=Paper=
{{Paper
|id=Vol-1741/jist2016pd_paper8
|storemode=property
|title=Ontology Refinement System for Improving Consistency of Classification among Brother Concepts
|pdfUrl=https://ceur-ws.org/Vol-1741/jist2016pd_paper8.pdf
|volume=Vol-1741
|authors=Takeshi Masuda,Kouji Kozaki,Kazunori Komatani
|dblpUrl=https://dblp.org/rec/conf/jist/MasudaKK16
}}
==Ontology Refinement System for Improving Consistency of Classification among Brother Concepts==
https://ceur-ws.org/Vol-1741/jist2016pd_paper8.pdf
Ontology Refinement System for Improving Consistency
of Classification among Brother Concepts
Takeshi Masuda, Kouji Kozaki, Kazunori Komatani
The Institute of Scientific and Industrial Research, Osaka University
Abstract. The consistency of classification is an indicator of ontologies’ quality.
In this paper, we focus on the consistency among brother concepts and develop
a refinement system that finds inconsistent parts from a target ontology and pro-
pose methods to make such parts consistent. To find inconsistent parts and make
proposals, the system compares a slot hierarchy and other two hierarchies that
have reference relationship to the slot hierarchy.
1 Introduction
Nowadays, ontologies are constructed in various fields such as medical information,
mechanical design and so on. These ontologies are used as a schema of knowledge
models for application systems. Therefore a construction of better quality ontology is a
considerable issue. However, both experience of ontology construction and expertise
in the target domain are required to build well organized ontologies. Therefore, it is not
easy for beginners to construct good ontologies. Because of these backgrounds, there
are some systems that correct some formal errors in ontology and these are embedded
in ontology editor like Protégé [1, 2, 3]. While in the case of quality improvement, we
have to investigate each concept to check whether the concept should be refined or not.
Therefore, we aim to develop the Ontology Refinement support system.
2 Ontology Refinement by Comparing Is-a Hierarchies
2.1 Similarity among Is-a Hierarchies
In order to develop a refinement method, we focus on an ontology development
guideline that “Each subclass of a super class is distinguished by the values of exactly
one attribute of the super class. [4]”, and found the interesting characteristic of among
is-a hierarchies under the guideline. We found that conceptual structures are similar to
other is-a hierarchies in ontologies which follow the guideline. For example, in Fig.1,
these 3 hierarchies, “Basic Concept Hierarchy”, “Slot Hierarchy” and “Referred Con-
cept Hierarchy”, are following guideline and then their structure are similar. In this
paper, our refinement system compare these 3 hierarchies and detect un-similar parts
as a refinement candidates and make proposals to add new concepts for each candidates.
Basic Concept Hierarchy
Basic Concept Hierarchy
Vehicle B BM1 B_BR1
Carriage Movement S
Space
Movement Ground
Space
Natural Area Area of BM2 BL1
Airplane refinement SL1
Movement candidate reference
Space BL2
Reference Air at the previous
B_BR2 SL2
method
Slot Hierarchy Reference R RM1 RL1 reference
Natural Area
Ground R_BR1 RL2
Referred Concept Hierarchy R_BR4 R_BR2
Referred Concept Hierarchy Air
R_BR3
Fig.1 similarity among is-a hierarchy Fig.2 Comparing Brother Concepts
2.2 Comparing among Brother Concepts
In our previous method [5], we compared 3 hierarchies but it is only use super-sub
relations whereas in this paper, we also consider comparing among brother concepts’
hierarchies. To compare these 3 hierarchies with brother concepts, we focus on “Slot
Hierarchy” as a basis. “Slot Hierarchy” consists of a certain slot “S” and its lower slots
“SLm” (m = 1 ~ M, M is a number of lower slots). These lower slots are specialized
only once from “S”. In this case, “Basic Concept Hierarchy” consists of concepts that
has slots in slot hierarchy (“B”, “BLm”), these brother concepts (“B_BRx”, x = 1~ X, X
is a number of brother concepts.) and middle concepts (“BMa”, a = 1~ A, A is a number
of middle concepts.) (Fig.2.). “Referred Concept Hierarchy” consists likewise.
