construction on this basis; Miscellaneous service related information and query function are carried out the methodization tissue, can extract a kind of general business information querying method.The present invention combines knowledge base and tree construction exactly; Come the numerous sector application demands of fast adaptation through the knowledge base content configuration; With the tree construction mode come descriptor recursive query function logical organization, carry out visual to main body and related information thereof; Thereby realize the physical separation between correlation inquiry function and concrete business information query demand, and reach the purpose that function makes up fast, efficient information is inquired about and visual result is expressed.
The present invention derives from national science and technology supporting plan important function for of research achievement, the better quick integration inquiry of being satisfied with magnanimity multisource emergency command data, and obtained good effect.
Summary of the invention
In order to solve the problems of the technologies described above, the invention provides a kind of fast construction method of setting based on the generic associative information inquiry of knowledge base, it is characterized in that comprising step:
Step (1) according to the business relations between Various types of data in the target data, is a root node with the business division, makes up the related information logical tree structure of business division;
Step (2), according to said related information logical tree structure, record father node and child node tables of data pointed separately, and mutual associate field;
Step (3) according to associate field information between the tree structure tables of data, makes up related information tree construction knowledge base;
Step (4), according to said related information tree construction construction of knowledge base related information query function, and the generic associative information inquiry of Recursive Implementation loose coupling degree;
Step (5), according to said related information logical tree structure, the related information of reorganization business division.
In technique scheme, the related information tree construction construction of knowledge base method in the step (3) is at first to distribute the unique index value for each tree node; Write down his father's nodal information then, if certain tree node is not a root node, then write down its parent node index, otherwise be designated as invalid value; Simultaneously, if in the father node representative tables of data in set of fields F1 and the child node representative tables of data set of fields F2 have certain incidence relation, then write down F1 and F2 respectively, as the foundation that dynamically associates in the future inquiry.
In technique scheme, the recursive query method of the related information in the step (4) comprises step:
Step (a) inquires the target service main body by external information, creates root node, and Query Result information is bundled in the root node of new establishment, and the setting root node is a present node;
Step (b) travels through said related information tree construction knowledge base, and the child node B that searches present node A describes set; If child node B set is not empty; Execution in step (c), otherwise return the last layer tree, recurrence is handled other nodes successively; All child nodes under root node all dispose, execution in step (d);
Step (c) is described from said related information tree construction knowledge base circulation extraction child node B (i), and and present node between associate field; And be constraint condition with present node information, from said related information tree construction knowledge base, inquire about B (i) indication tables of data record, if there is qualified table record; Then under present node, create new node, Query Result is bundled in new establishment child node, setting newly-built node is present node; And execution in step (b), otherwise handle next node B (i+1), up to B0 ... All nodes of Bn all dispose; Return the last layer tree, recurrence is handled other nodes successively;
Step (d), the related information tree makes up and finishes, and accomplishes the related information inquiry.
In technique scheme, further comprise: when the change of business information query demand, adjust tree construction knowledge base content, with the new business information correlation inquiry demand of self-adaptation through the direct modification knowledge base.
Characteristics of the present invention are:
1, is the center with the practical business main body, according to the practical business characteristics, makes up business division related information logical tree structure, the mutual relationship between distinct multi-level complicated business information directly perceived and business division;
2, utilize incidence relation between knowledge base record complicated business information table, realize general complicated business associating information method for distilling with recursive mode at last, thereby simplify the implementation procedure of complicated business information extraction, shorten the project cycle;
3, strengthened the extensibility of related information abstraction function separating of information extraction logic function and physical data, improved the suitable ability of this achievement in research.
The present invention has obtained following technique effect:
The inventive method is simple, it is high to carry out efficient, is applicable to that all show the associated services demand one to one, possesses very strong query function multiplexing capacity, can improve complicated business information extraction formation function efficient greatly, and the adjustment of fast adaptation business demand.
Description of drawings
Fig. 1 is the synoptic diagram of the pairing related information logical tree structure of business division knowledge base table;
Fig. 2 is a related information recursive query process flow diagram.
Embodiment
Understand and embodiment of the present invention for the ease of those of ordinary skills, the present invention is made further detailed description below in conjunction with accompanying drawing and embodiment.
Theoretical foundation of the present invention is based upon between the natural all things on earth getting in touch with of the countless ties that all directly or indirectly exist; But to different application requirements, the main body that will pay close attention to often can conclude limited several objects, like this; With limited object is root node; And the information that other is associated just can constitute some inforamtion trees as the child node of its different levels, thereby realizes meeting the orderly tissue of complex information and directly perceived expression of human thinking's custom.
Dendrogram is a kind of important nonlinear data structure, and this structure extensively is present in the objective world.The finite aggregate that one tree (tree) is made up of the individual element of n (n＞0), wherein:
(1) each element is called node (node);
(2) a specific node is arranged, be called root node (root);
(3) except that root node, other node is divided into the individual mutually disjoint finite aggregate of m (m＞=0), and each subclass all is an one tree.
