CN106570187A - Ontological-concept-similarity-based software component retrieving method - Google Patents
Ontological-concept-similarity-based software component retrieving method Download PDFInfo
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Abstract
The invention discloses an ontological-concept-similarity-based software component retrieving method. The method comprises: a user sends a component retrieving request to a software component base according to a need and the software component base constructs a corresponding semantic ontological tree based on a component demand of the user; according to the user demand, the software component base retrieves a corresponding component description ontological tree from an OWL description document library of the component; fuzzy matching is carried out on the component description ontological tree and a user need ontological tree; if the component description ontological tree does not match the user need ontological tree, component restructuring is carried out on the mismatched component; with a KMP algorithm, modified re-matching is carried out on similarity concepts of ontological tree inquiring; and according to automatic mapping of the OWL description document of the component and the corresponding component, the user retrieves a corresponding component from the component base. A target component can be discovered precisely by using semantic similarity, so that the component inquiring efficiency and reuse rate can be improved and the development period of the software can be shortened.
Description
Technical field
The invention belongs to technical field of software engineering, is related to a kind of software component retrieval method based on body, particularly
A kind of software component retrieval method based on Ontological concept similarity.
Background technology
With the fast development of software engineering technology, component-oriented and the software development methodology of service have become vast soft
The exploitation main flow of part developer.And the maximum object that relies on of the software development methodology of component-oriented is component base.To software structure
The method of expression and the inquiry of part is a lot.At present the component library system of main flow to the description of component all using feature based or
The faceted description method of XML.
In gentle and quiet, Zhao Haiyan, Zhang Wei, Jin Zhi " software product of feature based model is derived automatically from method survey [J].
Journal of Software, 2016,1:002 " a kind of method that utilization characteristic model and taxonomy model are derived automatically from software component is mentioned in, is
It is derived automatically from software product and provides support.But the method for feature based lack the careful research to characteristic model organization framework and
Illustrate, redundancy and confusion of the characteristic model in the form of expression is result in a certain extent, also cause domain analysis people
Member is difficult effectively to carry out field modeling activity in practice.Wang Y, Wang B etc. is in " A Component Retrieval
Tree Matching Algorithm Based on a Faceted Classification Scheme[J]
.Cybernetics and Information Technologies, 2015,15 (1):A kind of facet classification is mentioned in 14-23 "
Software component retrieval method framework with it is theoretical, the component of certain specific area can be inquired about, improve inquiry efficiency and
Accuracy.But, under conditions of semantic net, the context language of field component cannot be described using the component description method of facet
Justice, and the recall ratio and precision ratio that are based on the querying method of keyword or faceted description are extremely limited, it is impossible to meet software and open
The query demand of the personnel of sending out.
As software category is significantly increased the expansion with software size, it is semantically similar and in the different component of grammer
Increase therewith, and existing method is difficult with semantic similarity and accurately finds target member, increases the reusability of component, carries
The efficiency of high software development.The present invention is based on body, inquires about target member using semantic similarity, and that improves component looks into standard
Rate, and then shorten the construction cycle of software.
The content of the invention
The purpose of the present invention is to provide a kind of based on Ontological concept similarity to overcome above-mentioned the deficiencies in the prior art
Software component retrieval method, the method describes component by using body web language (OWL), and is translated into body tree and enters
Row fuzzy matching, then recombinates to the component of mismatch, and the similar concept for inquiring about body tree is repaiied using KMP algorithms
Change to improve the precision ratio of component retrieval, improve the efficiency of software development.
In order to solve the problems referred to above of prior art, the present invention is employed the following technical solutions.
A kind of software component retrieval method based on Ontological concept similarity of the present invention, comprises the following steps：
Step one, user send component retrieval request to software component library according to demand, and software component library receives user's
After request, corresponding Ontology tree is built according to the component demand of user；
Step 2, described software component library are described in document library to coarseness according to the demand of user from the OWL of component
Retrieve corresponding component description body tree；
Step 3, described component description body tree and user's request body tree are carried out into fuzzy matching；
If step 4, described component description body tree are unsuccessful with matching for user's request body tree, to mismatch
Component carry out component restructuring；And using KMP algorithms the similar concept for inquiring about body tree is modified and match again；
Step 5, the automatic mapping that document and corresponding component are described according to the OWL of component, make user examine from component base
Rope is to corresponding component.
