A Proposal for Screening Inconsistencies in Ontologies based on
Query Languages using WSD
Chieko NAKABASAMI
Toyo University
1-1-1 Izumino, Itakura,
Oura, Gunma, Japan, 374-0193
chiekon@itakura.toyo.ac.jp
Naoyuki NOMURA
Hosei University
2-17-1 Fujimi, Chiyoda-ku,
Tokyo, Japan, 102-8160
nomuran@tokyo.email.ne.jp
Abstract
In this paper, we discuss a method to screen
inconsistencies in ontologies by applying a nat-
ural language processing (NLP) technique, es-
pecially, those used for word sense disambigua-
tion (WSD). In the database research field, it
is claimed that queries over target ontologies
should play a significant role because they rep-
resent every aspect of the terms described in
each ontology. According to (Calvanese et al.,
2001), considering the global and the local on-
tologies, the terms in the global ontology can be
viewed as the query over the local ontology, and
the mapping between the global and the local
ontologies is given by associating each term in
the global ontology with a view. On the other
hand, ontology screening systems should be able
to take advantage of some popular techniques
for WSD, which is supposed to decide the right
sense where the target word is used in a specific
context. We present several examples regard-
ing inconsistencies in ontologies with the aid of
DAML+OIL notation(DAML+OIL, 2001), and
propose that WSD can be one of the promising
method to screen such as inconsistencies.
1 Introduction
In recent years, the semantic web (Berners-Lee
et al., 2001) has been evolving as the next-
generation web technology and has attracted
the attention of many researchers in database
and knowledge engineering communities. In
the semantic web, contributions obtained from
fields related to databases frequently refer to on-
tology maintenance, reuse, and sharing. Based
on the results from database research, this pa-
per proposes a method to screen inconsistencies
in ontologies by applying a natural language
processing (NLP) technique, especially, those
used for word sense disambiguation (WSD).
Many reference books on WSD are available
(e.g., (Manning and Schutze, 1999)). As for
ontology integration, several approarches are
proposed (Mitra et al., 2001)(Euzenat, 2001).
In (Calvanese et al., 2001), global-centric (aka
global-as-view) and local-centric (aka local-as-
view) approaches for an ontology integration
framework are proposed, respectively. In this
paper, we support the global-as-view approach
for screening inconsistencies in ontology. In
addition, (Calvanese et al., 2001) claims that
queries over target ontologies should play a sig-
nificant role because they represent every as-
pect of the terms described in each ontology.
The terms in the global ontology can be viewed
as the query over the local ontology, which de-
scribes a concept definition. On the other hand,
ontology screening systems should be able to
take advantage of some popular techniques for
WSD, which is supposed to decide the right
sense where the target word is used in a spe-
cific context. We claim that WSD is a promis-
ing method for determining which local ontol-
ogy should be used for forming concepts for a
global ontology. This paper contains a brief in-
troduction of the global-centric approach, which
is described in Section 2. In addition, we men-
tion the inconsistencies in ontologies caused by
words with multiple definitions. For example,
the word gbassh is chosen, and we explain that
some queries based on two of its definitions have
concepts in the global ontology. After extract-
ing new concepts by querying the global on-
tology, each local ontology is illustrated with
DAML+OIL (DAML+OIL, 2001). Several in-
consistencies in the ontology are presented in
Section 3 over the global ontology, and the WSD
for solving such inconsistencies are discussed.
The final section is the conclusion.
2 Global-as-view approach
In (Calvanese et al., 2001), global-as-view and
local-as-view approaches are proposed for on-
tology integration. According to the global-as-
view approach, the mapping between the global
and the local ontologies is given by associating
each term in the global ontology with a view.
Let C be a term in the global ontology G, V a
query language over the terms of the local on-
tologies, and M the mapping between the global
and the local ontologies. Given that D is a local
model for the ontology integration system and I
a global interpretation for the system, the cor-
respondence between C and V is specified as
follows by referring to (Calvanese et al., 2001):
•〈C,V,sound〉
if all tuples satisfying V in D satisfy C in
I
•〈C,V,complete〉
if no tuple other than those satisfying V in
D satisfies C in I
•〈C,V,exact〉
if the set of tuples that satisfy C in I is
exactly the set of tuples satisfying V in D.
In the above notation, “I satisfies” means
that I satisfies every correspondence in the
mapping between the global ontology and the
local ontologies wrt D. These correspondences
are valid if the global ontology is assumed to be
consistent; however, inconsistencies might occur
when the term in the global ontology has more
than one definition. We use the word “bass,”
for example, which has at least two definitions,
one, “a man whose singing voice is very low,”
and another, “a kind of fish”(LDOCE, 1995).
Each definition is represented in order with first-
order-language-like notation, as follows:
C(x) ← singer(x),voice(x,low).
C(x) ← aKindOf(x,fish).