Dendrogram clear layer, relation are understood; Meet people custom is divided in the classification of information, can describe subordinate relation between various information intuitively, logicality is strong; Thereby simplify the understanding of people to complexity, the orderly tissue that helps to realize the unordered information of multi-source is expressed with directly perceived.
Knowledge base (Knowledge Base) is structuring in the knowledge engineering; Easy to operate; Be prone to utilize; Comprehensive organized knowledge cluster is to a certain or needs that some field question is found the solution, and the knowledge sheet that interknits that adopts certain or some kinds of knowledge representation modes in computer memory, stores, organize, manage and use is gathered.These knowledge sheets comprise the knowwhy relevant with the field, factual data, and the heuristic knowledge that is obtained by expertise is like definition relevant in certain field, theorem and algorithm and common sense knowledge etc.From studying objective things in essence, and make up knowledge base, help realizing the ordering of knowledge and information, accelerate flowing of knowledge and information, thereby realize the high-efficiency management of knowledge and information, cooperation and communication capability that enhancing is organized.
Implementation procedure of the present invention is:
(1), analyze business demand, confirm essential all kinds of business information, analyze the significance level of all kinds of business information, and by the arrangement of actual service logic relation and make up business information set V (v
1, v
2... V
n); Clear and definite business division; Carry out the significance level that relates to various information in the process according to practical business, analyze the degree of association between they and business division, direct or indirect hierarchical associated relation between combing business division and all kinds of business information; Structure is the business information incidence relation logic tree model at center with the business division; Each subitem is respectively each child node of incidence relation logic tree in the business information set, and is as shown in Figure 1, v
1, v
2..., v
7ITEM1 shown in the difference corresponding diagram 1, ITEM2 ... ITEM7, wherein ITEM1 is a business division, it generally is associated with the main body essential information; Be the root node of logic tree, ITEM2 wherein ... ITEM7 is a business information, is the child node of logic tree, has the direct writing incidence relation between ITEM2 and ITEM5 and the ITEM1.Have the direct writing incidence relation between ITEM3 and ITEM4 and the ITEM2, and arrive ITEM1 through the ITEM2 indirect association.Have the direct writing incidence relation between ITEM6 and ITEM7 and the ITEM5, and arrive ITEM1 through the ITEM5 indirect association.
(2), according to the business information set V that makes up in the step (1), and actual business requirement designs and makes up all kinds of service information list, confirms each tables of data particular content; With reference to the incidence relation logic tree, put the master slave relation between each tables of data in order, confirm to reach associate field between tables of data, make up related information query tree knowledge base, as shown in table 1 below.
Table 1 tree construction knowledge base table
| NAME | ID | P_ID | FIELD | P_FIELD | TABLE | P_TABLE |
| ITEM1 | 1 | FIELD1 | TABLE1 | |||
| ITEM2 | 2 | 1 | FIELD2 | FIELD2 | TABLE2 | TABLE1 |
| ITEM3 | 3 | 2 | FIELD3 | FIELD3 | TABLE3 | TABLE2 |
| ITEM4 | 4 | 2 | FIELD4 | FIELD4 | TABLE4 | TABLE2 |
| ITEM5 | 5 | 1 | FIELD5 | FIELD5 | TABLE5 | TABLE1 |
| ITEM6 | 6 | 5 | FIELD6 | FIELD6 | TABLE6 | TABLE5 |
| ITEM7 | 7 | 5 | FIELD7 | FIELD7 | TABLE7 | TABLE5 |
NAME field in the tree construction knowledge base table representes to write down the tree node title, directly is shown in bearing-age tree; The id field presentation code is the child node unique identification; The P_ID field is represented the father node coding, is the father node unique identification; The FIELD field is represented the child node associate field, is the associate field of child node corresponding to father node; The P_FIELD field is represented the father node associate field, is the associate field of father node corresponding to child node; The TABLE field is represented child node corresponding data table, for child node corresponding data table name is claimed; The P_TABLE field is represented father node corresponding data table, for father node corresponding data table name is claimed.
(3), the implicit expression logical tree structure information in the search knowledge base, make up business division related information tree, and information such as the corresponding title of each node, coding, associate field, tables of data be stored in this tree node; From the business division root node, obtain node corresponding data table name and claim, obtain associate field, the associated data table name is claimed, inquiry practical business information, and preserve related information; To each child node of business division, adopt recursive fashion to make up related information search algorithm to concrete business division, the recurrence process flow diagram is as shown in Figure 2.