In described step two, described describing from the OWL of component retrieve in document library corresponding component description sheet
Body tree, including procedure below：
(21) document is described using the OWL that OWL is described so as to form component to each field component, is then utilized
XPath extracts Ontological concept therein；But for the base attribute of the objects concept in body is ignored, described basic category
Property includes：Author, date；
(22) Ontological concept for extracting XPath carries out hierarchical classification and forms a body tree；Described component description
Document is to there is a description body tree；In order to the mapping of Ontological concept similarity is better achieved, by the description above body
Tree is converted into description binary tree.
In described step three, described component description body tree carries out fuzzy matching with user's request body tree, is
Refer to：
Save in by calculating the similarity between two component description body trees and user's request body tree interior joint and tree
Point number, i.e., matching length come calculate the matching between two body trees be worth；And Value Realization two is matched according to it
, there are two kinds of situations in this process in the fuzzy matching mapping between body tree；
, there is fuzzy matching between two tree body trees in the first situation：
If T=(V, E, root (T)) is the corresponding binary tree of an arbitrary body tree, then the matching of binary tree T is worth H
(M) it is：
Wherein：Represent the Ontological concept phase of all nodes in tree
Like degree and；︱ v ︱ represent matching length, as set the number of interior joint；
If Q=(V, E, root (Q)) is the corresponding binary tree of inquiry body tree, D=(V, E, root (D)) is description body
Corresponding binary tree is set, wherein：
If：
Then two body trees are called fuzzy matching；Wherein, ≈
Refer to and set up in certain threshold range；
Second situation, if two body trees do not have fuzzy matching, by the section in the binary tree D of inquiry body tree
Point Node and the leaf together with node are divided into ︱ Node ︱ stalk trees；In the same manner also by inquiry body tree binary tree Q also draw
It is divided into some stalk trees；Then by the above-mentioned every stalk tree for marking off to carrying out fuzzy matching；
The binary tree D interior joint Node of inquiry body tree are extracted using following algorithm:
P=com → lchild；
while(p→rchild！=null) { p=p → rchild；printf(″p″)；}
Thus, egress series is decomposed to obtain；Then corresponding subtree series is obtained, meanwhile, according to same decomposition rule
The binary tree Q of inquiry body tree is resolved into into subtree；
In the latter case, to the subtree for inquiring about the binary tree D of body tree and the subtree of the binary tree Q for inquiring about body tree
Mapped, if T1=(V1,E1,root(T1)), T2=(V2,E2,root(T2)) it is respectively the son for inquiring about body tree binary tree D
Tree and the subtree of body tree binary tree Q is inquired about, one from T1To T1Mapping M be defined as：And to all of (V1′,
V2′),(V1″,V2") ∈ M meet following condition：
(1)Represent that the node in two stalk trees is one-to-one；
(2)Represent and keep between mapping ancestors' descendent relationship；
The domain of definition of mapping is：
Range of a mapping is：
For a body tree, it is allowed to the node in tree is inserted, delete and concept transfer this 3 kinds of concept editor
Operation；Therefore, for the subtree of its corresponding binary tree can also carry out this 3 generic operation.
Described step four, including step in detail below：
(41) by the mapping between the fuzzy matching described in step 3 and subtree, obtain it is semantic same or similar but
The inconsistent node of grammer, recombinates to it, and calculates corresponding restructuring cost；
If Q=(V, E, root (Q)), D=(V, E, root (D)) is the son of the described corresponding binary tree of two body trees
Tree, M is the mapping from Q to D, then restructuring cost S (M) for defining M is：
Described group cost includes three parts：Part I for due to label (v) ≈ label (w) change inquiry this
The cost of the concept of body tree tree；Part II is no this node in description body tree tree, and Delete query body tree
The cost of tree interior joint；Part III adds this node to have this node in description body tree tree in query sub tree
Cost.
