=Paper=
{{Paper
|id=Vol-1248/paper7
|storemode=property
|title=On the Identification and Representation of Ontology Correspondence Antipatterns
|pdfUrl=https://ceur-ws.org/Vol-1248/WoMO14-Paper9.pdf
|volume=Vol-1248
|dblpUrl=https://dblp.org/rec/conf/fois/GuedesBR14
}}
==On the Identification and Representation of Ontology Correspondence Antipatterns==
https://ceur-ws.org/Vol-1248/WoMO14-Paper9.pdf
On the Identification and Representation of
Ontology Correspondence Antipatterns
Anselmo GUEDES 1, Fernanda BAIÃO and Kate REVOREDO
Department of Applied Informatics, Federal University of the State of Rio de Janeiro –
UNIRIO, Brazil
Abstract. Semantic data integration and application interoperability are
recognized challenges in distributed scenarios, and they have long being addressed
by ontology alignment techniques, which try to find correspondences between
entities that are semantically equivalent in distinct ontologies. However, ontology
alignment is still a costly and difficult task, considering the existence of large-scale
scenarios and complex domains. This work introduces the concept of a
correspondence antipattern, which is essentially a set of generic correspondences
that represent an incorrect alignment and can be used as a predefined template to
help identify incorrect correspondences between two existing ontologies and thus
refine ontology alignments. We also introduce a methodology that assists the
identification and construction of ontology correspondence antipatterns, and
evaluate it through a case study using datasets from the last three campaigns of
OAEI, the Ontology Alignment Evaluation Initiative.
Keywords. ontology matching, correspondence antipatterns, alignment problem.
Introduction
As the research and practice on Ontology become more popular and evolve, several
ontology artifacts arise for the same universe of discourse. However, they differ among
each other in several perspectives, such as distinct representation languages (syntactic
heterogeneity), variations in names referring to the same entity (terminological
heterogeneity), different conceptualizations for the same domain (conceptual
heterogeneity) and entities being perceived differently (semiotic heterogeneity)[19].
The Ontology Matching area [19] [23] deals with all these problems, being considered
by many authors the key element for heterogeneity reduction between ontologies.
The Ontology Matching task consists in identifying the correct correspondences
among entities of multiple ontologies, which it is a necessary condition for establishing
the interoperability among them [23]. Techniques used to identify the correspondence
between the entities of two ontologies include the analysis of subsumption between
classes and the similarity between the entity names [19]. However, current results from
state-of-the-art techniques are neither complete nor precise, i.e., they are not able to
identify all existing correspondences between two ontologies and sometimes suggest
1
Corresponding Author: anselmo.guedes@uniriotec.br
correspondences that may be incorrect [26]. With regard to precision errors, suggesting
an incorrect correspondence may lead to either logical or ontological incompatibilities.
On the other hand, in the context of software development, antipatterns are
considered a valuable tool for the identification of bad or incorrect practices.
Antipatterns prevent or hamper the good conduct of the software development and
maintenance processes. According to Sales et al. [12], an anti-pattern is “just like
pattern, except that instead of a solution it gives something that looks superficially like
a solution, but is not one”. It is, thus, a recurrent decision for a specific scenario that
usually results in more negative consequences than positive ones [11]. Once the
instantiation of an anti-pattern is identified in a solution, this solution should be
refactored so as to become an appropriate solution.
In the context of ontology matching, we claim that those bad solutions consist of
incorrect (including missing) correspondences. A correspondence antipattern is then a
matching model for identifying incorrect correspondences that may occur repeatedly in
ontology matching processes. The results from an ontology matching approach could
be improved by looking for instances of the correspondence antipatterns within the
results and proposing a refactoring of the identified problem, so that the problematic
situation is removed.
In this work, we introduce the concept of correspondence antipatterns and show a
methodology for identifying and computationally representing them. Furthermore, we
apply this methodology in some scenarios of the OAEI (Ontology Alignment
Evaluation Initiative) and identified one correspondence antipattern.
This work is divided as follows: Section 1 presents related work in the literature;
Section 2 defines the ontology matching problem. Section 3 presents the basic concepts
of design patterns and antipatterns. Section 4 introduces the concept of a
correspondence antipattern and a methodology for its identification and representation.
Section 5 presents results of a case study on the OAEI, while and, finally, Section 6
presents the final considerations of this work.