Some concepts in the global ontology are de-
scribed using the above two C(x)s. The follow-
ing concept is represented with the first C(x) be-
cause C
1
(x) is a member of an orchestra, which
would be impossible that C
1
(x) is a fish from
the standard context.
C
1
(x) ← C(x),isMemberOf(x,orchestra). (1)
The concept C(x) in (1) can be represented
with the DAML+OIL notation, as shown below.
This concept is assumed to belong to one local
ontology (for example, local-ont-1). In the rep-
resentation, some parts in the representation,
such as namespace prefixes and URIs, are omit-
ted because they do not directly relate to this
paperfs intention. In addition, the referenced
resources and properties whose definitions are
not in the representation are supposed to be de-
fined implicitly.
<rdf:RDF>
<daml:Ontology rdf:about=" ">
<rdfs:comment>local-ont-1
</rdfs:comment>
</daml:Ontology>
<daml:Class rdf:ID="bass">
<rdfs:subClassOf rdf:resource="#singer"/>
<hasVoiceOf rdf:resource=#very_low />
</daml:Class>
<daml:ObjectProperty rdf:ID="hasVoiceOf">
<rdfs:range rdf:resource="#voice"/>
</daml:ObjectProperty>
<daml:Class rdf:ID="voice">
<daml:oneOf rdf:parseType="daml:collection">
<voice rdf:ID="high/">
<voice rdf:ID="medium"/>
<voice rdf:ID="low"/>
<voice rdf:ID="very_low"/>
</daml:oneOf>
</daml:Class>
</rdf:RDF>
C
1
(x) is assumed to be a result
from a query over the global ontology,
“C(x),memberOf(x,orchestra)” The set of
extensions of C
1
is returned as a result of the
query. Once C
1
is formed, ‘x’ as a result of the
global ontology becomes a set of instances for
a new class, and the local ontology is revised
by adding a subclass of the existing class. If
such a new subclass were named “sub-bass,” it
would be represented in this manner:
<daml:Class rdf:ID="sub-bass">
<subClassOf rdf:resource="#bass"/>
</daml:Class>
<daml:Class rdf:about="#sub-bass">
<isMemberOf rdf:resource="#orchestra"/>
</daml:Class>
Let O be an ontology screening system. In
this case, O obtains one concept “sub-bass,”
i.e., “a bass singer working at an orches-
tra,” from the intersection between C(x) and
isMemberOf(x,orchestra). The concept “sub-
bass” is formed by combining the local and
global ontologies. Considering this example, ap-
propriate extensions of the intended concept in
the global ontology are obtained and recognized
only if the right sense of the concept in the local
ontology is used. A diﬀerent concept might be
drawn from diﬀerent senses from other local on-
tologies, but the high rate of co-occurrence be-
tween singer and orchestra led by WSD would
play a crucial role in preventing another sense
from being considered. Let’s consider another
example:
C
2
(x) ← C(x),livesIn(x,river). (2)
In (2), x is likely the second sense of “bass.”
For example, x is an extension of the bass class
as a subclass of fish. WSD would justify it be-
cause the co-occurrence of “fish” and “river”
is rather strong. The local ontology of “bass”
from the second sense (local-ont-2) is repre-
sented with DAML+OIL, as shown below. It
is also revised when a new subclass from query
(2) is added.
<rdf:RDF>
<daml:Ontology rdf:about=" ">
<rdfs:comment>local-ont-2
</rdfs:comment>
</daml:Ontology>
<daml:Class rdf:ID="bass">
<rdfs:subClassOf rdf:resource="#fish"/>
</daml:Class>
<daml:Class rdf:ID="sub-bass">
<subClassOf rdf:resource="#bass"/>
</daml:Class>
<daml:Class rdf:about="#sub-bass">
<livesIn rdf:resource="#river"/>
</daml:Class>
</rdf:RDF>
3 Screening inconsistencies in the
global ontology
According to (Calvanese et al., 2001), the fol-
lowing situations involving inconsistency or am-
biguity occur as a result of the mapping of
global and local ontologies:
1. There are no global models.
This occurs when the data in the exten-
sion of the local ontologies do not satisfy
the constraints for functional attributes.
For example, in (Calvanese et al., 2001), a
property “age” is considered. Since “age”
is a function, the value as its range must be
defined with only one value. However, the
global ontology does not have a model con-
cerning “age” any more if “age” has more
than one value from the constraint of the
concepts in a local ontology.
2. There are several global models.
This occurs when the data in the exten-
sion of the local ontologies do not satisfy
the ISA relationships of the global ontol-
ogy: i.e., several ways exist to add suit-
able instances to the elements of the global
ontology in order to satisfy the constraints
from a local ontology. For example, it is as-
sumed that a “student” must be enrolled in
a certain university. More than one univer-
sity can be considered when the constraints
in a local ontology do not mention which
university each extension of “student” is en-
rolled in from the concept definitions of the
local ontology. Such ambiguities depend on
how precisely the concepts of a local ontol-
ogy are defined. As a result, all interpreta-
tions for an intended ontology integration
system within which a valid concept exists
must be accepted as models because of the
potential ambiguities present in local on-
tologies.