(42) weight is carried out to described inquiry body tree, description body two body trees of tree according to described restructuring cost
Group, and similar concept is modified using KMP algorithms；
It can be seen from the definition of restructuring cost, when right in the concept for inquiring about certain node on body tree with description body tree
When the concept answered is mismatched, if extracting similar concept collection phase corresponding with this concept in inquiry body tree from similar conceptual base
Seemingly, then this concept in inquiry body tree is replaced；
Assume that the character string that concept set is constituted is：′S1,S2,S3...Sn', abbreviation concept set string；Inquire about the general of body tree
Reading corresponding character string is：′P1,P2,P3...Pn', referred to as inquire about substring；If, now should be with kth (k in inquiry substring<
J) character continues to compare, then inquire about substring in front k-1 character substring must be fulfilled for following relationship satisfaction ' p1,p2,
...pk-1'=' si-k+1,si-k+2,…si-1', and can not possibly there is k '>k；
According to the result of " part match " for having obtained ' pj-k+1,pj-k+2,...pj-1'=' si-k+1,si-k+2,…si-1',
Can obtain ' p1,p2,...pk-1'=' pj-k+1,pj-k+2,...pj-1'；
Meanwhile, unmatched concept is modified, so that concept substring is consistent with inquiry substring.
Compared with prior art, the present invention includes advantages below and beneficial effect：
The present invention can accurately have found target member using semantic similarity, effectively improve component search efficiency and
Reusability, shortens the construction cycle of software.
Description of the drawings
Fig. 1 is a kind of method flow diagram of embodiment of the present invention.
Fig. 2 is a kind of binary tree of the corresponding description body tree of description document with description body tree of embodiment of the present invention
Schematic diagram.
Fig. 3 is a kind of schematic diagram of the fuzzy matching with subtree series of embodiment of the present invention.
Fig. 4 is the schematic diagram that a kind of component of embodiment of the present invention inquires about framework.
Specific embodiment
The present invention is described in further details below in conjunction with the accompanying drawings.
Fig. 1 is a kind of method flow diagram of embodiment of the present invention.Present invention method is comprised the following steps：
Step one, user send component retrieval request to software component library according to demand, and software component library receives user's
After request, corresponding Ontology tree is built according to the component demand of user；
Step 2, described software component library are according to the demand of user, OWL (body web language of the coarseness ground from component
Speech) description document library in retrieve corresponding component description body tree, including procedure below：
(21) document is described using the OWL that OWL is described so as to form component to each field component, is then utilized
XPath extracts Ontological concept therein；But for the base attribute of the objects concept in body is ignored, described basic category
Property includes：Author, date etc..It is as shown in table 1 that the OWL of component describes document.
(22) Ontological concept for extracting XPath carries out hierarchical classification and forms a body tree；Described component description
Document is to there is a description body tree；The corresponding description body tree of component description document in table 1 below as shown in Fig. 2 in order to
The mapping of Ontological concept similarity is better achieved, the description above body tree is converted into into description binary tree.If Fig. 3 is Fig. 2
The binary tree that middle Com1 component representations are converted into.
Step 3, described component description body tree and user's request body tree are carried out into fuzzy matching, its process is：
Save in by calculating the similarity between two component description body trees and user's request body tree interior joint and tree
Point number, i.e., matching length come calculate the matching between two body trees be worth；And Value Realization two is matched according to it
, there are two kinds of situations in this process in the fuzzy matching mapping between body tree；
, there is fuzzy matching between two tree body trees in the first situation：
If T=(V, E, root (T)) is the corresponding binary tree of an arbitrary body tree, then the matching of binary tree T is worth H
(M) it is：
Wherein：Represent the Ontological concept phase of all nodes in tree
Like degree and；︱ v ︱ represent matching length, as set the number of interior joint；
If Q=(V, E, root (Q)) is the corresponding binary tree of inquiry body tree, D=(V, E, root (D)) is description body
Corresponding binary tree is set, wherein：
If：
Then two body trees are called fuzzy matching；Wherein, ≈
Refer to and set up in certain threshold range；Fig. 4 is fuzzy matching figure.