1. Related Work
Many of the existing research have given focus on identification and specification of
antipatterns in general. In the literature about antipatterns, Brown et al. [3] is
particularly relevant. This work identified antipatterns that can be detected not only in
the architecture and design of systems, but also in project management software. Other
research [4] [5] identified performance antipatterns as their main concern, because
impact directly on the quality of the software.
Some research has been carried out to provide formalism to antipattern
specifications. For example, Ballis et al. [7] propose a new visual language for
describing patterns and antipatterns. This language is an extension of UML with some
new graphics, so that patterns and antipatterns can be specified in a more rigorous.
More recently, Stamelos [8] proposed the use of Bayesian Belief Networks, Ontologies,
arrays of structures and Design social networks as tools to formally represent
antipatterns Software project management.
In ontology research, Ontology Design Patterns (ODPs) are an emerging approach
that favors the reuse of encoded experiences and good practices. ODPs are modeling
solutions to solve recurrent ontology development problems [9]. According to Falbo et
al. [10], patterns in Ontology Engineering are still in infancy when compared with
Software Engineering, where patterns have been used for a long period. The earliest
works addressing patterns in Ontology Engineering are from the beginning of the
2000s. Sales and colleagues [11] present semantic antipatterns for ontology engineering.
These antipatterns capture error-prone modeling decisions, which can result in the
creation of models that allow for unintended model instances (representing undesired
state of affairs). The authors present antipatterns that have been empirically elicited by
validating the ontology conceptual models via visual simulation. The proposed
antipatterns do not comprise corresponding relations between entities, which are crucial
for the Ontology Matching task.
In the context of Ontology Matching, correspondence patterns were proposed by
[12] and are essentially correspondences and sets of correspondences with generic
entities. They help find more precise correspondences than simply relating one entity to
another one. Each correspondence pattern is a generic solution to a problem of
alignment. Scharffe [12] proposed a library of correspondence patterns that represent
solutions to different recurring mismatches which are quite hard for matchers using
usual matching techniques. However, correspondence patterns do not help in scenarios
where the ontology matching technique returns an ontology alignment including an
incorrect correspondence. ASMOV (Automated Semantic Matching of Ontologies with
Verification) is an algorithm that uses lexical and structural characteristics of two
ontologies to iteratively calculate a similarity measure between them, derives an
alignment, and then verifies it to ensure that it does not contain semantic
inconsistencies [27]. ASMOV is designed to combine a comprehensive set of element-
level and structure-level measures of similarity with a technique that uses formal
semantics to verify whether computed correspondences comply with desired
characteristics.
Padilha [13] proposes design patterns and antipatterns for ontology alignment
using top-level ontologies. The proposed design patterns were built based on
OntoUML [14], an ontology modeling language which considers the ontological
distinctions and axiomatic theories proposed in Unified Foundational Ontology (UFO).
UFO was proposed by Guizzardi [28] and meets axiomatic theories that deal with the
main categories of concepts used in conceptual modeling [29]. In [13], some patterns
are formally defined focusing on the analysis of the OntoUML modeling constructs;
however, there was no intention to specify a generic definition for antipatterns in
ontology matching, or to address the problem of their identification and
operationalization.
2. The Ontology Matching Problem
Ontology is an explicit specification of a conceptualization, in which classes, attributes,
relationships and instances are considered as first class elements [12]. Formally, an
ontology may be represented as a tuple O = , where C is a set of classes
(concepts of the domain), R is a set of relationships between classes in C (also called
object properties in some languages), P is a set of data properties (a specific type of
relation whose domain is a class and the range is a data type), I is a set of class
instances (concrete objects) and A is a set of axioms. Meilicke [1] also defines a
signature S of O as S = .
Ontology matching identifies correspondences between the entities of multiple
ontologies, and it is a necessary condition to establish interoperability between them
[23]. According to Euzenat [19], technically the ontology matching process occurs by
taking two ontologies O and O' as input, optionally adding a set of resources r, a set of
parameters p and a preliminary alignment A. The result of this process is an alignment
A’ between the ontologies O and O', represented as A’ = f (O, O’, A, p, r).
Basically, ontology matching is a process in which semantic links between entities
of ontologies are established. Each semantic link is called a correspondence. A set of
correspondences is called an alignment. According to Sváb-Zamazal [18], consider two
ontologies O and O’, and a function Q that defines corresponding entity sets Q(O) and
Q(O’) in which Q(O) O and Q(O’) O’. A correspondence D between O and O’ is a
quadruple e, e’, r, n so that e Q(O), e’ Q(O’), r is the semantics of the
correspondence (for example, equivalence), and n [0,1] is a confidence value. An
alignment A between O and O’ is a set of correspondences between O and O’.