2.3 Patterns of Refinement Candidates
Comparing 3 hierarchies like Fig.2, if there are no brother concepts (B_BRx, R_BRy)
that did not have (or be referred from) “Sm”, these 3 hierarchies are not similar. So we
can classify patterns of refinement candidate by existence of B_BRx or R_BRy. To sum
up, patterns of refinement candidate are the following four. (i) X > 0 and Y > 0, (ii) X
= 0 and Y > 0, (iii) X > 0 and Y = 0, (iv) X = 0 and Y = 0. (X is a number of bother
concepts in “Basic Concept Hierarchy, Y is a number of bother concepts in “Referred
Concept Hierarchy”. We do not make any proposals for (iv) because it is already simi-
lar.)
2.4 Limitations on Refinement Proposals
In our refinement method, refinement proposals are 3 types as follows. A corre-
spondence between refinement candidates and proposals are shown at Table.1.
(a). add new Slot
(b). add new concept to Basic Concept Hierarchy and new Slot
(c). add new concept to Referred Concept Hierarchy and new Slot
Table.1. correspondence between refinement candidates and proposals
Concept
Where to Acceptable A number of
chosen as
add new slot Candidate Type proposals
class constraint
a 1 R_BRy (i) X*Y
B_BRx
2 R_Lm (i), (iii) X*M
b 1 New concept R_BRy (i), (ii) X(not leaf)*Y
2 in BCH Rn_Lm (i), (ii), (iii) X(not leaf)*M
New concept (i), (iii) Y(not leaf)*X
c 1 B_BRx
in RCH
BCH : basic concept hierarchy, RCH : referred concept hierarchy
B_BRx: brother concepts that donʼt have slot (x︓1..X)
R_BRy: brother concepts that are not referred (y︓1..Y)
M︓a number of referred concepts
At this comparison with brother concept hierarchies, a number of refinement pro-
posal become enormous. Because there are much more comparison concepts in brother
concepts hierarchies than comparison among upper and lower concepts. For example,
I assume that some numbers M = 2 (lower slot of “S”), X = 40 (brother concept in basic
concept hierarchy “B_BRx”) and number of leaf concepts is 18, Y = 3 (brother concept
in referred concept hierarchy R_BRy) and number of leaf concepts is 2. This example
is X > 0 and Y > 0, then candidate type is (i). From table.1, all proposals, a1 ~ c1, are
suggested. Therefore total number of proposals for this example is 328. This number is
quite large to consider as refinement proposal, even this example has a few brother
concept in referred concept hierarchy. If there are same number of brother concepts in
referred concept hierarchy, a number of proposals can be over a thousand per a candi-
dates. For the above reasons, we consider 2 limitations on refinement proposals.
limitation1: Compare brother concepts that have same parents
limitation2: Compare brother concepts that are specialized same level.
3 Evaluation
3.1 Evaluation Methods
We conducted a pre-experience to evaluate this refinement proposal method. We use
a race ontology that is made by author and this ontology contains 213 concepts. This
experience was designed to asses 2 points: (1) how many appropriate candidates are
detected and (2) how the 2 limitations work.
3.2 Results and Discussions
(1) How many appropriate candidates are detected.
Table.2 shows the result. By previous method, 149 candidates are detected and 18%
are proposed correctly. While by new method, 11 candidates are detected and 63% are
proposed appropriately. Both new and previous method’s candidates are not repeated.
However average number of proposals are 1154, which is too much to consider.
(2) How the 2 limitations work
Table.3 shows the result. By “limitation1”, average number of proposals decreased
to 13, it is 100 times fewer than no limitation. In this case, 4 candidates still have correct
proposal under “limitation 1”, but 3 candidates cannot be proposed any suggestions..
While by “limitation 2”, average proposals also decreased to 377 but it is still enormous
to see. But it has 5 correct candidates, it is better than “limitation1”.
Table.2. Comparison between previous and new refinement method
All A number of Candidates A percentage of Candidates Average number of
candidates that have correct proposals that have correct proposals proposals
Previous method 149 27 18% 7
New method 11 7 63％ 1154
Table.3. Result of the 2 limitations
All Candidates Candidates Average number of
Candidates that have correct proposals that donʼt have any proposals proposals
No limitation 7 7 0 1154
Limitation 1 7 4 3 13
Limitation 2 7 5 2 377
4 Conclusion
In this paper, we focus on similarity among brother concepts’ hierarchies. However
a number of refinement proposals are drastically increased by the explosion of combi-
nation. Then we provide 2 types of limitations to prevent this explosion. As a result, we
could improve an accuracy of proposals and suppress the number of proposals. In future
work, we consider some refinement candidates that we cannot make any proposal and
integrate the previous refinement method and the new refinement method.
Acknowledgements
This work was supported by JSPS KAKENHI Grant Numbers JP25280081, JP
26240033, JP24120002.
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