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Dual Patterns for Mapping between two ontologies
B.L.Saranya
Post Graduate Student, Department of Computer Science
Pondicherry
University, Pondicherry - 14.
blsaranya@yahoo.in
Ontology provides meaning to the largely disintegrated on web and it is an integral part of semantic web. Its basic operation is mapping ontologies which provide a common layer from which several ontologies can be accessed and hence can exchange information in a better way. But because of the unsatisfactory performance in mapping, some background knowledge is needed. Many single arrangement methods exist for communicating between ontologies. But these methods failed to perform simultaneous and multi arrangement techniques. To overcome these drawbacks, this work focuses on dual arrangement method for better and effective exchange of information between two ontologies.
1. Introduction
Semantic Web
is a vision in which computers, software as well as humans can find,
read, understand and use the data over the World Wide Web to accomplish
useful goals. Ontology plays a significant role towards the realization
of semantic web by describing semantics of information explicitly. According
to a report by marketing research firm Gartner [1], Ontologies are identified
as one of the leading IT technologies (ranking it third in its list
of top 10 technologies). As lots of ontologies are developed by different
people and organizations, the heterogeneity between different ontologies
is inevitable. To overcome the heterogeneity and establish interoperability
between agents or services, mapping between ontologies is necessary.
Mapping Ontologies
is defined as “Given two ontologies O1 and O2, mapping one ontology
onto another means that for each entity (concept C, relation R, or instance
I) in ontology O1, we try to find a corresponding entity, which has
the same intended meaning in ontology O2. It can be done either manually
or using semi-automated/automated tools. Fully or semi-automated mapping
approaches have been examined by several research studies, e.g., analyzing
linguistic information of elements in ontologies. Automatic ontology
mapping is important to various practical applications such as the emerging
Semantic Web, query processing across disparate sources, and many others.
Arrangement
or patterns can play an important role in the specification of ontology
mappings. They are very useful in guiding the developer of ontology
mapper to correctly construct ontology mappings. They can also be used
as a guide for developers of ontology matching algorithms. These arrangement
or patterns are exploited to overcome the unsatisfactory performance
of ontology mappings. Many single arrangement methods exist in literature.
But these arrangement methods or patterns are only one-to-one, non-simultaneous
and are not used in ontology merging, agent communication, query answering
and ontology mapping projects. To overcome these issues, dual arrangement
methods are proposed.
2. related work
Ondrej Svab [2] describes a mapping arrangement or pattern as a graph structure, where nodes are classes, properties or instances. Edges represent mappings, relations between elements (eg. domain and range of properties) or structural relations between classes (eg. subclasses or siblings). In this work three simple mapping arrangements or patterns are examined. In first mapping arrangement or pattern: The left-hand side (class A) is from ontology O1 and the right-hand side (class B and its subclass C) is from ontology O2. There is a mapping between A and B and between A and C. The second mapping arrangement or pattern is quite similar to the previous one, but child and a parent from each ontology are mapped. The third mapping arrangement or pattern consists of mappings between class A from O1 and two sibling classes C and D from O2. They are particularly interested in two types of ontology design patterns: naming conventions nd structural patterns. Naming conventions are related to naming classes, properties and/or instances. Structural patterns concern the modeling choices in using certain ontology entities and connecting them together.
Jos de Bruijn et al., [3] have provided a number of ontology mapping arrangement or patterns, as well as a Language independent ontology mapping language, based on these patterns. The mapping language and mapping patterns are mutually dependant. They structured the elements according to the four meta elements namely: name, problem, solution and consequences.
According to Fancois Scharffw [4] Mapping Arrangement or Patterns are templates that match the more usual mistakes between two ontologies. The use of predefined patterns considerably reduces the mapping designer’s task. They proposed the use of a pattern language to define them, a pattern library allowing storing and retrieving them efficiently. The Graphical User Interface allows the user to select entities from the ontologies and to apply a pattern on them. This interface uses some modules of the Ontology Editing and Browsing tool.
