CN106570566B - A kind of tea-plant pests representation of knowledge and sharing method based on ontology - Google Patents
A kind of tea-plant pests representation of knowledge and sharing method based on ontology Download PDFInfo
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- 241001046947 Ectropis obliqua Species 0.000 description 2
- 241001012098 Omiodes indicata Species 0.000 description 2
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- 230000007812 deficiency Effects 0.000 description 2
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- 241000255777 Lepidoptera Species 0.000 description 1
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Abstract
Ontology is introduced the tea-plant pests representation of knowledge and Share Model by the present invention, it is proposed a kind of tea-plant pests representation of knowledge and sharing method based on ontology, domain knowledge is obtained by demand analysis first, then with solving the problems, such as domain knowledge effective expression using ontology and OWL language, the multiplexing of domain knowledge finally is provided based on Prot é g é api interface and share.The method achieve the expression of the formalization of tea-plant pests domain knowledge specification, it improves tea-plant pests domain knowledge and shares reuse efficiency, more fruitful Knowledge Service Platform is provided for tea-plant pests prevention and treatment, tea grower's production decision etc., the development information-based for the raising of tea production quality, tea industry has certain facilitation.
Description
Technical field
The present invention is application of the computer information technology in agriculture field, mainly proposes a kind of virtual ontology in Tea Science field
Modeling method.
Background technique
Tea is drink popular in the world, has the function of promoting longevity to human body.In recent years, with tea-drinking market
Tea industry knowledge services are proposed requirements at the higher level by the raising of share, the rapid development of tea industry.Wherein, tea-plant pests are prevented and treated,
The even more research hotspot of market concern.Tea-plant pests prevention and treatment is related to multiple subject crossing necks such as Tea Science, biology, Plant Protection
Domain includes a large amount of, complicated concept and relationship in knowledge system.And effectively expressing tea-plant pests domain knowledge be diagnosis,
Tea-plant pests are prevented and treated, realize basis and the premise of tea industry knowledge services.
Domain knowledge expression mainly have logical representation, production KR, frame representation, semantic notation,
XML representation, ontology representation method etc..Logical representation indicates movement Subjective and Objective in the form of predicate, is described using logical formula
Practical judgment, property, relationship etc., but be not easy to realize uncertain reasoning；Production KR, i.e. IF-THEN representation, behind IF
The prerequisite of description rule, the conclusion of description rule behind THEN, the control for being chiefly used in stating between various processes knowledge and
Its interaction mechanism, but structured knowledge beyond expression of words.
Ontology is the specific Formal Specification explanation of shared conceptual model, by determining concept in the field approved jointly
And the relationship of the concepts, the model of knowledge is described on semantic and knowledge level.Ontology usually by concept, relationship, example, axiom,
The first language composition of five modelings of function, i.e. O=(C, R, I, A, F).Wherein, C indicates concept set, and R is set of relationship, and I represents real
Example, A represent axiomatic set theory, F then representative function set.
Summary of the invention
For tea-plant pests field knowledge hierarchy in large scale, the present invention is on the basis of existing research, by ontology skill
Art introduces tea industry knowledge services, a kind of tea-plant pests representation of knowledge and sharing method based on ontology is proposed, from bulky complex
Tea-plant pests knowledge system in take out the domain knowledge for being easy to reuse, determine between the concept and concept approved jointly in field
Relationship expresses tea-plant pests domain knowledge, constructs domain knowledge ontology library, generates domain knowledge shared platform, realizes tea insect
The effective expression of evil domain knowledge, shared reuse, organization and administration etc. are that such as tea-plant pests diagnosis of development tea industry knowledge services is anti-
It controls, the element task of tea Culture problem decision.
