CN102402599A - Dynamic maintenance system for large-scale semantic knowledge base - Google Patents
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Abstract
The invention discloses a dynamic maintenance system for a large-scale semantic knowledge base. The dynamic maintenance system comprises a main server, a distributed data server and an ontology knowledge base, wherein the ontology knowledge base stores ontology which is described by using a web ontology language (OWL). The system is characterized by also comprising a global knowledge management system which is arranged on the main server and a local data management system which is arranged on the distributed data server of the system, wherein the global knowledge management system interacts with the local data management system through a knowledge bus controller by using a set of standard knowledge communication instructions. Compared with the prior art, the dynamic maintenance system for the large-scale semantic knowledge base has the advantage that: the ontology of the large-scale semantic knowledge base is optimized, so that the storage scale is reduced, and inference efficiency and query efficiency are optimized.
Description
Technical field
The present invention relates to network information treatment technology, particularly relate to a kind of dynamic maintaining system of the isomery semantic knowledge-base based on the Web2.0 network.
Background technology
Involved in the present invention have a following technology:
1. the knowledge base of Semantic Web
Year surplus Semantic Web has developed ten.The research of the representation of knowledge, knowledge store and Semantic Web Technology such as inquiry, knowledge reasoning has had significant progress.But compare with Traditional Web, the knowledge base scale of Semantic Web also differs greatly, and ontology knowledge storehouse phoenix feathers and unicorn horns that can practical application is difficult to satisfy the demand of Semantic Web to extensive knowledge base.Trace it to its cause, the construction cost of body, efficient, and the management problems of body all is the obstacle of restriction semantic knowledge-base development.Body is the main body that constitutes knowledge base; The approach that makes up body at present mainly is through the manual body of making by the domain expert; Not only cost is high, efficient is low, and safeguards very difficulty, also has the method for researchist through statistical study and natural language processing that the existing Web page is marked or extracts and obtains semantic knowledge; But semantic recognition accuracy is not high, has caused the body quality low.So far people do not find a kind of approach that can continue, efficiently, high-quality make up body as yet.Yet; Bottom-up construction Semantic Web has obtained increasing common recognition; Begin from related a large amount of open data set simply, accumulate gradually and enrich its semanteme, started to walk and obtained significant progress thereby make up the work that has semantic Web knowledge base.
2. distributed body research
The development of Semantic Web is based upon on the Web2.0 basis from the beginning, will comprise the semantic data of magnanimity.In addition; In the semantic early stage of development; Numerous standards has formed a large amount of isomery semantic data sources with technology; How to unite isomery semantic data source and in the magnanimity semantic data, inquire about the attention that has more and more received the scientific research personnel with reasoning, yet because large-scale Semantic Web knowledge base is not set up as yet, therefore the research to distributed body also is difficult to find center of effort.But some thoughts and the framework that put forward under study for action still can be used for reference, with solving the difficult problem under the distributed bulk conditions.
3. knowledge base maintenance research
The maintenance of semantic knowledge-base mainly is meant the management to a large amount of isomery semantic data source, so that whole semantic knowledge-base keeps upper strata semantic net to use the required consistance and the efficient of reasoning and inquiry.Around knowledge base inconsistency processing aspect, produced each research field, at present like inconsistency detection, inconsistency reasoning, inconsistency debugging etc.; Promoting on reasoning and the search efficiency; Pair improvement of ontology inference search algorithm is arranged on the one hand, as centering on description logic reasoning algorithm tabular optimization Algorithm, to the optimization of SPARQL inquiry mechanism; Have on the other hand through excavation data relationship between the body; Optimize the storage organization of body in the knowledge base, and then improve the efficient of reasoning inquiry, cut apart like body merging, body.
Difficult problem to the Dynamic Maintenance of extensive semantic knowledge-base; How various body maintenance algorithms are combined and make up one and can provide the inconsistency body to handle, and the management interface that can dynamically optimize body storage organization in the knowledge base is this area problem to be solved.
Summary of the invention
Based on above-mentioned prior art; The present invention proposes a kind of dynamic maintaining system of extensive semantic knowledge-base; In conjunction with body merge algorithm, body inconsistency Processing Algorithm and body partitioning algorithm; Inconsistency Processing Algorithm and extensive semantic knowledge-base are integrated, thereby realized the coherency management of extensive semantic knowledge-base.