Ondrej Svab [5] considered three categories of mapping arrangement or patterns: content patterns, logical patterns and frequent errors. Content patterns use specific non-logical vocabulary and describe a recurring, often domainindependent state of affairs. Logical patterns, in turn, capture the typical ways of modeling problems which can be tackled in a specific ontological language. Frequent errors (though not usually denoted as patterns, they are clearly so) describe inadequate constructions that are often used by inexperienced modelers.
Ming Mao [6]
proposes a new generic and scalable ontology mapping approach, it takes
advantage of propagation theory, information retrieval technique and
artificial intelligence model to solve ontology mapping problem. It
utilizes both linguistic and structural information of ontologies, measures
the similarity of different elements of ontologies in a vector space
model, and integrates interactive activation network to deal with constraints.
Most existing systems for ontology mapping combine various methods for
achieving higher performance in terms of recall and precision.
Ondrej Svab et al., [7] approach relies on Bayesian networks (BNs) as well-known formal technique that can capture interdependencies among random variables. A mapping arrangement or pattern is, essentially, a structure containing some (at least one) constructs from each of the two (or more) ontologies plus some (candidate) mapping among them. The simplest mapping arrangement only considers one concept from each of the two ontologies. A bit more complex mapping arrangement is one concept from first ontology maps with two or more concepts from second ontology simultaneously. Using ontologies in a dynamic environment, such as a Grid, to share some common concepts is still a challenge. It is difficult to keep a static mapping between ontologies.
Nelson et al., [8] have adopted the concept of Tuple Space and proposed a flexible approach for managing ontologies in a Grid. This approach simplifies the communication process and provides flexibility of participation of all participants.
3.1 Need
As the number of ontologies which are being publicly available and accessible on the web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the semantic web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. To achieve interoperability between agents or services, mapping is needed.
3.2 Process
Mapping ontologies
specifies how the concepts in the different ontologies are related in
a logical sense. This means that original ontologies have not changed
but the additional axioms describe the relationship between the concepts.
Mapping process involves import of ontologies, similarity finding and
mapping ontologies. Figure 1 depicts this process flow.
O1
O2
Import
Ontologies
Finding
Similarities
Mapping
Ontologies
Figure 1. Ontology
Mapping Process
Import of
ontologies: Ontologies can be specified in different languages,
which indicate a need to convert them to a common format in order to
be able to specify the mapping.
Finding Similarities: Many methods use match operator to semi-automatically find similarities between schemas or ontologies. For any two source ontologies, the Match operator returns the similarities between the ontologies. We distinguish this phase in the mapping process only when the similarities are determined in an automatic fashion. If the mapping process is completely manual, this phase is skipped.
Mapping
ontologies: After finding the potential similarity between the two
ontologies, the ontologies are mapped. This is usually a manual process
but can be done automatically.
3.3 Categories
Ontology mapping
can be classified into the following three categories [9]: Mapping between
an integrated global ontology and local ontologies, Mapping between
local ontologies and Mapping on ontology merging and alignment. The
first category of ontology mapping supports ontology integration by
describing the relationship between an integrated global ontology and
local ontologies. The second category enables interoperability for highly
dynamic and distributed environments as mediation between distributed
data in such environments. The third category is used as a part of ontology
merging or alignment as an ontology reuse process. One of the crucial
differences among the three ontology mapping categories is how mapping
among ontologies is constructed and maintained. Each category of ontology
mapping has different characteristics.
3.4 Challenges
Although a
lot of functions and methods are available for tackling the problem
of ontology mapping, there are still some new challenges for researchers
and underline new directions for the future. Some of the challenges
imposed by ontology mapping are discussed below:
Largeness,
complexity, and heterogeneity of ontologies and their mapping results
bring about a bundle of challenges which need to be addressed in future
research.
3.5 Usage
The two most
important uses of mappings required for information integration: mappings
between ontologies and the information they describe and mapping between
different ontologies used in a system are specified [10].
Connection
to Information Sources:
The first and most obvious application of mappings is to relate the
ontologies to the actual content of an information source. Ontologies
may relate to the database scheme, but also to single terms used in
the database. Regardless of this distinction, we can observe different
general approaches used to establish a connection between ontologies
and information sources.