1, a kind of tea-plant pests representation of knowledge and sharing method based on ontology, it is characterized in that step includes:
1) knowledge acquisition:
Firstly, determining that knowledge representation is all relevant knowledges in tea-plant pests field by demand analysis；
Then, by multiple Knowledge Sources in tea-plant pests field, finishing field knowledge is collected, it is related sufficiently to obtain field
Concept and the relationship of the concepts；
Finally, building ontology logical framework i.e. O=(C, R, I, A, F) based on the domain knowledge arranged, knowledge table is defined
Show class C, attribute R, example I, axiom A and the rule F of ontology；Representation of knowledge ontology is using biological classification method as standard, to tea tree
Pest concept is classified；Tea-plant pests field concept and the relationship of the concepts include father's subclass relation, equivalence class relationship, object category
Property, data attribute；
2) knowledge representation:
2.1) building of class
In tea-plant pests field, by Pests of Tea-Plants, tea tree position, Cha Qu, hazard approach, the extent of injury, morphological feature, life
State prevention and treatment and pest natural enemy are defined as the top layer class of ontology；Subclass or example are respectively set again；
2.2) attribute constructs
Noumenon property includes object properties and data attribute；
Object properties for be associated with two examples, express concept between non-categorical relationship, have anti-reciprocity, symmetry and
Transitivity constraint；The domain and codomain of object properties determine associated two classes respectively；Tea-plant pests define tea tree in field
Pest and Cha Qu, tea tree position, hazard approach, the extent of injury, pest natural enemy, pest and pest morphological feature are associated class,
I.e. object properties are set as liveIn (tea area), harm (tea tree position), harmMode (hazard approach), harmDegree (danger
Evil degree), eated (pest natural enemy), eat (Pests of Tea-Plants), pestMorphology (pest morphological feature), wherein
PestMorphology (pest morphological feature) attribute is provided with sub- attribute；
Data attribute describes the characteristic of example itself using basic data type；Tea-plant pests FIELD Data attribute includes
Tea Qu Tezheng, history of life generation, life habit, hazard symptoms, control method, pest picture and natural enemy morphological feature, it is various anti-
Control the sub- attribute that method is then set as data attribute；
2.3) example creates
Instances of ontology is the member of class, has atomicity；Creation example need sufficiently to enumerate each subclass included at
Member, and its data attribute and object properties are respectively set, it improves between the concrete property and example and example of each example
Objective connection；Tea-plant pests EXAMPLE OF FIELD concentrates on two concepts of Pests of Tea-Plants type and morphological feature；
2.4) axiom constructs
Axiom rule further illustrates concept and the relationship of the concepts；Ontology axiom includes the axiom of class, attribute axiom, reality
Example axiom, axiom of constraint and custom rule；
The axiom of class has DisjiontClasses, SubClassOf, EquivalentClasses, expresses mutual exclusion respectively
The definition of class, father and son's class, equivalence class；Three axioms of class are defined while constructing class；
Attribute axiom includes attribute definition domain, number field restriction and symmetry and transitivity constraint；
Example axiom then refers to that instances of ontology is stated；
Axiom of constraint is divided into value constraint and constraint base, for defining necessity and necessary and sufficient condition of class；
Value constraint includes All valuesSome valuesCodomain for limitation attribute；
Constraint base includes Max cardinality (≤), Min cardinality (>=) and Exact
Cardinality (=), the number for limitation attribute value；
2.5) knowledge encoding
Ontology formally expresses concept and relationship by modeling language, is led using OWL language to determining tea-plant pests
Domain knowledge is encoded, and is realized the expression of the formalization of domain knowledge semanteme, is generated user-friendly tea-plant pests ontology library；
Cataloged procedure is manual coding to be carried out referring to the OWL syntax rule issued by World Wide Web Consortium W3C, or utilize
It is encoded by way of object-oriented by the prot é g é software that Stanford University develops.
2.6) ontology inference
Detection is made inferences to tea-plant pests domain body by the FaCT++ inference machine being integrated in prot é g é software；
If it is detected that corrected there are logic or example conflict according to prompt, and again reasoning inspection until errorless；
2.7) ontology evaluation
Ontology evaluation includes concept evaluation and the relationship of the concepts evaluation:
2. concept is evaluated, comprising:
Conceptual integrity evaluation, i.e. concept should be as far as possible comprising most of basic and important concepts in the field；
Concept evaluation of the accuracy, concept defined in ontology is not only needed with integrality, and needs correctness；
Integrity detection and standard are carried out to tea-plant pests domain body using the concept evaluation method based on corpus respectively
True property detection, measures accuracy rate and recall rate；
2. the relationship of the concepts is evaluated, comprising:
Relationship evaluation of the accuracy judges whether the classification of the relationship of the concepts is correct, meets objective fact；
Relationship consistency detection, refers to and judges concept in ontology, asserts and the relationship between various other concepts, front and back are fixed
Whether justice has semantic conflict；
The detection of relationship terseness, refers to judge whether the relationship of the concepts duplicates in ontology, redundancy error；
Using based on Concept Semantic Similarity algorithm to the consistency of tea-plant pests domain body the relationship of the concepts, accurate
Property, terseness etc. are evaluated respectively；
Concept is associated by the relationship between it, therefore, whether to evaluate the relationship between concept correctly it is necessary to first evaluating
Whether concept associated by the relationship is correct, and this programme evaluates the language of concept in concept associated with relationship and corpus first
Adopted similarity illustrates that concept associated by relationship is correctly, to derive association if the semantic similarity is more than threshold value
The relationship of these concepts is also correct.