The present invention proposes a kind of dynamic maintaining system of extensive semantic knowledge-base; This system comprises the ontology knowledge storehouse that the OWL language description is adopted in master server, distributed data server and storage; It is characterized in that; This system also comprises global knowledge management system that is deployed on the master server and the local data's management system that is deployed in the distributed data server of this system; Said global knowledge management system and said local data management system are carried out alternately with the instruction of standard set knowledge communication through the knowledge bus controller, wherein:
Local data's management system; Be used to monitor the instruction that sends through bus MULE and return the body deal with data according to the control flow commands that bus is returned; Realize the storage and management of semantic knowledge-base Dynamic Maintenance algorithm data, comprise body merging, cut apart and inconsistency is handled;
The global knowledge management system is used for pool, maintenance and applied ontology knowledge base, and this system comprises:
Localized services device index; This index is used for writing down the information of the mark of the localized services device that is positioned on the assistance data server; Be positioned at the global knowledge management system operation core processing of being responsible for pool, maintenance and applied ontology knowledge base on the master server then; According to requesting query respective index information, and send multicast and broadcasting instructions;
The API interpreter is used for the request from the function API on upper strata is construed to the core query statement on basis the back-up system operation;
The global knowledge database management module runs on the master server backstage, is used to carry out reasoning of ontology knowledge storehouse and evolution algorithmic, optimizes knowledge base structure and storage, realizes that consistent this stereogram extracts, body merges, body is cut apart, load balancing, and specific algorithm is following:
The body merge algorithm; At first find the association of entity between two input bodies, specify a body A then, another body B is imported among the body A as element body; In merging body B, add related the description then, then obtained the body result after the required merging;
Body inconsistency Processing Algorithm, to each inconsistent body, that calculates inconsistent body can not satisfy concept set; Read the minimum inconsistent sub-body that each can not satisfy notion: from each inconsistent sub-body, extract a tlv triple, have crossingly like the fruit body, then extract the tlv triple of intersection, form triplet sets; From former body, remove this triplet sets, make all inconsistent bodies consistent; Obtain maximum consistent sub-body;
The body partitioning algorithm, a given body at first is translated into graphic structure, judges its whether full-mesh then, if full-mesh then calculate minimal cut set comprises cutpoint and cut edge; Cut apart according to cut set; If not full-mesh, then calculate very big connected subgraph, cut apart according to subgraph then.
The identification information of said localized services device comprises like mailing address, body tabulation, localized services device status information.
The said step that finds the association of entity between two input bodies; Specifically comprise: based on the entity character string apart from the structure distance matrix; Through character string distance calculation algorithm, try to achieve each to the distance between the entity, nearest entity is promptly thought related entity.
Symbol string distance calculation algorithm among the said character string distance calculation algorithm use ontosim.
The said steps of association that find entity between two input bodies, the concrete realization of this step may further comprise the steps: through to the calling of outside semantic tools, find the degree of association between the physical name, the degree of association is minimum promptly thinks related entity;
Said external semantic instrument is WordNet or Wikipedia.
The said instruction that sends through bus MULE, this command file name must be consistent with ontologyURI.
Compared with prior art, the present invention can realize the optimization to extensive semantic knowledge-base body through the optimized Algorithm on platform and the platform, thereby reduces storage size, and the efficient of optimizing reasoning and inquiry.
Description of drawings
Fig. 1 is distributed body management architectural framework figure;
Fig. 2 is a body merge algorithm process flow diagram;
Fig. 3 is a body inconsistency Processing Algorithm process flow diagram;
Fig. 4 is a body partitioning algorithm process flow diagram;
Embodiment
At first, the present invention has utilized the body merge algorithm.Because isomery between the ontology data source and notion is overlapping; Find Structural Interrelationship between the body; The possibility thereby the efficient that reduces storage size optimization reasoning and inquiry just becomes; This algorithm is mainly through the distance between the entity of seeking body, thus the description of interpolation entity relationship in the body after merging, and then set up the association between the body.
Secondly, the present invention has utilized body inconsistency Processing Algorithm.There is imperfection and knowledge dynamic evolution property in time in knowledge itself in the reality open world, so the appearance of inconsistency is inevitable.Solve these problems through the present invention proposes a kind of algorithm that extracts maximum consistent sub-body.Body in the present invention is the language that adopts based on description logic---OWL describes.And, this algorithm and bottom knowledge base are combined, thereby have realized the coherency management in global knowledge storehouse under the distributed environment.
And the present invention has also utilized the application of body partitioning algorithm.In the ontology knowledge storehouse, some extensive bodies appear sometimes, and the appearance of these bodies has remarkable influence to the efficient of knowledge base reasoning and inquiry, just can greatly improve the efficient of reasoning and inquiry through being divided into some small-scale bodies.