We propose two other solutions to the unsuit-
able situations described above; the first relates
to yielding invalid extensions, and the second
to forming wrong concept definitions as a result
of WSD failure. For an explanation of the first
situation, consider this example:
Example 1. Re-considering (2) in Section 2.
C
2
(x) ← C(x),livesIn(x,river). (3)
concept
(bass who lives in a river)
Ichiro
livesIn(x,river)
hasAddressOf(x,Tokyo)
man
hasAddressOf()
extension
extension
local ontology
bass
(a subclass of singer)
local ontology
bass
(a subclass of fish)
bass(x), livesIn(x,river)?
wrong choice
global ontology
? Tokyo = river
’=’ means "same category"
Figure 1: An inconsistency resulting from a wrong choice from word “bass”
In (3), concept C
2
is assumed to be formed
from concept C, which has the meaning of “bass
as a singer.” The meaning of C
2
is “a bass that
lives in a certain river.” Provided that “Ichiro”
is an extension of C
2
, it could be said that
Ichiro lives in a river. In an ontology hierarchy,
there would be multiple inheritances around one
term. Suppose “Ichiro” is also an extension of
the class “man.” If the class “man” has a prop-
erty called “isAddressOf,” which is defined as
the same property of “livesIn,” “Ichiro” must
have an instantiated address. When his ad-
dress is “Tokyo,” an inconsistency arises be-
cause “Tokyo” and “river” are not classified in
the same category. In addition, this inconsis-
tency in terms of having an inappropriate value
of properties is propagated towards lower sub-
classes of the target class. This situation is ex-
pressed in Figure 1. We claim that a situation
such as this should be solved using WSD be-
cause of the strong relationship between “fish”
and “river” mentioned in Section 2.
Example 2. The second situation concerns
WSD failure. Suppose the following query:
C
3
(x) ← C(x),hasHobbyOf(x,swimming).
(4)
In (4), C
3
would be assumed to be a concept
referring to a subclass of person from the ordi-
nary context. However, the ontology screening
system might classify the concept C
3
into the
wrong place in the global ontology if it takes
the concept C as the second meaning of “bass,”
i.e., a kind of fish. Deriving the related concepts
in the global ontology on the basis of a wrong
concept whose meaning is never intersectable
with the right one causes an unexpected incon-
sistency over the global ontology. After C
3
is
mistaken, the local ontology would result in a
concept with the meaning “a bass whose hobby
is swimming” in spite of the fact that a bass is
a type of fish.
<rdf:RDF>
<daml:Ontology rdf:about=" ">
<rdfs:comment>local-ont-2
</rdfs:comment>
</daml:Ontology>
<daml:Class rdf:ID="bass">
<rdfs:subClassOf rdf:resource="#fish"/>
</daml:Class>
<daml:Class rdf:ID="sub-bass">
<subClassOf rdf:resource="#bass"/>
</daml:Class>
<daml:Class rdf:about="#sub-bass">
<livesIn rdf:resource="#river"/>
<hasHobbyOf rdf:resource="#swimming"/>
</daml:Class>
</rdf:RDF>
The above situation has not yet been solved
because the strength of the relationship between
“fish” and “swimming” is rather high. In order
to solve such problems, WSD should be applied
to the property names as well as the property
values: i.e., “hobby.”
4 Conclusion
In this paper, we described a perspective for the
mapping between the global and the local on-
tologies based on (Calvanese et al., 2001). We
support the global-as-view approach for main-
taining the global ontology. This approach is
thought to be promising, but it has some un-
solved problems concerning inconsistencies in
ontologies. Word sense disambiguation, called
WSD, can provide one clue for solving these
problems because it draws certain close relation-
ships among words appearing as property val-
ues or concept definitions. In contrast to (Cal-
vanese et al., 2001), we proposed two situations
in which there is ontology inconsistency because
one word has multiple meanings: the first case
was that of a query based on the wrong choice
of meaning for a word, and the second situa-
tion involved the inability to use WSD to de-
cide the meaning of a concept. To solve these
problems, more precise disambiguation methods
should be developed in WSD, or more elaborate
representations should be provided for the con-
straints in ontology definitions. On the other
hand, the ontology screening system proposed
in this paper can be easily implemented us-
ing query engines (e.g., (RQL, 1994)(RDFQL,
2000)(Miller, 2001)(JENA, 2001) based on RDF
Query (RDFQuery, 1998) as one of the alterna-
tives. A query language must play a crucial role
in the formation of concepts and the screening
for inconsistencies over intended ontologies. In
the semantic web world, we believe that screen-
ing for inconsistencies in ontologies will become
critical and that the use of NLP methods like
WSD to screen for inconsistencies will oﬀer a
significant contribution.

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