It is judged as whether tea-plant pests Domain Ontology Modeling is good according to Ontological concept evaluation and relationship evaluation acquired results,
Domain knowledge indicates whether to reach original target, if realizes the shared of knowledge and reuses；
3) knowledge sharing
Domain knowledge effectively expressing is realized by tea-plant pests ontology, and is known domain knowledge as tea-plant pests field
Know library, add Prot é g é api interface, realizes that platform is shared and reused to the tea-plant pests domain knowledge based on ontology library；
Prot é g é API is the open source java class library realized for Web Ontology Language OWL and RDF (S), which provides
Load and preservation OWL file, inquiry and operation OWL data model.Tea-plant pests ontology passes through addition Prot é g é api interface, energy
It is enough preferably to provide support for tea-plant pests knowledge services, thus realize the tea-plant pests domain knowledge based on ontology library it is shared with
It reuses.
During ontology library puts into knowledge services, modifies to old concept and increases new conceptual knowledge,
Ontology logical framework is formally defined and improved, finally the knowledge of modification is encoded, realizes tea-plant pests field sheet
The adjustment of the corresponding duration in body library, maintenance and upgrade.It furtherly, further include tea-plant pests Acquirement of field knowledge module, tea
Set insect pest domain knowledge expression module and tea-plant pests domain knowledge sharing module；
The step of knowledge acquisition module, includes the following steps one~tri-:
Step 1: demand analysis is carried out to market and user, determines the territory of knowledge representation；
Step 2: it by the cooperation with domain expert, is obtained from multiple sources such as expert's monograph, document website, knowhows
It takes domain knowledge (term), combs the concept and the relationship of the concepts in domain knowledge；
Step 3: according to the domain knowledge by arranging, building the domain knowledge indicates the logical framework of ontology, i.e. O=
(C,R,I,A,F)；Define class, example, object properties, data attribute and the axiom rule of domain knowledge；
The step of knowledge representation module, includes the following steps four~seven:
Step 4: the creation such as domain body class, attribute, example, axiom is completed, relationship, the Mei Geshi of each class are built
Axiom rule in the object properties of example, data attribute and ontology；
Step 5: encoding determining domain knowledge using OWL language, realizes the formalization of domain knowledge semanteme
Expression；
Step 6: ontology progress is consistent to be indicated to the domain knowledge constructed with Tableau (x) algorithm based on description logic
Property reasoning, check its knowledge representation with the presence or absence of logic conflict and example conflict；If there is conflict after reasoning, need according to mistake
Return step four is prompted, ontology modification and reasoning inspection again are completed, until ontology is errorless；
Step 7: the ontology expression of domain knowledge is evaluated based on corpus and Arithmetic of Semantic Similarity；If evaluation
As a result bad, return step two, step 3 are needed, the logical framework for redefining domain knowledge, correcting ontology；If result is good
It is good, that is, generate domain knowledge ontology library；
The step of knowledge sharing module, includes the following steps eight~nine:
Step 8: domain knowledge ontology library is connect by Prot é g é api interface with WWW, can be produced for tea grower,
The knowledge services such as Knowledge Decision-making, Semantic Web field provides the representation of knowledge and shared platform based on ontology；
Step 9: it during the representation of knowledge and shared platform carry out knowledge services, needs to collect user terminal in time
Feedback information, the domain knowledge newly updated and demand for services, and be sent to knowledge acquisition module, that is, three are entered step, has been updated
Rare book body logical framework realizes domain knowledge ontology library routinely maintenance and upgrade.