Second situation, if two body trees do not have fuzzy matching, by the section in the binary tree D of inquiry body tree
Point Node and the leaf together with node are divided into ︱ Node ︱ stalk trees；In the same manner also by inquiry body tree binary tree Q also draw
It is divided into some stalk trees；Then by the above-mentioned every stalk tree for marking off to carrying out fuzzy matching；
The binary tree D interior joint Node of inquiry body tree are extracted using following algorithm:
P=com → lchild；
while(p→rchild！=null) { p=p → rchild；printf(″p″)；}
Thus, egress series is decomposed to obtain；Then corresponding subtree series is obtained.Meanwhile, according to same decomposition rule
The binary tree Q of inquiry body tree is resolved into into subtree；As shown in Figure 1.
In the latter case, to the subtree for inquiring about the binary tree D of body tree and the subtree of the binary tree Q for inquiring about body tree
Mapped, if T1=(V1,E1,root(T1)), T2=(V2,E2,root(T2)) it is respectively the son for inquiring about body tree binary tree D
Tree and the subtree of body tree binary tree Q is inquired about, one from T1To T1Mapping M be defined as：And to all of (V1′,
V2′),(V1″,V2") ∈ M meet following condition：
(1)Represent that the node in two stalk trees is one-to-one；
(2)Represent and keep between mapping ancestors' descendent relationship；
The domain of definition of mapping is：
Range of a mapping is：
For a body tree, it is allowed to the node in tree is inserted, delete and concept transfer this 3 kinds of concept editor
Operation；Therefore, for the subtree of its corresponding binary tree can also carry out this 3 generic operation.
If step 4, described component description body tree are unsuccessful with matching for user's request body tree, to mismatch
Component carry out component restructuring；And using KMP algorithms the similar concept for inquiring about body tree is modified and match again；
Step 5, the automatic mapping that document and corresponding component are described according to the OWL of component, make user examine from component base
Rope is to corresponding component.
The step of embodiment of the present invention four, including procedure below：
(41) by the mapping between the fuzzy matching described in step 3 and subtree, obtain it is semantic same or similar but
The inconsistent node of grammer, recombinates to it, and calculates corresponding restructuring cost；
If Q=(V, E, root (Q)), D=(V, E, root (D)) is the son of the described corresponding binary tree of two body trees
Tree, M is the mapping from Q to D, then restructuring cost S (M) for defining M is：
Described group cost includes three parts：Part I for due to label (v) ≈ label (w) change inquiry this
The cost of the concept of body tree tree；Part II is no this node in description body tree tree, and Delete query body tree tree
The cost of interior joint；Part III adds the cost of this node to have this node in description body tree tree in query sub tree.
(42) weight is carried out to described inquiry body tree, description body two body trees of tree according to described restructuring cost
Group, and similar concept is modified using KMP algorithms；
It can be seen from the definition of restructuring cost, when right in the concept for inquiring about certain node on body tree with description body tree
When the concept answered is mismatched, if extracting similar concept collection phase corresponding with this concept in inquiry body tree from similar conceptual base
Seemingly, then this concept in inquiry body tree is replaced；
Assume that the character string that concept set is constituted is：′S1,S2,S3...Sn', abbreviation concept set string；Inquire about the general of body tree
Reading corresponding character string is：′P1,P2,P3...Pn', referred to as inquire about substring；If, now should be with kth (k in inquiry substring<
J) character continues to compare, then inquire about substring in front k-1 character substring must be fulfilled for following relationship satisfaction ' p1,p2,
...pk-1'=' si-k+1,si-k+2,…si-1', and can not possibly there is k '>k；
According to the result of " part match " for having obtained ' pj-k+1,pj-k+2,...pj-1'=' si-k+1,si-k+2,…si-1',
Can obtain ' p1,p2,...pk-1'=' pj-k+1,pj-k+2,...pj-1'；
Meanwhile, unmatched concept is modified, so that concept substring is consistent with inquiry substring.