The concrete steps of this related information querying method are following:
(a) information such as the title of input main body sign A and ID, inquiry obtains corresponding record R1, and adds it root node of query tree to;
(b) the corresponding information of inquiry main body sign A in knowledge base, and recurrence layer count value layer is set to 1;
(c) whether child node information N1 is arranged among the judgemental knowledge library inquiry result, N2 ..., Nn, node counts value i is set to 1; Do not equal n if inquire the result or the i of i node, then get into step (d); If inquire the result of i node, then get into step (h);
(d) recurrence layer count value layer is subtracted 1, node counts value i adds 1;
(e) judge recurrence layer count value layer whether greater than 0 and node counts value i smaller or equal to n; If do not satisfy condition then get into step (f), if satisfy condition then get into step (h);
(f) judge whether recurrence layer count value layer equals 0, if do not satisfy condition then get into step (g); Otherwise getting into step (j) algorithm finishes;
(g) whether decision node count value i smaller or equal to n, if satisfy condition then get into step (d); Otherwise getting into step (j) algorithm finishes;
(h) obtain query note Ri, and add tree node to;
(i) continue search knowledge base, obtain the child node information under the present node, and recurrence layer count value layer is added 1, get into step (c) then;
(j) finish.
(4), utilize in the step 3 the related information recursive query algorithm that makes up, according to the user input query condition, inquiry business main body essential information at first; It is incidence relation tree root node; According to the business division essential information, make up the corresponding query statement SQL of next stage child node then, as shown in table 2 below; Inquiry and saving result information go out business division and relevant information thereof by tree construction level recursive query at last.
The corresponding query SQL of table 2 tree node
| NAME | SQL |
| ITEM1 | SELECT*FROM?TABLE1 |
| ITEM2 | SELECT*FROM?TABLE2?WHERE?FIELD2＝TABLE1[FIELD2] |
| ITEM3 | SELECT*FROM?TABLE3?WHERE?FIELD3＝TABLE2[FIELD3] |
| ITEM4 | SELECT*FROM?TABLE4?WHERE?FIELD4＝TABLE2[FIELD4] |
| ITEM5 | SELECT*FROM?TABLE5?WHERE?FIELD5＝TABLE1[FIELD5] |
| ITEM6 | SELECT*FROM?TABLE6?WHERE?FIELD6＝TABLE5[FIELD6] |
| ITEM7 | SELECT*FROM?TABLE7?WHERE?FIELD7＝TABLE5[FIELD7] |
(5), according to incidence relation logic tree model; Arrangement business division related information Query Result, reorganization business division related information makes up the business division inforamtion tree; The various information relevant with concrete business division directly is bundled in each tree node, so that checking fast in the practical application in the future.
(6), later stage such as demand change; Then can be through revising father node coding, associate field, tables of data in the knowledge base; Increase tree node and dispose multiple mode such as father node information and carry out the reorganization of information and the fine setting of function, or reconstruct incidence relation logical tree structure comes quick finished surface to inquire about to the related information of new business demand.
Claims (4)
1. fast construction method based on the generic associative information inquiry of knowledge base tree is characterized in that comprising step:
Step (1) according to the business relations between Various types of data in the target data, is a root node with the business division, makes up the related information logical tree structure of business division;
Step (2), according to said related information logical tree structure, record father node and child node tables of data pointed separately, and mutual associate field;
Step (3) according to associate field information between the tree structure tables of data, makes up related information tree construction knowledge base;
Step (4), according to said related information tree construction construction of knowledge base related information query function, and the generic associative information inquiry of Recursive Implementation loose coupling degree;
Step (5), according to said related information logical tree structure, the related information of reorganization business division.
2. the fast construction method of setting based on the generic associative information inquiry of knowledge base according to claim 1 is characterized in that: the related information tree construction construction of knowledge base method in the step (3) does, at first is that each tree node distributes the unique index value; Write down his father's nodal information then, if certain tree node is not a root node, then write down its parent node index, otherwise be designated as invalid value; Simultaneously, if in the father node representative tables of data in set of fields F1 and the child node representative tables of data set of fields F2 have certain incidence relation, then write down F1 and F2 respectively, as the foundation that dynamically associates in the future inquiry.
3. according to any described fast construction method of setting based on the generic associative information inquiry of knowledge base among the claim 1-2, it is characterized in that: the recursive query method of the related information in the step (4) comprises step:
Step (a) inquires the target service main body by external information, creates root node, and Query Result information is bundled in the root node of new establishment, and the setting root node is a present node;
Step (b) travels through said related information tree construction knowledge base, and the child node B that searches present node A describes set; If child node B set is not empty; Execution in step (c), otherwise return the last layer tree, recurrence is handled other nodes successively; All child nodes under root node all dispose, execution in step (d);
Step (c) is described from said related information tree construction knowledge base circulation extraction child node B (i), and and present node between associate field; And be constraint condition with present node information, from said related information tree construction knowledge base, inquire about B (i) indication tables of data record, if there is qualified table record; Then under present node, create new node, Query Result is bundled in new establishment child node, setting newly-built node is present node; And execution in step (b), otherwise handle next node B (i+1), up to B0 ... All nodes of Bn all dispose; Return the last layer tree, recurrence is handled other nodes successively;
Step (d), the related information tree makes up and finishes, and accomplishes the related information inquiry.
4. according to any described fast construction method of setting based on the generic associative information inquiry of knowledge base among the claim 1-3; It is characterized in that further comprising: when the change of business information query demand; Adjust tree construction knowledge base content through the direct modification knowledge base, with the new business information correlation inquiry demand of self-adaptation.
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