Ontology is introduced the tea-plant pests representation of knowledge and Share Model by the present invention, proposes the tea-plant pests knowledge based on ontology
Expression and sharing method obtain domain knowledge by demand analysis first, then solve field using ontology and OWL language
Knowledge effective expression ground problem finally provides the multiplexing of domain knowledge and shared based on Prot é g é api interface.This method is realized
The expression of the formalization of tea-plant pests domain knowledge specifications improves tea-plant pests domain knowledge and shares reuse efficiency, is tea
The more fruitful Knowledge Service Platforms of offers such as pest control, tea grower's production decision, raising, tea for tea production quality are provided
The development of production informatization has certain facilitation.
Detailed description of the invention
Fig. 1 Pests of Tea-Plants classification chart；
Fig. 2 Pests of Tea-Plants concept relation graph；
The tea-plant pests representation of knowledge of the Fig. 3 based on ontology and shared system framework schematic diagram.
Specific embodiment
The present invention is further described with specific embodiment with reference to the accompanying drawing.
1 knowledge acquisition
Firstly, determining the territory of knowledge representation by demand analyses such as market surveys.The present invention is tea-plant pests
All relevant knowledges in field.
Secondly, collecting finishing field by multiple sources such as the expert in the field, monograph, document, website, knowhows
Knowledge sufficiently obtains the relevant concept in field and the relationship of the concepts.The present invention by with tealeaves biochemistry and biotechnology state
The cooperation of key lab, family cultivation base, combs vocabulary, term in tea-plant pests field etc., determines common in field
The concept of approval, and classification and non-categorical relationship between concept is arranged to clear, the arrangement of completion tea-plant pests domain knowledge.Such as tea
Setting insect pest field includes that the concepts such as Homoptera, green plant bug, larva, wing expanse, cultural control, south China Cha Qu, bud-leaf and Miridae-are green
The relationships such as fleahopper, false eye leafhopper-suction juice.
Finally, building its ontology logical framework based on the domain knowledge arranged, i.e. O=(C, R, I, A, F) defines knowledge
Indicate the class of ontology, attribute, example, axiom, rule etc..
Representation of knowledge ontology classifies to Pests of Tea-Plants concept, as shown in Figure 1 using biological classification method as standard.Tea
Insect pest field concept and the relationship of the concepts are set, including is-a, instance-of, attribute-of etc., as shown in Figure 2.With green
For fleahopper, father's subclass relation: Arthropoda Insecta Semiptera Miridae；Equivalence class relationship: scientific name, alias；Object category
Property: life area, morphological feature, hazard approach, the extent of injury, hazard symptoms；Data attribute: history of life generation, life habit,
Environmental factor, control method, natural enemy etc..
2 knowledge representations
(1) building of class
Ontology, which indicates some using class, has denominator group of individuals, and passes through father and son's class, fraternal class, equivalence
The definition of the classes relationships such as class, mutual exclusion class, improves concept classification relationship, guarantees the integrality and accuracy of field concept.
Tea-plant pests field is by Pests of Tea-Plants, tea tree position, Cha Qu, hazard approach, the extent of injury, morphological feature, ecology
Prevention and treatment, pest natural enemy etc. are defined as top layer class, then subclass or example is respectively set, blind if Miridae and green plant bug are father and son's class
Pentatomiddae and Scutelleridae are fraternal class, and green plant bug and floral leaf worm are equivalence class, and Miridae and Scutelleridae are mutual exclusion class etc..
(2) attribute constructs
Noumenon property includes object properties and data attribute.Object properties are expressed between concept for being associated with two examples
Non-categorical relationship, there is the constraint such as anti-reciprocity, symmetry, transitivity, domain and codomain determine associated two respectively
Class.Pests of Tea-Plants and Cha Qu, tea tree position, hazard approach, the extent of injury, pest natural enemy, form are defined in tea-plant pests field
Feature etc. be associated class, i.e., object properties be set as liveIn, harm, harmMode, harmDegree, eated, eat,
PestMorphology etc., wherein pestMorphology attribute is provided with sub- attribute.
Data attribute describes the characteristic of example itself using basic data type.Tea-plant pests FIELD Data attribute includes
Tea Qu Tezheng, history of life generation, life habit, hazard symptoms, control method, pest picture, natural enemy morphological feature etc., it is various anti-
Control the sub- attribute that method is then set as data attribute.