Otherwise：
1. when being matching for the first time (when i.e. as n=1), carry out resolving into ︱ Node ︱ stalk trees by node (Node), so
Matched again afterwards, if the match is successful, terminated.
2. when there is matching more than twice (i.e. when n >=2), recombinated according to restructuring cost, and using KMP algorithms
The modification of similar concept is carried out, until all of Sub-tree Matching terminates.
To check the effect of the method for the invention, experimental verification is done as follows：
A component inquiry prototype based on body is developed using Eclipse, using Prot é g é ontology modeling tool structures
Field member bodies are built.Adopt 120 components in experiment altogether, in being stored in component base, wherein most component is from Shanghai
The free components extracted in government's component base.And each component is described respectively using OWL and faceted description mode.
Experimental result is respectively adopted based on the search method of keyword, base with precision ratio and recall ratio parameter as a comparison
Search method in facet and the search method based on body similarity are compared analysis.
By experimental data, the component querying method based on body is on precision ratio and recall ratio apparently higher than being based on
The querying method of keyword and the querying method based on facet.Higher than 23.4% based on facet in terms of precision ratio, higher than base
In the 47.5% of keyword；Higher than 31.2% based on facet in terms of recall ratio, higher than 51.9% based on keyword.
Therefore, the present invention is proposed based on the component retrieval algorithm of Ontological concept similarity, and the OWL of component is described into document
It is converted into body tree and carries out fuzzy matching.Then, the component of mismatch is recombinated.On this basis, using KMP algorithms pair
The similar concept of inquiry body tree is modified, and so as to be met the component collection of user's request, improves the efficiency of software development.
The OWL of table 1, component describes document：
。
Claims (5)
1. a kind of software component retrieval method based on Ontological concept similarity, it is characterised in that comprise the following steps：
Step one, user send component retrieval request to software component library according to demand, and software component library receives the request of user
Afterwards, corresponding Ontology tree is built according to the component demand of user；
, according to the demand of user, coarseness ground is described in document library from the OWL of component to be retrieved for step 2, described software component library
Go out corresponding component description body tree；
Step 3, described component description body tree and user's request body tree are carried out into fuzzy matching；
If step 4, described component description body tree match unsuccessful structure to mismatch with user's request body tree
Part carries out component restructuring；And using KMP algorithms the similar concept for inquiring about body tree is modified and match again；
Step 5, the automatic mapping that document and corresponding component are described according to the OWL of component, make user retrieve from component base
Corresponding component.
2. a kind of software component retrieval method based on Ontological concept similarity according to claim 1, it is characterised in that
In described step two, described describing from the OWL of component retrieve in document library corresponding component description body tree, including
Procedure below：
(21) document is described using the OWL that OWL is described so as to form component to each field component, is then utilized
XPath extracts Ontological concept therein；But for the base attribute of the objects concept in body is ignored, described basic category
Property includes：Author, date；
(22) Ontological concept for extracting XPath carries out hierarchical classification and forms a body tree；Described component description document
To there is a description body tree；In order to the mapping of Ontological concept similarity is better achieved, the description above body tree is turned
Turn to description binary tree.