When two classes (or sets of classes) do not match, we can affirm, in turn, that
their sets of possible instances is not equivalent, so we have the following definition for
a non-correspondence [13]: Consider the classes e and e’ and their sets of possible
instances in all possible worlds Ie and Ie’ respectively (and analogously the sets of
classes C and C’ and their sets IC and IC’). If e’ and e do not match by the relation r
(either equivalence or specialization), then Ie’ Ie. In other words: (e, e’, r, n)
Ie’ Ie and (C, C’, r, n) IC’ IC. A non-equivalence correspondence between
classes may be defined as: e, e’, equality, n Ie’ ≠ Ie.
3. Design Patterns and Antipatterns
When specialists work on a particular problem, it is not very common that new
proposals are completely different from existing solutions. Often, solutions to similar
problems are retrieved and the essence of the solution is reused for the resolution of the
new problem [15]. Patterns assist in building a collective experience based on the skills
of software engineers. They capture existing and proven experience in software
development and help to promote good design practices. Each pattern deals with a
specific problem and recurring design or implementation of software. A software
design pattern describes a particular design problem (project) that arises in specific
contexts and presents a proven generic schema for the solution [15].
On the other hand, an antipattern is a description of a given solution to a common
problem that generates, definitely, negative consequences. When properly documented,
an antipattern describes the overall solution, the primary causes that lead to problematic
solution, symptoms that describe how to recognize the antipattern, consequences of
using this solution and a refactored solution that can change the antipattern in a most
appropriate solution [3]. Antipatterns are new forms of patterns. A fundamental
difference between a pattern and an antipattern is that the solution adopted which the
antipattern identifies, presents negative consequences on their use. Some consequences
may appear immediately (symptoms) and some might appear only in the future
(consequences). To be truly useful, antipatterns should present an appropriate solution
to the problem identified [3], in the other words, the refactored solution. According to
[16], refactoring is the process of changing a software system in such a way that they
do not alter the external behavior of the code yet improves its internal structure. Is a
disciplined way to clean code that minimizes the chances of introducing errors.
The use of templates is what makes the design patterns and antipatterns different
from other forms of technical discussions. The models ensure that important issues are
answered for each pattern and antipattern. According to Brown [3], antipatterns can be
specified in three different ways: (i) Pseudo Antipattern Template, in which the author
only textually describes a bad solution; (ii) Mini Antipattern Template is the most basic
form of the antipattern, consisting only of its name, the problem identified and
refactored solution; and (iii) Complete Antipattern Template, which consists of a
detailed description and specific features of the antipattern. We adopt the complete
antipattern model as detailed in Table 1.
Table 1.Complete Antipattern Model
Antipattern Item Short Description
Name The name of the antipattern.
A solution may be given at the level of software, technology, process and
roles. Software-level solution indicates that new software is created for the
solution. Technology indicates that the solution implies the acquisition of
Refactored solution type
new technology or a product. Process indicates that must follow a solution
from the process involved. Role indicates that the solution implies the
attribution of responsibilities to an individual or group.
Root cause(s) The general cause(s) of the antipattern.
The primary forces that are ignored, misused or used too much in this
Unbalanced forces
antipattern.
Examples of where the problem may occur or general information that may
Background
be useful for better understanding the antipattern. Is optional.
A diagram or schema that identifies general characteristics of the
Antipattern general form
antipattern.
Symptoms and/or
A list of symptoms and consequences caused by the antipattern.
consequences
Specific situations in which the antipattern may be acceptable. It is
Known exceptions
optional.
Optional item that lists the major variations of the antipattern. Additionally,
Variations
alternative solutions should be described here.
Related solutions List and citations cross-references suitable to the context of the antipattern.
4. Correspondence Antipatterns
Due to possible precision errors that every ontology alignment tool is subject to, it may
be the case that a correspondence included in an ontology alignment is not correct.
Figure 1 exemplifies a fragment of the resulting alignment between two ontologies o1
and o2[1]. Within each ontology, a rectangle around two classes indicates that these
classes are disjoint, and a dotted line connecting two classes from different ontologies
represents a correspondence.