(3) example creates
Instances of ontology is the member of class, has atomicity.Creation example need sufficiently to enumerate each subclass included at
Member, and its data attribute and object properties are respectively set, it improves between the concrete property and example and example of each example
Objective connection.Tea-plant pests EXAMPLE OF FIELD has focused largely among two concepts of Pests of Tea-Plants and morphological feature, such as defines green blind
Stinkbug, tea geometrid etc. are the example of Pests of Tea-Plants class, and yellow, ellipse etc. are the example of morphological feature class.
(4) axiom constructs
Axiom rule further illustrates concept and the relationship of the concepts, keeps ontology logic tighter, facilitates inconsistent
The detection and knowledge reasoning of property.Ontology axiom includes the axiom of class, attribute axiom, example axiom, axiom of constraint, custom rule
Etc. forms.
The three of class big axiom DisjiontClasses, SubClassOf, EquivalentClasses, express mutual exclusion respectively
The definition of class, father and son's class, equivalence class, the classification relation between perfect concept.Three grand dukes of class are defined while constructing class
Reason, if the mutual exclusion class of green plant bug is tea angle fleahopper, equivalence class is floral leaf worm, and parent is Miridae etc..
Attribute axiom includes the constraint such as attribute definition domain, number field restriction and symmetry, transitivity, such as tea-plant pests ontology
Attribute eat domain is pest natural enemy, codomain is Pests of Tea-Plants, and attribute eated domain is Pests of Tea-Plants, codomain is pest day
Enemy, and with eat each other against attribute.
Example axiom then refers to that instances of ontology is stated, such as same instance, different instances restrict.
Axiom of constraint is divided into value constraint and constraint base, is used mainly to define necessity and necessary and sufficient condition of class.Value constraint packet
Include All valuesSome valuesCodomain for limitation attribute.Such as the harm of Miridae pest
Mode only inhales juice, therefore adds All values from, that is, only constraint, if the adjective limitation of attribute is some or deposits
Then adding some constraints.Constraint base include Max cardinality (≤), Min cardinality (>=) and
Exact cardinality (=), the number for limitation attribute value.
Ontology supplements the deficiency of axiomatic specification by custom rule, expands the ability and range of ontology expression knowledge, such as
SWRL rule etc..SWRL (Semantic Web Rule Language) rule is presented by systematic fashion, with OWL sublanguage
Based on OWL DL and OWL Lite, in conjunction with the regular describing mode of Unary/Binay Datalog Rule ML, OWL is supplemented
Deficiency of the language in terms of rule description and reasoning, provides more powerful logical expression ability.
(5) knowledge encoding
Ontology formally expresses concept and relationship, such as SHOE, CycL, RDF, RDF-S, OWL by modeling language.
SHOE is an extension based on HTML.CycL is the Knowledge Description Language of Cyc system.RDF is a kind of mark for describing web resource
Language is remembered, for handling metadata.OWL be based on RDF and RDF-S, using the RDF syntax gauge based on XML, definition and
Write a kind of label language of Semantic Web ontology.
Ontology OWL language has clearly grammer system, semantic meaning representation ability abundant, effective computability, can
Description that is explicit to the concept and the relationship of the concepts progress in field, formalizing, and carry out rationally consistent reasoning.Therefore,
The present invention selects OWL language to encode determining tea-plant pests domain knowledge, realizes the formalization of domain knowledge semanteme
Expression, generates user-friendly tea-plant pests ontology library.
(6) ontology inference
For domain body based on modeling by hand, heavy workload, process are complicated, it is therefore desirable to which ontology inference checks that knowledge is patrolled
The conflict such as inconsistency of frame and Formal Representation is collected, and the domain knowledge based on offer infers and wherein contains information.
OWL ontology is based on description logic theory form, completes logic detection and this using (optimization) Tableaux algorithm
Body reasoning.Inference machine applied to OWL language ontology is more, and the present invention selects FaCT++ inference machine to tea-plant pests domain body
Make inferences detection.If it is detected that needing to be corrected according to prompt there are logic or example conflict, and reasoning inspection again
Until errorless.The reasoning results show that tea-plant pests domain body structure is consistent, without logic conflict and example conflict.
(7) ontology evaluation
Ontology evaluation is concentrated mainly on concept evaluation and the relationship of the concepts evaluates two aspects.