Structure Resemblance: A straightforward approach for connecting the ontology with the database scheme is to simply produce a one-to-one copy of the structure of the database and encode it in a language that makes automated reasoning possible. The integration is then performed on the copy of the model and can easily be tracked back to the original data.
3.6 Mapping Arrangements
or Patterns
Mapping Arrangements
or Patterns is a template that is used to define the data structure
for mapping two ontologies. The data structure differs according to
the pattern. Each pattern has its own functionalities. Mapping Arrangements
or Patterns can play an important role in the specification of ontology
mapping, because they have the potential to make mappings more concise,
better understandable and reduce the number of errors.
3.7 Drawbacks of Single
Arrangement Methods
4. PROPOSED WORK
The
pictorial representation of mapping Concept-Attribute to Concept-Attribute
is shown in Figure 2. In this mapping arrangement, concept from ontology
1 is mapped to concept in ontology 2 and at the same time attribute
from ontology 1 is mapped to attribute in ontology 2.
Ontology 1
Concept
Attribute
Ontology 2
Concept
Attribute
Figure 2. Concept-Attribute to Concept-Attribute Mapping Arrangement
The pictorial
representation of mapping Concept-Relation to Concept-Relation is shown
in Figure 3. In this mapping arrangement, concept from ontology 1 is
mapped to concept in ontology 2 and at the same time relation from ontology
1 is mapped to relation in ontology 2.
Ontology 2
Concept
Relation
Ontology 1
Concept
Relation
Figure 3. Concept-Relation
to Concept-Relation Mapping Arrangement
The pictorial
representation of mapping Concept-Value to Concept-Value is shown in
Figure 4. In this mapping arrangement, concept from ontology 1 is mapped
to concept in ontology 2 and at the same time value from ontology 1
is mapped to value in ontology 2.
Ontology 2
Concept
Value
Ontology 1
Concept
Value
Figure 4. Concept-Value
to Concept-Value Pattern Mapping Arrangement
The pictorial
representation of mapping Attribute-Relation to Attribute-Relation is
shown in Figure 5. In this mapping arrangement, attribute from ontology
1 is mapped to attribute in ontology 2 and at the same time relation
from ontology 1 is mapped to relation in ontology 2.
Ontology 2
Attribute
Relation
Ontology 1
Attribute
Relation
Figure 5. Attribute-Relation
to Attribute-Relation Mapping Arrangement
The pictorial
representation of mapping Attribute-Value to Attribute-Value is shown
in Figure 6. In this mapping arrangement, attribute from ontology 1
is mapped to attribute in ontology 2 and at the same time value from
ontology 1 is mapped to value in ontology 2.
Ontology 2
Attribute
Value
Ontology 1
Attribute
Value
Figure 6. Attribute-Value
to Attribute-Value Mapping Arrangement
The pictorial
representation of mapping Relation-Value to Relation-Value is shown
in Figure 7. In this mapping arrangement, relation from ontology 1 is
mapped to relation in ontology 2 and at the same time value from ontology
1 is mapped to value in ontology 2.
Ontology 2
Relation
Value
Ontology 1
Relation
Value
Figure 7. Relation-Value
to Relation-Value Mapping Arrangement
4.3 Design Component
Dual arrangement
for mapping two ontologies is a technique which maps two entities from
one ontology to two entities in another ontology at the same time. This
process encompasses Ontology Repository, Input Entity, Similarity Selector,
Similarity Checker, Dual Arrangement Assembler and Output Entity. Figure
8 shows the process flow of dual arrangement for mapping two ontologies.
Ontology Repository:
Repository
is a collection of resources that can be accessed to retrieve information.
They often consist of several databases tied together by a common search
engine. An ontology repository is a facility where ontologies and related
information artifacts can be stored, retrieved and managed.
Similarity Selector
Entity{i1<=>j1}, Entity{i1<=>j2},…. Entity{i1<=>jm}
Entity{i2<=>j1}, Entity{i2<=>j2},…. Entity{i2<=>jm}
Entity{i3 <=>j1}, Entity{i3<=>j2},…. Entity{i3<=>jm}
.
