CN102402599B - 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 the network information processing 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
Semantic Web developed for more than ten years.The research of the representation of knowledge, knowledge store and the 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 Semantic Web to the demand of extensive knowledge base.Trace it to its cause, the construction cost of body, efficient, and the management problems of body is all the obstacle of restriction semantic knowledge-base development.Body is the main body that consists of knowledge base, the approach that builds at present body is mainly to make body by the domain expert by craft, not only cost is high, efficient is low, and safeguard very difficult, also there is the researchist by the method for statistical study and natural language processing, the existing Web page to be marked or extract to obtain semantic knowledge, but semantic recognition accuracy is not high, has caused this weight low.So far people not yet find a kind ofly can continue, efficiently, high-quality ground builds the approach of body.Yet, bottom-up construction Semantic Web has obtained increasing common recognition, from related a large amount of open data set simply, accumulate gradually and enrich its semanteme, thereby the work that builds with the Web knowledge base of semanteme has started to walk and obtained significant progress.
2. Distributed Ontologies 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 and technology have formed a large amount of isomery semantic data sources, how to unite isomery semantic data source and inquire about in mass semantic data with reasoning and more and more be subject to scientific research personnel's attention, yet because large-scale Semantic Web knowledge base is not yet set up, therefore the research of Distributed Ontologies 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 a difficult problem that solves under the Distributed Ontologies condition.
3. knowledge base maintenance research
The maintenance of semantic knowledge-base mainly refers to the management to a large amount of isomery semantic data source, so that whole semantic knowledge-base keeps upper strata semantic net to use required consistance and the efficient of reasoning and inquiry.At present around knowledge base inconsistency handling aspect, produced each research field, as inconsistency detection, inconsistency reasoning, inconsistency debugging etc., promoting on reasoning and search efficiency, pair improvement of ontology inference search algorithm is arranged on the one hand, as the optimization around description logics reasoning algorithm tabular algorithm, optimization to the SPARQL inquiry mechanism, have on the other hand by the excavation to data relationship between body, optimize the storage organization of body in knowledge base, and then improve the efficient of reasoning inquiry, cut apart as ontology merging, body.
A difficult problem for the Dynamic Maintenance of extensive semantic knowledge-base, how various body maintenance algorithms are combined and build one and can provide the inconsistency body to process, and the management interface that can dynamically optimize body storage organization in 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 ontology merging algorithm, body inconsistency handling algorithm and body partitioning algorithm, inconsistency handling 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 database service device and storage, it is characterized in that, this system also comprises the global knowledge management system that is deployed on master server and is deployed in local data's management system of the Distributed database service device of this system, described global knowledge management system and described local data management system are by the knowledge bus controller, carry out alternately with the instruction of standard set knowledge communication, wherein:
Local data's management system, be used for monitoring the instruction that sends by bus MULE and return to the body deal with data according to the control stream instruction that bus is returned, realize the storage and management of semantic knowledge-base Dynamic Maintenance algorithm data, comprise body merging, cut apart and inconsistency handling;
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 recording the information of the mark of the localized services device that is positioned on assistance data server, then be positioned at the global knowledge management system operation core processing of being responsible for pool, maintenance and applied ontology knowledge base on master server, according to requesting query respective index information, and send multicast and broadcasting instructions;
The API interpreter is used for and will from the request of the function API on upper strata, be 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 for carrying out ontology knowledge storehouse reasoning and evolution algorithmic, optimizes knowledge base structure and storage, realizes that consistent this stereogram extraction, ontology merging, body are cut apart, load balancing, and specific algorithm is as follows:
The ontology merging algorithm, at first find the association of entity between two input bodies, then specify a body A as element body, another body B is imported in body A, then add related the description in merging body B, obtained the body result after required merging;
Body inconsistency handling 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 concept: extract a tlv triple from each inconsistent sub-body, have crossingly as the fruit body, extract the tlv triple of intersection, form triplet sets; Remove this triplet sets from former body, make all inconsistent bodies consistent; Obtain maximum consistent sub-body;
The body partitioning algorithm, at first a given body is translated into graphic structure, then judges its whether full-mesh, if full-mesh calculate minimal cut set comprises cutpoint and cut edge; Cut apart according to cut set; If not full-mesh, calculate very big connected subgraph, then cut apart according to subgraph.
The identification information of described localized services device comprises as mailing address, body list, localized services device status information.
The described step that finds the association of entity between two input bodies, specifically comprise: based on distance matrix of entity String distance structure, by the String distance computational algorithm, try to achieve the distance between every a pair of entity, nearest entity is namely thought related entity.
Symbol string in described String distance computational algorithm employing ontosim is apart from computational algorithm.
The described steps of association that find entity between two input bodies, the specific implementation of this step comprises the following steps: by to the calling of outside semantic tools, find the degree of association between physical name, degree of association minimum namely think related entity;
Described external semantic instrument is WordNet or Wikipedia.
The described instruction that sends by 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 by the optimized algorithm on platform and platform, thereby reduces storage size, and the efficient of optimizing reasoning and inquiry.