1. concept is evaluated: being evaluated including conceptual integrity, i.e., concept should be as far as possible comprising most of basic in the field
With important concept；Concept evaluation of the accuracy, concept defined in ontology are not only needed with integrality, and are needed correct
Property.Carry out integrity detection and accuracy inspection respectively to tea-plant pests domain body using the concept evaluation method based on corpus
It surveys, measures accuracy rate and recall rate as shown in table 1, table 2:
The accuracy rate of 1 tea-plant pests Ontological concept of table evaluation
The recall rate of 2 tea-plant pests Ontological concept of table evaluation
2. the relationship of the concepts is evaluated: whether inclusion relation evaluation of the accuracy, the i.e. classification of the relationship of the concepts are correct, meet visitor
It sees true；Relationship consistency detection, refers to concept in ontology, asserts and the relationship between various other concepts, and front and back definition is
It is no to have semantic conflict；The detection of relationship terseness, refers to whether the relationship of the concepts duplicates in ontology, redundancy error.Using being based on
Concept Semantic Similarity algorithm to consistency, accuracy, terseness of tea-plant pests domain body the relationship of the concepts etc. respectively into
Row evaluation, the results are shown in Table 3:
3 tea-plant pests ontological relationship of table evaluates list
Wherein, relationship consistency testing result is shown as 0 mistake, and relationship accuracy testing result is shown as 7 mistakes,
Relationship terseness testing result is 4 mistakes, and is corrected to the mistake of prompt.According to Ontological concept evaluation and relationship
It evaluates acquired results and carries out comprehensive analysis, can determine whether that tea-plant pests Domain Ontology Modeling is good, domain knowledge expression reaches original
Target may be implemented the shared of knowledge and reuse.
3 knowledge sharings
The tea-plant pests field ontology library constructed at present is total the example of attribute 960 of 560 classes 56, including domestic normal
Relevant knowledge in the Pests of Tea-Plants seen and tea-plant pests field.It can be with graphic software platform, intuitively by OntoGraf plug-in unit
Express tea-plant pests field concept and the relationship of the concepts in ground., respectively show tea-plant pests ontology general frame, tea-plant pests sheet
Body Lepidoptera frame, tea-plant pests ontology tea geometrid frame.
Domain knowledge effectively expressing is realized by tea-plant pests ontology, and as tea-plant pests domain knowledge base,
Addition Prot é g é api interface realizes that platform is shared and reused to the tea-plant pests domain knowledge based on ontology library, produced for tea grower,
The numerous areas such as scientific research, Semantic Web, knowledge engineering provide extensive knowledge services.
During ontology library puts into knowledge services, need constantly to pay attention to the feedback information of each user terminal, Yi Jigeng
New conceptual knowledge and demand for services is constantly collected to knowledge acquisition module, by analyze existing knowledge be need modify or
It is to delete, then according to the knowledge and demand updated, modifies to old concept and increase new conceptual knowledge, formalizes
Ground defines and improves ontology logical framework, finally encodes to the knowledge of modification, realizes tea-plant pests field ontology library phase
The adjustment for the duration answered, maintenance and upgrade mention for the inquiry diagnosis in subsequent knowledge based library, decision support, shared multiplexing etc.
For better tea industry knowledge services.
For the feasibility and validity for verifying the tea-plant pests representation of knowledge and sharing method based on ontology, the present invention is used
Protege platform, Mysql database and Jsp/Servlet technology, develop the tea-plant pests representation of knowledge based on ontology with
Shared system, used exploitation environment are Windows 7.The system can both carry out semantic formalization to domain knowledge
Expression can also provide the shared of knowledge on client service platform for user and reuse service.
The tea-plant pests representation of knowledge and shared system based on ontology are mainly by tea-plant pests Acquirement of field knowledge module, tea
Set the insect pest domain knowledge expression composition such as module and tea-plant pests domain knowledge sharing module, basic frame structure such as Figure 83
It is shown:
The system specific steps process is as follows:
Step 1: demand analysis is carried out to market and user, determines the territory of knowledge representation.
Step 2: it by the cooperation with domain expert, is obtained from multiple sources such as expert's monograph, document website, knowhows
It takes domain knowledge (term), combs the concept and the relationship of the concepts in domain knowledge.
Step 3: according to the domain knowledge by arranging, building the domain knowledge indicates the logical framework of ontology, i.e. O=
(C,R,I,A,F).Define class, example, object properties, data attribute and the axiom rule of domain knowledge.