3. a kind of software component retrieval method based on Ontological concept similarity according to claim 1, it is characterised in that
In described step three, described component description body tree carries out fuzzy matching with user's request body tree, refers to：
By calculating the similarity between two component description body trees and user's request body tree interior joint and setting interior joint
Number, i.e. matching length come calculate the matching between two body trees value；And two sheets of Value Realization are matched according to it
, there are two kinds of situations in this process in the fuzzy matching mapping between body tree；
, there is fuzzy matching between two tree body trees in the first situation：
If T=(V, E, root (T)) is the corresponding binary tree of an arbitrary body tree, then matching value H (M) of binary tree T
For：
Wherein：Represent the Ontological concept similarity of all nodes in tree
With；︱ v ︱ represent matching length, as set the number of interior joint；
If Q=(V, E, root (Q)) is the corresponding binary tree of inquiry body tree, D=(V, E, root (D)) is right for description body tree
The binary tree for answering, wherein：
If：
Then two body trees are called fuzzy matching；Wherein, ≈ is referred to
Set up in certain threshold range；
Second situation, if two body trees do not have fuzzy matching, by the node in the binary tree D of inquiry body tree
Node and the leaf together with node are divided into ︱ Node ︱ stalk trees；In the same manner also by inquiry body tree binary tree Q also divide
For some stalk trees；Then by the above-mentioned every stalk tree for marking off to carrying out fuzzy matching；
The binary tree D interior joint Node of inquiry body tree are extracted using following algorithm:
P=com → lchild；
while(p→rchild！=null) { p=p → rchild；printf("p")；}
Thus, egress series is decomposed to obtain；Then corresponding subtree series being obtained, meanwhile, will look into according to same decomposition rule
The binary tree Q for asking body tree resolves into subtree；
In the latter case, the subtree to inquiring about the subtree of the binary tree D of body tree and the binary tree Q of inquiry body tree is carried out
Mapping, if T1=(V1,E1,root(T1)), T2=(V2,E2,root(T2)) respectively the subtree of inquiry body tree binary tree D with
The subtree of inquiry body tree binary tree Q, one from T1To T1Mapping M be defined as：And to all of (V1′,V2′),
(V1″,V2") ∈ M meet following condition：
(1)Represent that the node in two stalk trees is one-to-one；
(2)Represent and keep between mapping ancestors' descendent relationship；
The domain of definition of mapping is：
Range of a mapping is：
For a body tree, it is allowed to the node in tree is inserted, delete and concept transfer this 3 kinds of concept editor behaviour
Make；Therefore, for the subtree of its corresponding binary tree can also carry out this 3 generic operation.
4. a kind of software component retrieval method based on Ontological concept similarity according to claim 1, it is characterised in that
Described step four, including step in detail below：
(41) by the mapping between the fuzzy matching described in step 3 and subtree, semantic same or similar but grammer is obtained
Inconsistent node, recombinates to it, and calculates corresponding restructuring cost；
If Q=(V, E, root (Q)), D=(V, E, root (D)) is the subtree of the described corresponding binary tree of two body trees, M
It is the mapping from Q to D, then restructuring cost S (M) for defining M is：
(42) described inquiry body tree, description body two body trees of tree are recombinated according to described restructuring cost, and
Similar concept is modified using KMP algorithms；
According to restructuring cost definition understand, when inquiry body tree on certain node concept with describe body tree in it is corresponding
When concept is mismatched, if extraction similar concept collection corresponding with this concept in inquiry body tree is similar from similar conceptual base,
Then replace this concept in inquiry body tree；
Assume that the character string that concept set is constituted is：′S1,S2,S3…Sn', abbreviation concept set string；The concept institute of inquiry body tree is right
The character string answered is：′P1,P2,P3…Pn', referred to as inquire about substring；If, now should be with kth (k in inquiry substring<J) character after
It is continuous to compare, then inquire about front k-1 character in substring substring must be fulfilled for following relationship satisfaction ' p1,p2,...pk-1'='
si-k+1,si-k+2,…si-1', and can not possibly there is k '>k；
According to the result of " part match " for having obtained ' pj-k+1,pj-k+2,...pj-1'=' si-k+1,si-k+2,…si-1', can be with
Obtain ' p1,p2,...pk-1'=' pj-k+1,pj-k+2,...pj-1'；
Meanwhile, unmatched concept is modified, so that concept substring is consistent with inquiry substring.
5. a kind of software component retrieval method based on Ontological concept similarity according to claim 4, it is characterised in that
Described group cost includes three parts：Part I for due to label (v) ≈ label (w) change inquiry body tree tree
Concept cost；Part II is no this node in description body tree tree, and Delete query body tree tree interior joint
Cost；Part III adds the cost of this node to have this node in description body tree tree in query sub tree.
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Application publication date: 20170419 Assignee: NUPT INSTITUTE OF BIG DATA RESEARCH AT YANCHENG Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS Contract record no.: X2021980013920 Denomination of invention: A software component retrieval method based on ontology concept similarity Granted publication date: 20200421 License type: Common License Record date: 20211202 |