The function of each algorithm is following:
One, body merge algorithm.To any two given OWL bodies, find the association (subclass or equivalence) between their entity (class, attribute, instance).
Two, body inconsistency Processing Algorithm.To any given inconsistent body, return an interim maximum consistent sub-body.
Three, body partitioning algorithm.To the OWL body of any rule, return its minimal cut set.
Algorithm demonstration platform combines above three kinds of algorithms; Thereby can test these algorithms more easily; On the basis of this external this platform and in the global design to extensive semantic knowledge-base; The inconsistency Processing Algorithm is incorporated into the knowledge-base management interface, has realized coherency management extensive semantic knowledge-base.
Below description through embodiment, further specify technical scheme of the present invention:
1. extensive semantic knowledge-base design
We are designed to this extensive semantic knowledge-base to be controlled by a master server pattern of a plurality of assistance data server.
As shown in Figure 1, this distributed body management architectural framework is the border with the knowledge bus controller, is divided into two parts.Top is the global knowledge management system, and following is the local data's management system that is positioned on the distributed data server.These two parts are carried out mutual through knowledge bus controller and the instruction of standard set knowledge communication, finally accomplish knowledge-base management work.Local data's management system is positioned on the assistance data server, is responsible for monitoring the instruction that bus transmits, and returns desired data, safeguards the storage and management of local data.The global knowledge management system then is positioned on the master server, is responsible for the function of pool, maintenance and applied ontology knowledge base.At first this system's localized services device index of needs is used for writing down the information of localized services device, like information such as mailing address, body tabulation, localized services device states.Inquiry operation kernel program need be inquired about this index information then, and sends multicast and broadcasting instructions.The API interpreter is equivalent to an adapter, with the omnifarious API request in upper strata, is construed to the core query statement on basis, comes the back-up system operation with this.The global knowledge librarian then is a special program; It ceaselessly runs on the master server backstage; And execution ontology knowledge storehouse reasoning and evolution algorithmic; Constantly optimize knowledge base structure and storage, its effect should comprise that consistent this stereogram extracts, body merges, body is cut apart, load balancing etc.
2. body merge algorithm
As shown in Figure 2; Be body merge algorithm flow process, when using Jena to carry out the merging of body, at first find the association of entity between two input bodies; Specify a body A as element body then; Another body B is imported among the body A, in merging body B, add related the description then, then obtained the body result after the required merging.May there be the situation of inconsistency (Inconsistency) in body after the merging, at this moment can adopt Jena or Pellet to carry out consistency checking.For example, in body B, added the related deduce machine interface (Reasoner) that just can call later Jena or Pellet of describing, carried out consistency checking.If inconsistent, could be the appearance which notion has caused the body inconsistency then through deduce machine interface (Reasoner) check of calling Jena or Pellet once more.
The core of body merge algorithm and main calculated amount concentrate on the entity associated of seeking between the body.Seek related principle and mainly contain two types: based on entity character string distance, by external semantic instrument (like WordNet, Wikipedia).Method based on first kind principle mainly is distance matrix of structure, through calculating character string similarity algorithm, tries to achieve each to the distance between the physical name, and nearest entity is promptly thought related entity.In ontosim, the author provides the algorithm of multiple calculating character string distance, and these algorithms can combine with Alignment API very easily.By the help of this API, the result that we can more various algorithms, and with functions such as standard results compares.In the present invention, in order to simplify above-mentioned character string distance algorithm, suppose that then distance is 0, otherwise is 1 if character string equates.
Based on the method for second kind of principle then is through to the calling of outside semantic tools, and finds the degree of association between the physical name, and the degree of association is minimum promptly thinks related entity.For example; The BLOOMS system has utilized the categorizing system (category hierarchy) of Wikipedia; To every pair of class name that be about to merge, the service (Webservice) of calling Wikipedia obtains the kind (category) under its, and it is 4 tree that recurrence obtains a height; Registration between relatively these are set, the relation between type of obtaining equates, subclass or irrelevant.In addition, in Alignment API, utilize the merge algorithm of WordNet can find two semantic distances between the notion easily.These two kinds of principles have all obtained reasonable effect in reality.
3. body inconsistency Processing Algorithm
The body that the present invention considers is the body that adopts the OWL language description, and the logical foundations that the OWL language is followed is the subclass of description logic.In logic, contradiction can be derived all, thereby an inconsistent body can not directly be used for reasoning.But in the actual environment, because the imperfection of knowledge and dynamic evolution property in time make that the inconsistency of knowledge is inevitable.Thereby we need seek a kind of method and solve the ontology inference under inconsistent environment.A kind of method commonly used is exactly the consistent sub-body of interim maximum of structure inconsistent body.