Step 4: the creation such as domain body class, attribute, example, axiom is completed, relationship, the Mei Geshi of each class are built
Axiom rule etc. in the object properties of example, data attribute and ontology.
Step 5: encoding determining domain knowledge using OWL language, realizes the formalization of domain knowledge semanteme
Expression.
Step 6: ontology progress is consistent to be indicated to the domain knowledge constructed with Tableau (x) algorithm based on description logic
Property reasoning, check its knowledge representation with the presence or absence of logic conflict and example conflict.If there is conflict after reasoning, need according to mistake
Return step four is prompted, ontology modification and reasoning inspection again are completed, until ontology is errorless.
Step 7: the ontology expression of domain knowledge is evaluated based on corpus and Arithmetic of Semantic Similarity.If evaluation
As a result bad, return step two, step 3 are needed, the logical framework for redefining domain knowledge, correcting ontology；If result is good
It is good, that is, generate domain knowledge ontology library.
Step 8: domain knowledge ontology library is connect by Prot é g é api interface with WWW, can be produced for tea grower,
The knowledge services such as Knowledge Decision-making, Semantic Web field provides the representation of knowledge and shared platform based on ontology.
Step 9: it during the representation of knowledge and shared platform carry out knowledge services, needs to collect user terminal in time
Feedback information, the domain knowledge newly updated and demand for services, and be sent to knowledge acquisition module, that is, three are entered step, has been updated
Rare book body logical framework realizes domain knowledge ontology library routinely maintenance and upgrade.
By the test analysis to system, good effect is achieved, demonstrates the feasibility and validity of this method.
Ontology is introduced the tea-plant pests representation of knowledge and Share Model by the present invention, proposes the tea-plant pests knowledge based on ontology
Expression and sharing method obtain domain knowledge by demand analysis first, then solve field using ontology and OWL language
Knowledge effective expression ground problem finally provides the multiplexing of domain knowledge and shared based on Prot é g é api interface.This method is realized
The expression of the formalization of tea-plant pests domain knowledge specifications improves tea-plant pests domain knowledge and shares reuse efficiency, is tea
The more fruitful Knowledge Service Platforms of offers such as pest control, tea grower's production decision, raising, tea for tea production quality are provided
The development of production informatization has certain facilitation.
Claims (1)
1. a kind of tea-plant pests representation of knowledge and sharing method based on ontology, it is characterized in that step includes:
1) knowledge acquisition:
Firstly, determining that knowledge representation is all relevant knowledges in tea-plant pests field by demand analysis；
Then, by multiple Knowledge Sources in tea-plant pests field, finishing field knowledge is collected, it is relevant general sufficiently to obtain field
Thought and the relationship of the concepts；
Finally, building ontology logical framework i.e. O=(C, R, I, A, F) based on the domain knowledge arranged, representation of knowledge sheet is defined
Class C, attribute R, example I, axiom A and the rule F of body；Representation of knowledge ontology is using biological classification method as standard, to Pests of Tea-Plants
Concept is classified；Tea-plant pests field concept and the relationship of the concepts include father's subclass relation, equivalence class relationship, object properties,
Data attribute；
2) knowledge representation:
2.1) building of class
It is in tea-plant pests field, Pests of Tea-Plants, tea tree position, Cha Qu, hazard approach, the extent of injury, morphological feature, ecology is anti-
Control the top layer class that ontology is defined as with pest natural enemy；Subclass or example are respectively set again；
2.2) attribute constructs
Noumenon property includes object properties and data attribute；
Object properties express the non-categorical relationship between concept, have anti-reciprocity, symmetry and transmitting for being associated with two examples
Property constraint；The domain and codomain of object properties determine associated two classes respectively；Tea-plant pests define Pests of Tea-Plants in field
It pair is associated class with tea area, tea tree position, hazard approach, the extent of injury, pest natural enemy, pest and pest morphological feature, i.e.,
(journey is endangered as attribute is set as liveIn (tea area), harm (tea tree position), harmMode (hazard approach), harmDegree
Degree), eated (pest natural enemy), eat (Pests of Tea-Plants), pestMorphology (pest morphological feature), wherein
PestMorphology (pest morphological feature) attribute is provided with sub- attribute；
Data attribute describes the characteristic of example itself using basic data type；Tea-plant pests FIELD Data attribute includes tea area
Feature, history of life generation, life habit, hazard symptoms, control method, pest picture and natural enemy morphological feature, various prevention and treatment sides