Description of drawings
Fig. 1 is Distributed Ontologies management architecture figure;
Fig. 2 is the ontology merging algorithm flow chart;
Fig. 3 is body inconsistency handling algorithm flow chart;
Fig. 4 is body partitioning algorithm process flow diagram;
Embodiment
At first, the present invention has utilized the ontology merging algorithm.Overlapping due to the isomery between the ontology data source and concept, find the association of structure between body, the possibility thereby the efficient that reduces storage size optimization reasoning and inquiry just becomes, this algorithm is mainly by the distance between the entity of seeking body, thereby add the description of entity relationship in the body after merging, and then set up the association between body.
Secondly, the present invention has utilized body inconsistency handling algorithm.There is imperfection and knowledge dynamic evolution in time in knowledge itself in the reality open world, so the appearance of inconsistency is inevitable.Solve these problems by 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 of global knowledge base 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, the appearance of these bodies has significant impact to the efficient of Analysis of Knowledge Bases Reasoning and inquiry, by be divided into some on a small scale bodies just can greatly improve the efficient of reasoning and inquiry.
The function of each algorithm is as follows:
One, ontology merging algorithm.To two any given OWL bodies, find the association (subclass or equivalence) between their entity (class, attribute, example).
Two, body inconsistency handling algorithm.To an any given inconsistent body, return to an interim maximum consistent sub-body.
Three, body partitioning algorithm.To any one regular OWL body, return to 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, with the management interface of inconsistency handling Algorithms Integration to knowledge base, realized the coherency management to extensive semantic knowledge-base.
Below description by embodiment, further illustrate 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 Ontologies management architecture is divided into two parts take the knowledge bus controller as the border.Top is the global knowledge management system, and following is the local data's management system that is positioned on the Distributed database service device.These two parts are undertaken finally completing the management work of knowledge base alternately by knowledge bus controller and the instruction of standard set knowledge communication.Local data's management system is positioned on assistance data server, is responsible for monitoring the instruction that bus transmits, and returns to desired data, safeguards the storage and management of local data.The global knowledge management system is positioned on 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 recording the information of localized services device, as information such as mailing address, body list, localized services device states.Then inquiry operation kernel program need to be inquired about this index information, 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 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 extraction, ontology merging, body are cut apart, load balancing etc.
2. ontology merging algorithm
As shown in Figure 2, be the ontology merging algorithm flow, when using Jena to carry out the merging of body, at first find the association of entity between two input bodies, then specify a body A as element body, another body B is imported in body A, then add related the description in merging body B, obtained the body result after required merging.May there be the situation of inconsistency (Inconsistency) in body after merging, at this moment can adopt Jena or Pellet to carry out consistency checking.For example, added the related deduce machine interface (Reasoner) that just can call later Jena or Pellet of describing in body B, carried out consistency checking.If inconsistent, could be the appearance which concept has caused the body inconsistency by deduce machine interface (Reasoner) check of again calling Jena or Pellet.
The core of ontology merging algorithm and main calculated amount concentrate on the entity associated of seeking between body.Seek related principle and mainly contain two classes: based on the entity String distance, by external semantic instrument (as WordNet, Wikipedia).Method based on first kind principle is mainly distance matrix of structure, by calculating character string similarity algorithm, tries to achieve the distance between every a pair of physical name, and nearest entity is namely thought related entity.In ontosim, the author provides the algorithm of multiple calculating character string distance, and these algorithms can be combined with Alignment API very easily.By the help of this API, the result that we can more various algorithms, and with the functions such as standard results compares.In the present invention, in order to simplify above-mentioned String distance algorithm, suppose that distance is 0, otherwise is 1 if character string equates.
Be by to the calling of outside semantic tools based on the method for the second principle, find the degree of association between physical name, degree of association minimum namely think related entity.For example, the BLOOMS system has utilized the categorizing system (category hierarchy) of Wikipedia, to every pair of class name that is about to merging, the service (Webservice) of calling Wikipedia obtains its affiliated kind (category), it is 4 tree that recurrence obtains a height, the registration between these trees relatively, the relation between class of obtaining equates, subclass or irrelevant.In addition, in Alignment API, utilize the merge algorithm of WordNet can find easily two semantic distances between concept.These two kinds of principles have all obtained reasonable effect in practice.
3. body inconsistency handling 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 subset of description logic.In logic, contradiction can be derived all, thereby an inconsistent body can not directly be used for reasoning.But in actual environment, because the imperfection of knowledge and dynamic evolution in time make the inconsistency of knowledge inevitable.Thereby we need to seek a kind of method and solve 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; Second step reads the minimum inconsistent sub-body that each can not satisfy concept: extract a tlv triple (have as the fruit body crossing, as to extract the tlv triple of intersection) from each inconsistent sub-body, form triplet sets; Remove this triplet sets from former body, make all inconsistent bodies consistent, namely obtained maximum consistent sub-body;
For example, the smallest subset O ' of location body O makes O ' can not satisfy concept C.In other words, concept C is met in any one maximum proper subclass of body O, and can not satisfy in smallest subset O '.This algorithm will can be used for definite upper limit that extracts the scale of consistent body.