Suppose that a terminology-based alignment tool identifies the following
correspondences, represented in Figure 1:
o1:Document, o2:Document, ≡, 1.0 and
o1:Reviewer,o2:Review, ≡, 0.9,
where “≡” is the symbol for the equivalence relationship. Considering the
commonly-known semantics of “Reviewer” and “Review” concepts, we intuitively
know that the second correspondence is incorrect since it results in a logical
contradiction, which is clear through the following argumentation: Suppose x is an
instance of o1:Reviewer. Since o1:Person generalizes o1:Reviewer, x is also an
instance of o1:Person. Considering the second correspondence, there is a possible
world w in which x is also an instance of o2:Review. Thus, x is also an instance of
o2:Document in w, since it generalizes the o2:Review concept. Considering the first
correspondence, x is also an instance of o1:Document in w. This results that x is an
instance of o1:Person and o1:Document in w which, according to the disjoint
relationship between o1:Person and o1:Document, is a logical contradiction [1]. In this
work, we provide a way in which this intuition may be automatically inferred.
Figure 1. Fragment of two ontologies and an alignment problem. (Adapted from: [1]).
Definition (Correspondence Antipattern): Given two aligned ontologies O and
O’, and their corresponding signatures S and S´, a correspondence antipattern T is a set
of generic 2 , domain-independent correspondences D and non-correspondences N
between entities of O and O´ that characterizes the existence of some problematic
correspondence in D. Formally, it is defined as a tuple T = .
An ontology correspondence antipattern oca is the result from a process that
indicates an affirmation or a negation from a correspondence D, and may be
represented as oca = f (O, O’, A, T, D). As a design (anti)pattern, T is specified as a
template, that is, a theory referring to generic entities and their relations.
The purpose of a correspondence antipattern is to identify an incorrect
correspondence c within an ontology alignment. This way, an instance t of a
correspondence antipattern T occurs in an ontology alignment A between ontologies O
and O´, in which cA. The search for an instance of T in a given ontology alignment A
may be performed by exhaustively looking for all possible instantiations of T given A.
The found instances represent evidences of this problematic solution.
For the development of correspondence antipatterns, the first step is to have the
correct understanding of the problem being treated. When properly understood, the
identified problem can result in correspondence antipatterns templates. Figure 2
presents our proposed methodology, which can assist in the construction of a
correspondence antipattern. This methodology focuses on responding to key issues
which are essential for an antipattern identification.
2
Generic in the sense that it does not represent a specific concept only.
Figure 2.Methodology to construct a correspondence antipattern.
Show Problematic Solution: Correspondences, when incorrectly identified, may
result in ontology artifacts that may be syntactically valid, but are likely to result in
unintended domain representations. Thus, the set of valid cases (possible states of the
domain of discourse) represented in the ontology artifact may not encompass the set of
instances that do not represent desired states in this domain [11] or result in logical
incompatibilities [1]. The first step to build an antipattern understands the problem that
is being treated. What are the negative consequences that may result from using the bad
solution (i.e., the set of matches)? In the scenario presented in Figure 1, we showed that
the correspondence of the entities o1:Reviewer and o2:Review results in a logical
contradiction according to the disjoint relationship between o1:Person and
o1:Document. Then we have the following problem: the correspondence of two
subclasses whose superclasses are disjoint.
Evidenciate Problematic Solution: for a solution to be considered problematic,
this should in fact occur in practice, that is, there should be evidences that the bad
solution is frequently applied. It is not reasonable to specify problematic solutions that
do not even occur in practice. Following the example in Figure 1, an ontology
alignment tool that only takes terminological similarity metrics into account will
probably identify o1:Reviewer and o2:Review as correspondent classes, given the
similarity of both strings.
Demonstrate Implications: For a correspondence antipattern specification to be
complete and useful, it is important to discuss the reasons why an alignment solution is
indeed problematic. In our previous example, given an instance x of o1:Reviewer, there
is a possible world w in which x is also an instance of o2:Review, which in turn leads to
the conclusion that x instantiates both o1:Person and o2:Document in w; however,
o1:Person and o2:Document are disjoint, resulting in a logical contradiction. When the
ontology alignment is being applied for the purpose of transforming instances or query
reformulation [19], this will surely result in errors.