.
.
Entity{in<=>j1},
Entity{in<=>j2},….
Entity{in<=>jm}
Similarity Checker
Sim1=> Entity{i1<=>j1},
Sim2=> Entity{i2<=>j1},
Sim3=> Entity{i3<=>j2},
Sim4=> Entity{in<=>jm},
.
.
Simk=> … … … … …
Ontology
R
E
P
O
S
I
T
O
R
Y
Input
i
Input
j
Entity i1
Entity i2
Entity i3
.
.
.
Entity in
Entity j1
Entity j2
Entity j3
.
.
.
Entity jm
Dual Arrangement Assembler
Output
k
Entity k1<=>Entity {i1j1)}
Entity {i2 j1}
.
. .
Figure 8. Design
Component of dual arrangement for mapping two ontologies
Input Entity:
Input entities
are retrieved from the ontology repository and they consist of entities
like concept, relation, attribute, values, etc. These entities are given
as input to the similarity selector.
Similarity Selector:
Similarity Selector component finds out the similarity that exists between the given input entities. Given two ontologies O1 and O2, a similarity measure is defined as a real-valued function:
Similarity: (Ei) × (Ej) → [0, 1]
Where Ei∈ {Ci, Ri, Ai, Vi} ∈ O1, Ej∈ {Cj, Rj, Aj, Vj} ∈ O2 and E1 and E2 are of the same kind. Entities C, R, A, V denote concept, relation, attribute and value respectively. The value of similarity indicates the probability of establishing mapping between E1 and E2
Similarity Checker:
Similarity Checker component checks the pattern mapping similarity which is present in the input entities of similarity selector. It filters out the irrelevant combination of entities available in the similarity selector.
Dual Arrangement Assembler:
After the similarity checker process is over, dual arrangement assembler component assembles the relevant combination of patterns in such a way that two mapping entities of patterns are chosen from both ontologies.
The source and target ontologies are given in XML format. Therefore, output (Mapping file) is also in XML format. Mapping arrangements are expressed using JAVA. For each mapping pattern a class is created with naming convention property and with appropriate methods. These classes are kept in a package called Mapped Patterns. Interested users can just import the package and make use of it, since all the requirements are already available in method format.
It is important
to have means to evaluate the quality of mapping, and, consequently
the fitness of different methods and tools with respect to different
domains and settings. Nowadays, the central approach to ontology mapping
evaluation is based on the notion of reference alignment (‘gold standard’),
defined a priori, to which the results obtained by the matching systems
are compared. This typically yields measures borrowed from the discipline
of Information Retrieval, such as precision (the proportion of mappings
returned by the matching system that are also present in the reference
mapping) and recall (the proportion of mappings present in the reference
mapping that are also returned by the matching system).The correspondences
in both the reference and experimental alignments are most often expressed
as simple concept-concept (or relation-relation) pairs, interpreted
as logical equivalence. Sometimes, alignments interpreted as logical
subsumption (analogously to the same notion as omnipresent in ontology
design), and/or with a non-Boolean value of validity are also considered.
However, we might even be interested in more complex alignment structures
(patterns), which could reveal interesting details about the relationship
of the two ontologies.
5. Conclusion and Future
Enhancement
This paper
described the possible dual mapping arrangements to be considered at
various levels while doing the process of mapping two ontologies. The
mapping arrangements identified helps to make the classification of
ontology mapping simpler and meaningful. They may be applied on quite
different ontologies depending on the requirement or need of the application
on hand. In this direction, mapped patterns may be exploited for ontology
integration / merging. In future, we have planned to extend the pattern
mapping from dual arrangement to n-arrangement. We have also planned
to implement the system and conduct experiment on it to evaluate whether
it operates according to our expectation. More functionality is expected
to be incorporated in the system as follows. User’s queries have to
be answered by referring the resultant mapping file. The system may
also be capable of managing changes, sharing ontologies, editing and
browsing the ontologies.
6. References
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