4. body partitioning algorithm
There are the ontology data of various scales, the particularly open body of some professional associations in reality Web environment.If these extensive bodies are added in an ontology knowledge storehouse, must be divided into small scale bulk, with the storage that is conducive to knowledge base and the efficient of reasoning.If regard class as node, the relation between class (subclass, mutual exclusion etc.) is regarded the limit as, and 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 graph theory and ask the algorithm of cutpoint (articulation point) to cut apart.The figure here refers to a kind of data structure of complexity.Relation between data element is arbitrarily.Other data structures (as tree, linear list etc.) have clear and definite condition restriction, and all can be associated between any two data elements in graphic structure.
As shown in Figure 4, be body partitioning algorithm flow process.At first the view of a given body is translated into graphic structure, then judges its whether full-mesh, if full-mesh calculate minimal cut set (comprising cutpoint and cut edge) is cut apart according to cut set; If not full-mesh, calculate very big connected subgraph, then cut apart according to subgraph.
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 nodes, and e is the limit number).This algorithm is only for the division on body construction; If again in conjunction with the consideration of the information such as semanteme of body, the present invention has proposed the framework of the expression and inference of a Modular Ontology in addition, this modulate expression can be regarded the further in-depth that body is cut apart as, and ontology modularization of the present invention has following three features: loose coupling (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 database service device and storage, it is characterized in that, this system also comprises the global knowledge management system that is deployed on master server and is deployed in local data's management system of the Distributed database service device of this system, described global knowledge management system and described local data management system are by the knowledge bus controller, carry out alternately with the instruction of standard set knowledge communication, wherein:
Local data's management system, be used for monitoring the instruction that sends by bus MULE and return to the body deal with data according to the control stream instruction that bus is returned, realize the storage and management of semantic knowledge-base Dynamic Maintenance algorithm data, comprise body merging, cut apart and inconsistency handling;
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 recording the information of the mark of the localized services device that is positioned on assistance data server, then be positioned at the global knowledge management system operation core processing of being responsible for pool, maintenance and applied ontology knowledge base on master server, according to requesting query respective index information, and send multicast and broadcasting instructions;
The API interpreter is used for and will from the request of the function API on upper strata, be 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 for carrying out ontology knowledge storehouse reasoning and evolution algorithmic, optimizes knowledge base structure and storage, realizes that consistent this stereogram extraction, ontology merging, body are cut apart, load balancing, and specific algorithm is as follows:
The ontology merging algorithm, at first find the association of entity between two input bodies, then specify a body A as element body, another body B is imported in body A, then add related the description in merging body B, obtained the body result after required merging;
Body inconsistency handling 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 concept: extract a tlv triple from each inconsistent sub-body, have crossingly as the fruit body, extract the tlv triple of intersection, form triplet sets; Remove this triplet sets from former body, make all inconsistent bodies consistent; Obtain maximum consistent sub-body;
The body partitioning algorithm, at first a given body is translated into graphic structure, then judges its whether full-mesh, if full-mesh calculate minimal cut set comprises cutpoint and cut edge; Cut apart according to cut set; If not full-mesh, calculate very big connected subgraph, then cut apart according to subgraph.
2. the dynamic maintaining system of extensive semantic knowledge-base as claimed in claim 1, is characterized in that, the identification information of described localized services device comprises as mailing address, body list, 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 described step that finds the association of entity between two input bodies, specifically comprise: based on distance matrix of entity String distance structure, by the String distance computational algorithm, try to achieve the distance between every a pair of entity, nearest entity is namely 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 in described String distance computational algorithm employing ontosim is apart from computational algorithm.
5. the dynamic maintaining system of extensive semantic knowledge-base as claimed in claim 1, it is characterized in that, the described step that finds the association of entity between two input bodies, the specific implementation of this step comprises the following steps: by calling outside semantic tools, find the degree of association between physical name, degree of association minimum namely think related entity.
6. the dynamic maintaining system of extensive semantic knowledge-base as claimed in claim 5, is characterized in that, described 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 described instruction that sends by bus MULE, and this command file name must be consistent with ontologyURI.
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| 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 |
| 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 |
| CN104915717B (en) * | 2015-06-02 | 2017-11-14 | 百度在线网络技术（北京）有限公司 | Data processing method, Analysis of Knowledge Bases Reasoning method and relevant apparatus |
| 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 |
| 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 |
| CN110738493B (en) * | 2018-07-19 | 2023-04-18 | 上海交通大学 | Body maintenance system based on block chain |
| CN110275919B (en) * | 2019-06-18 | 2021-06-18 | 合肥工业大学 | Data integration method and device |
| CN113343693A (en) * | 2020-03-03 | 2021-09-03 | 阿里巴巴集团控股有限公司 | Named entity identification method, device, equipment and machine readable medium |
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