Rule is set as the sub- attribute of data attribute；
2.3) example creates
Instances of ontology is the member of class, has atomicity；Creation example needs sufficiently to enumerate the member that each subclass is included, and
Its data attribute and object properties are respectively set, it is objective between the concrete property and example and example of each example to improve
System；Tea-plant pests EXAMPLE OF FIELD concentrates on two concepts of Pests of Tea-Plants type and morphological feature；
2.4) axiom constructs
Axiom rule further illustrates concept and the relationship of the concepts；Ontology axiom includes the axiom of class, attribute axiom, example public affairs
Reason, axiom of constraint and custom rule；
The axiom of class has DisjiontClasses, SubClassOf, EquivalentClasses, expresses mutual exclusion class, father respectively
The definition of subclass, equivalence class；Three axioms of class are defined while constructing class；
Attribute axiom includes attribute definition domain, number field restriction and symmetry and transitivity constraint；
Example axiom then refers to that instances of ontology is stated；
Axiom of constraint is divided into value constraint and constraint base, for defining necessity and necessary and sufficient condition of class；
Value constrainsCodomain for limitation attribute；
Constraint base includes Max cardinality (≤), Min cardinality (>=) and Exact cardinality
(=), the number for limitation attribute value；
2.5) knowledge encoding
Ontology formally expresses concept and relationship by modeling language, is known using OWL language determining tea-plant pests field
Knowledge is encoded, and is realized the expression of the formalization of domain knowledge semanteme, is generated user-friendly tea-plant pests ontology library；
Cataloged procedure is manual coding to be carried out referring to the OWL syntax rule issued by World Wide Web Consortium W3C, or utilize by this
The prot é g é software of Tan Fu university exploitation is encoded by way of object-oriented；
2.6) ontology inference
Detection is made inferences to tea-plant pests domain body by the FaCT++ inference machine being integrated in prot é g é software；If inspection
Measure there are logic or example conflict, then corrected according to prompt, and again reasoning inspection until errorless；
2.7) ontology evaluation
Ontology evaluation includes concept evaluation and the relationship of the concepts evaluation:
1. concept is evaluated, comprising:
Conceptual integrity evaluation, i.e. concept should be as far as possible comprising most of basic and important concepts in the field；
Concept evaluation of the accuracy, concept defined in ontology is not only needed with integrality, and needs correctness；
Integrity detection and accuracy are carried out to tea-plant pests domain body using the concept evaluation method based on corpus respectively
Detection, measures accuracy rate and recall rate；
2. the relationship of the concepts is evaluated, comprising:
Relationship evaluation of the accuracy judges whether the classification of the relationship of the concepts is correct, meets objective fact；
Relationship consistency detection, refers to and judge concept in ontology, asserts and the relationship between various other concepts, and front and back, which defines, is
It is no to have semantic conflict；
The detection of relationship terseness, refers to judge whether the relationship of the concepts duplicates in ontology, redundancy error；
Using the consistency, accuracy, letter based on Concept Semantic Similarity algorithm to tea-plant pests domain body the relationship of the concepts
Clean property etc. is evaluated respectively；
Evaluation method are as follows: the semantic similarity for evaluating concept in concept associated with relationship and corpus first, if the semanteme
Similarity is more than threshold value, then illustrates that concept associated by relationship is correctly, to derive the relationship for being associated with these concepts
It is correct；If the semantic similarity is no more than threshold value, illustrating concept associated by relationship not is correctly, to derive
The relationship for being associated with these concepts is also not correct；
It is judged as whether tea-plant pests Domain Ontology Modeling is good according to Ontological concept evaluation and relationship evaluation acquired results, field
Whether the representation of knowledge reaches original target, if realizes the shared of knowledge and reuses；
3) knowledge sharing
Domain knowledge effectively expressing is realized by tea-plant pests ontology, and using domain knowledge as tea-plant pests domain knowledge
Prot é g é api interface is added in library, realizes that platform is shared and reused to the tea-plant pests domain knowledge based on ontology library；
During ontology library puts into knowledge services, modifies to old concept and increase new conceptual knowledge, form
Change ground definition and improve ontology logical framework, finally the knowledge of modification is encoded, realizes tea-plant pests field ontology library
The adjustment of corresponding duration, maintenance and upgrade.
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