As shown in Figure 3, for calculating the algorithm flow of minimum inconsistent sub-body.In brief, to each inconsistent body, the first step, that calculates inconsistent body can not satisfy concept set; In second step, read the minimum inconsistent sub-body that each can not satisfy notion: from each inconsistent sub-body, extract a tlv triple (have like the fruit body crossing, as then to extract the tlv triple of intersection), the composition triplet sets; From former body, remove this triplet sets, make all inconsistent bodies consistent, promptly obtained the sub-body of maximum unanimity;
For example, the smallest subset O ' of location body O makes O ' can not satisfy notion C.In other words, notion C is met in any maximum proper subclass of body O, and in smallest subset O ', can not satisfy.This algorithm will can be used for definite upper limit that extracts the scale of consistent body.
4. body partitioning algorithm
The ontology data that has various scales in the reality Web environment, the particularly open body of some professional associations.If these extensive bodies are added in the ontology knowledge storehouse, must be divided into small scale bulk, with the storage that helps knowledge base and the efficient of reasoning.If regard class as node, the relation (subclass, mutual exclusion etc.) between type is regarded the limit as, and then its storage organization is exactly a figure.When realizing the body partitioning algorithm, can regard this view as non-directed graph, and use for reference in the graph theory and ask the algorithm of cutpoint (articulation point) to cut apart.The figure here is meant a kind of complex data structures.Relation between data element is arbitrarily.Other data structures (like tree, linear list etc.) all have clear and definite condition restriction, and all can be associated between any two data elements in the graphic structure.
As shown in Figure 4, be body partitioning algorithm flow process.The view of a given body at first is translated into graphic structure, judges its whether full-mesh then, if full-mesh then calculate minimal cut set (comprising cutpoint and cut edge) is cut apart according to cut set; If not full-mesh, then calculate very big connected subgraph, cut apart according to subgraph then.
The main process of this body partitioning algorithm is asked articulation point exactly.The process of asking articulation point is exactly the process of a depth-first traversal, and the time complexity of this algorithm is O (n+e) (wherein n is the node number, and e is the limit number).This algorithm is only to the division on the body construction; If combine the consideration of the information such as semanteme of body again; The present invention has proposed the expression of a Modular Ontology and the framework of reasoning in addition; This modulate expression can be regarded the further in-depth that body is cut apart as, and body moduleization of the present invention has following three characteristics: loose coupling property (loose coupling), self-contained nature (self-containment) and integrality (integrity).
Claims (7)
1. the dynamic maintaining system of an extensive semantic knowledge-base; This system comprises the ontology knowledge storehouse that the OWL language description is adopted in master server, distributed data server and storage; It is characterized in that; This system also comprises global knowledge management system that is deployed on the master server and the local data's management system that is deployed in the distributed data server of this system; Said global knowledge management system and said local data management system are carried out alternately with the instruction of standard set knowledge communication through the knowledge bus controller, wherein:
Local data's management system; Be used to monitor the instruction that sends through bus MULE and return the body deal with data according to the control flow commands that bus is returned; Realize the storage and management of semantic knowledge-base Dynamic Maintenance algorithm data, comprise body merging, cut apart and inconsistency is handled;
The global knowledge management system is used for pool, maintenance and applied ontology knowledge base, and this system comprises:
Localized services device index; This index is used for writing down the information of the mark of the localized services device that is positioned on the assistance data server; Be positioned at the global knowledge management system operation core processing of being responsible for pool, maintenance and applied ontology knowledge base on the master server then; According to requesting query respective index information, and send multicast and broadcasting instructions;
The API interpreter is used for the request from the function API on upper strata is construed to the core query statement on basis the back-up system operation;
The global knowledge database management module runs on the master server backstage, is used to carry out reasoning of ontology knowledge storehouse and evolution algorithmic, optimizes knowledge base structure and storage, realizes that consistent this stereogram extracts, body merges, body is cut apart, load balancing, and specific algorithm is following:
The body merge algorithm; At first find the association of entity between two input bodies, specify a body A then, another body B is imported among the body A as element body; In merging body B, add related the description then, then obtained the body result after the required merging;
Body inconsistency Processing Algorithm, to each inconsistent body, that calculates inconsistent body can not satisfy concept set; Read the minimum inconsistent sub-body that each can not satisfy notion: from each inconsistent sub-body, extract a tlv triple, have crossingly like the fruit body, then extract the tlv triple of intersection, form triplet sets; From former body, remove this triplet sets, make all inconsistent bodies consistent; Obtain maximum consistent sub-body;
The body partitioning algorithm, a given body at first is translated into graphic structure, judges its whether full-mesh then, if full-mesh then calculate minimal cut set comprises cutpoint and cut edge; Cut apart according to cut set; If not full-mesh, then calculate very big connected subgraph, cut apart according to subgraph then.