Identify Cause of Problematic Solution: The alignment fragment (that is, the
subset of correspondences) that suffices to characterize it as a problematic solution
should be identified. In the example being discussed, the two correspondences
(between o1:Reviewer and o2:Review, and between o1:Person and o2:Document)
cannot co-occur, due to the pre-defined disjoint relations. A domain-independent
intuition for specifying this problematic solution is that there may never be two
corresponding classes that are specializations of two disjoint classes. This
correspondence antipattern (named as OCA01) can be formalized as:
OCA01: (?o1:?e1 ≡ ?o2:?e1) ⊓ (?o1:?e1 ⊑ ?o1:?e2) ⊓ (?o1:?e2 ⊓ ?o1:?e3 ⊑ ⊥)
⊓ (?o1:?e3 ≡ ?o2:e2) ⊓ (?o2:?e1 ⊑ ?o2:?e2).
For the construction and computational representation of a correspondence
antipattern, we adopt EDOAL (Expressive Declarative Ontology Alignment
Language), an open and agnostic language [12]. EDOAL is an extension of the
alignment format proposed by Euzenat [24], and offers classes and relationships
constructs, class restrictions, transformation of properties values, comparison operators
for restriction on entities. The main advantages of EDOAL are that (i) it is independent
from formalisms of ontological entities being aligned; (ii) it is an expressive model for
documenting correspondences and (iii) it complies with semantic web technology
given its RDF and OWL syntax. EDOAL is used in [12] to represent correspondence
patterns.
A fragment of the OCA01 correspondence antipattern is illustrated as follows,
implemented using the EDOAL language:
Refactored Solution: Refactoring in this case means repairing the alignment. In
other words, when an instance of a correspondence antipattern is found in an
alignment, the incorrect correspondence should be removed from the alignment.
An antipattern documentation template ensures that important issues are explicated
for each antipattern. Table 2 lists the information that should be gathered during the
development of a correspondence antipattern following the previous proposed
methodology.
Table 2.Correspondence Antipattern OCA01
Antipattern Item Short Description
OCA01 - Correspondence of two subclasses whose superclasses are
Name
disjoint.
Refactored solution type Re-establishment of correspondence identified.
Assume that two terms correspond to each other ignoring semantic
Root cause
relationship with other entities and their respective matches.
A term mismatch may occur due to terminological homonyms, where
similar terms with distinct meanings are claimed as corresponding entities.
Unbalanced forces
The semantics of the statement of terms must be observed for alignment
composition.
(?o1:?e1 ≡ ?o2:?e1) ⊓ (?o1:?e1 ⊑ ?o1:?e2) ⊓ (?o1:?e2 ⊓ ?o1:?e3 ⊑ ⊥)
Antipattern general form
⊓ (?o1:?e3 ≡ ?o2:e2) ⊓ (?o2:?e1 ⊑ ?o2:?e2).
Symptoms and/or Symptom: two subclasses that have equivalence correspondence, but their
consequences superclasses do not have equivalence correspondence.
Known exceptions Not applicable.
Variations Not applicable.
Related solutions Not applicable.
5. A Case Study on OAEI
The Ontology Alignment Evaluation Initiative (OAEI) is a coordinated international
initiative whose goal is to evaluate the strengths and weaknesses of the ontology
alignment tools, compare the performance and improve the assessment of ontology
alignment techniques. Its main goal is to promote the continuous improvement of the
ontology alignment tools. OAEI organizes annual campaigns addressing several
domains, and publishes the results of the evaluated tools.
In this work, we manually scanned the published results from the 2013, 2012 and
2011.5 OAEI campaigns in the Anatomy, Benchmark, Conference, Library and Multi-
Languages (called Multifarm) domains to extract all the reported incorrect
correspondences resulted from the evaluated tools. We then ranked each
correspondence from the reference alignments with regard to how many of the
evaluated tools were not able to find this correspondence (that is, the most common
errors). Due to space, this Section presents only two more correspondence antipatterns.
The correspondences antipattern OCA02 and OCA03, documented below, occurred
respectively 47 and 46 times in the OAEI published results we scanned. The
correspondence antipattern OCA02 corresponds to the correspondence
conference.paper, ekaw.paper, ≡ , _ and the correspondence antipattern OCA03
corresponds to the correspondence confof.paper, ekaw.paper, ≡, _.
OCA02 - Disjointness-subsumption contradiction with disjoint classes with
subclasses.