2. the dynamic maintaining system of extensive semantic knowledge-base as claimed in claim 1 is characterized in that, the identification information of said localized services device comprises like mailing address, body tabulation, localized services device status information.
3. the dynamic maintaining system of extensive semantic knowledge-base as claimed in claim 1; It is characterized in that; The said steps of association that find entity between two input bodies specifically comprise: based on the entity character string apart from distance matrix of structure, through character string distance calculation algorithm; Try to achieve each to the distance between the entity, nearest entity is promptly thought related entity.
4. the dynamic maintaining system of extensive semantic knowledge-base as claimed in claim 3 is characterized in that, the symbol string distance calculation algorithm among the said character string distance calculation algorithm use ontosim.
5. the dynamic maintaining system of extensive semantic knowledge-base as claimed in claim 1; It is characterized in that; The said step that finds the association of entity between two input bodies; The concrete realization of this step may further comprise the steps: through to the calling of outside semantic tools, find the degree of association between the physical name, the degree of association is minimum promptly thinks related entity;
6. the dynamic maintaining system of extensive semantic knowledge-base as claimed in claim 1 is characterized in that, said external semantic instrument is WordNet or Wikipedia.
7. the dynamic maintaining system of extensive semantic knowledge-base as claimed in claim 1 is characterized in that, the said instruction that sends through bus MULE, and this command file name must be consistent with ontologyURI.
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| CN102981913A (en) * | 2012-12-04 | 2013-03-20 | 公安部第三研究所 | Inference control method and inference control system with support on large-scale distributed incremental computation |
| CN103500208A (en) * | 2013-09-30 | 2014-01-08 | 中国科学院自动化研究所 | Deep layer data processing method and system combined with knowledge base |
| CN104915717A (en) * | 2015-06-02 | 2015-09-16 | 百度在线网络技术（北京）有限公司 | Data processing method, knowledge base reasoning method and related device |
| CN107133671A (en) * | 2017-05-26 | 2017-09-05 | 西南大学 | Citrus knowledge Modeling and extensive Ontology learning method based on agriculture Eight Terms Guidelines |
| CN107133283A (en) * | 2017-04-17 | 2017-09-05 | 北京科技大学 | A kind of Legal ontology knowledge base method for auto constructing |
| CN107247738A (en) * | 2017-05-10 | 2017-10-13 | 浙江大学 | A kind of extensive knowledge mapping semantic query method based on spark |
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| CN102981913A (en) * | 2012-12-04 | 2013-03-20 | 公安部第三研究所 | Inference control method and inference control system with support on large-scale distributed incremental computation |
| CN102981913B (en) * | 2012-12-04 | 2015-04-08 | 公安部第三研究所 | Inference control method and inference control system with support on large-scale distributed incremental computation |
| CN103500208A (en) * | 2013-09-30 | 2014-01-08 | 中国科学院自动化研究所 | Deep layer data processing method and system combined with knowledge base |
| CN103500208B (en) * | 2013-09-30 | 2016-08-17 | 中国科学院自动化研究所 | Deep layer data processing method and system in conjunction with knowledge base |
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| CN107133283A (en) * | 2017-04-17 | 2017-09-05 | 北京科技大学 | A kind of Legal ontology knowledge base method for auto constructing |
| CN107247738A (en) * | 2017-05-10 | 2017-10-13 | 浙江大学 | A kind of extensive knowledge mapping semantic query method based on spark |
| CN107247738B (en) * | 2017-05-10 | 2019-09-06 | 浙江大学 | A kind of extensive knowledge mapping semantic query method based on spark |
| CN107133671A (en) * | 2017-05-26 | 2017-09-05 | 西南大学 | Citrus knowledge Modeling and extensive Ontology learning method based on agriculture Eight Terms Guidelines |
| CN110738493A (en) * | 2018-07-19 | 2020-01-31 | 上海交通大学 | Body maintenance system based on block chain |
| CN110738493B (en) * | 2018-07-19 | 2023-04-18 | 上海交通大学 | Body maintenance system based on block chain |
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