Postulate: Let e1, e2 and e3 be classes in an ontology o1, in which e2 is a subclass of
e3, which in turn is disjoint with e1. If class e1 in ontology o1 equivalently corresponds
to class e1 in ontology o2, class e2 in ontology o1 corresponds to class e2 in ontology
o1 and class e2 in o2 is a subclass of e1 in ontology o2, then there is an alignment
problem.
Table 3 shows a correspondence antipattern built from this problem, called OCA02.
Table 3.Correspondence Antipattern OCA02
Antipattern Item Short Description
OCA02 - Disjointness-subsumption contradiction with disjoint classes
Name
with subclasses.
Refactored solution type Re-establishment of correspondence identified.
Assume that two terms correspond to each other ignoring semantic
Root cause
relationship with other entities and their respective matches.
A term mismatch may occur due to terminological homonyms, where
similar terms with distinct meanings are claimed as corresponding entities.
Unbalanced forces
The semantics of the statement of terms must be observed for alignment
composition.
(?o1:?e1 ≡ ?o2:?e1) ⊓ (?o2:?e2 ⊑ ?o2:?e1) ⊓ (?o1:?e1 ⊓ ?o1:?e3 ⊑ ) ⊓
Antipattern general form
(?o1:?e2 ≡ ?o2:?e2) ⊓ (?o1:?e2 ⊑ ?o1:?e3)
Symptoms and/or Symptom: two subclasses that have equivalence correspondence, but their
consequences superclasses do not have equivalence correspondence.
Known exceptions Not applicable.
Variations OCA01, OCA03
Related solutions Not applicable.
OCA03 - Disjointness-subsumption contradiction with disjoint classes without
subclasses.
Postulate: Let e1 be a class in ontology o1 that is disjoint with class e2 in the same
ontology o1, and a class e1 in ontology o2 that specializes class e2 in o2. If class e1 in
o1 equivalently corresponds to class e1 in o2 and class e2 in o1 equivalently
corresponds to class e2 in o2, then there is a disjointness-subsumption contradiction
alignment problem.
Table 4 shows a correspondence antipattern built from this problem, called
OCA03.
Table 4.Correspondence Antipattern OCA03
Antipattern Item Short Description
OCA03 - Disjointness-subsumption contradiction with disjoint classes
Name
without subclasses.
Refactored solution type Re-establishment of correspondence identified.
Assume that two terms correspond to each other ignoring semantic
Root cause
relationship with other entities and their respective matches.
A term mismatch may occur due to terminological homonyms, where
similar terms with distinct meanings are claimed as corresponding entities.
Unbalanced forces
The semantics of the statement of terms must be observed for alignment
composition.
(?o1:?e1 ≡ ?o2:?e1) ⊓ (?o2:?e2 ⊑ ?o2:?e1) ⊓ (?o1:?e1 ⊓ ?o1:?e2 ⊑ ) ⊓
Antipattern general form
(?o1:?e2 ≡ ?o2:?e2)
Symptoms and/or Symptom: two subclasses that have equivalence correspondence, but their
consequences superclasses do not have equivalence correspondence.
Known exceptions Not applicable.
Variations OCA01, OCA02
Related solutions Not applicable.
6. Final Considerations
Ontology matching is a very active research field in the scientific community, where
various techniques, approaches and tools have been proposed. However, such methods
are still likely to identify incorrect correspondences between the entities of the
ontologies that are being aligned. By identifying which errors frequently occur in the
ontology matching process, it is possible to generalize such errors to specify
correspondence antipatterns. Correspondence antipatterns assist in identifying incorrect
correspondences or set of correspondences between ontologies that are being matched.
This work defined the concept of a correspondence antipattern and introduced a
methodology to identify and computationally represent them. When analyzing a real
alignment database, two incorrect solutions frequently found resulted in new
correspondence antipatterns. The ontology alignment data used for this analysis was
systematically extracted from OAEI, a representative initiative in the ontology
matching field. We formally defined and proposed a methodology to assist the
identification of correspondence antipatterns, as well as its documentation and
computational representation. The language used to represent the correspondence
antipatterns, EDOAL, is rich of constructs that allow the specification of complex
relations among the entities of the ontologies, and is also flexible to be extended given
its XML, OWL and RDF syntax.
Further work will focus on the development of a reasoned to automatically
recognize and refactor instances of a correspondence antipatterns on a given ontology
alignment or set of alignments. Moreover, we are working on building a catalogue of
correspondence antipatterns identified from all OAEI published results.
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