=Paper=
{{Paper
|id=Vol-2535/paper_14
|storemode=property
|title=An Evidence-Based, Contextual Approach to the Validation of Concept Alignments to
Support Ontology Reuse
|pdfUrl=https://ceur-ws.org/Vol-2535/paper_14.pdf
|volume=Vol-2535
|authors=Marwa Abdelreheim,Friederike Klan,Taysir Soliman
|dblpUrl=https://dblp.org/rec/conf/qurator/AbdelreheimKS20
}}
==An Evidence-Based, Contextual Approach to the Validation of Concept Alignments to
Support Ontology Reuse==
https://ceur-ws.org/Vol-2535/paper_14.pdf
An Evidence-Based, Contextual Approach to the
Validation of Concept Alignments to Support Ontology
Reuse
Marwa Abdelreheim1, Friederike Klan2, Taysir Hassan A. Soliman1
1 Assuit University, Assuit, Egypt
2 DLR Institute of Data Science, Jena, Germany
marwa.abdelrehem@fci.au.edu.eg, friederike.klan@dlr.de,
taysser.soliman@fci.au.edu.eg
Abstract. Reusing ontology components can save development costs, preserves
knowledge and fosters interoperability. Existing software tools for ontology re-
use are based on lexical matching techniques comparing input keywords and la-
bels of the concept and often don’t consider the contextual semantics encoded in
the ontology. In this paper, we propose a novel context-based semantic matching
approach that identifies pairs of equivalent concepts in terms of the context of
their ontologies. The algorithm is part of a framework that supports ontology en-
gineers and domain experts in their task of developing new ontologies by reusing
parts of existing ontologies. The proposed algorithm takes the correspondences
obtained by existing matchmakers and checks the correctness of those mappings
based on the semantics encoded in the given ontologies. Our algorithm correctly
detects true positive mappings and discards false positive mappings, while in
some cases it incorrectly discards true positives as these mappings are not simi-
larly encoded in their ontologies.
Keywords: Ontology Development, Ontology Reuse, Contextual Semantic On-
tology Matching.
1 Introduction
Ontologies formally represent domain knowledge in terms of concepts and relations
between those concepts. They are used to easily share and reuse domain knowledge. In
conceptually diverse domains, developing ontologies is a complex process that requires
an extensive professional exchange between domain experts and ontology engineers. It
is not a one-step process but rather an iterative one that needs to be repeated until a
mutually accepted version of the ontology is reached. Rather than designing ontologies
from scratch, it is good practice to reuse existing ontologies, or parts of them. This can
reduce development time and effort. It can also produce high quality ontologies as it
reuses parts/fragments of existing ontologies that (ideally) were validated by domain
experts and tested against inconsistencies. The process typically taken in order to build
a new ontology based on existing ones [4, 12, 13, 15] involves the following five steps
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
2
(1) defining the scope of the target ontology, (2) extracting domain-related keywords,
which are later used to (3) select candidate ontologies for reuse, (4) evaluating and
filtering those candidates based on some criteria (e.g. encoding quality requirements
and possibly rank them). Finally, (5) integrating/merging the selected ontologies to pro-
duce a new ontology.
The main challenge in the outlined ontology reuse process is how to select the ap-
propriate ontologies that accurately represent the target domain. Existing ontology se-
lection and reuse tools/approaches [1, 4, 6, 10, 11] typically consider input coverage as
the most important criterion to assess the ontology relatedness to a particular target
domain. They use lexical matching (i.e., exact or partial matching) between user input
keywords, and possibly their synonyms, and the labels of ontology concepts. The larger
the number of lexical matches in a source ontology is, the higher is the input coverage
score, and the more relevant is the source ontology considered to be to the domain of
the target ontology. Another challenge when reusing ontologies arises when an ontol-
ogy engineer wants to build upon an already existing target ontology or a part of it. In
this case, he needs to identify concepts from existing source ontologies that semanti-
cally match the concepts of the target ontology and reuse them to extend it. To this end,
the ontology engineer can use existing ontology matching tools/systems to find corre-
spondences between concepts in a source and a target ontology.
Ontology matching typically considers two input ontologies and attempts to find se-
mantically equivalent entities (i.e. concepts, properties, and individuals). Several ontol-
ogy matching systems exist [3, 7, 14] and use complex strategies to find accurate align-
ments between the input ontologies’ entities. They use a combination of lexical and
structural approaches to find alignments. Some approaches consider external
knowledge sources such as WordNet1 to identify correspondences. In fact, existing on-
tology matching tools can determine alignments between concepts that have simi-
lar/synonymous labels, but their limitation is the fact that if two concepts have identical
labels this does not necessarily mean that those concepts are semantically equivalent.
This includes homonyms, which are concepts that share the same or similar name but
have different definition in different domains. In addition, concepts in some ontologies
may not have a definition indicating their meaning, and an expert should be entrusted
to determine whether, or not, the two concepts are equivalent. For example, consider
the concept “Property” in two different ontologies. A “Property” in one ontology could
represent the weight, mass, or length of an object, while in another ontology could rep-
resent taste, color, or texture of an object. In this case, although the two concepts "Prop-
erty" have the same label, they do not have the same contextual semantics in their on-
tologies and should not be considered equivalent.
In this paper, we aim to find contextual semantic equivalent concepts (i.e., two con-
cepts have the same contextual semantics if they have the same intensional meaning in
their ontologies). In logic, intensional meaning of a concept is equivalent to specifying
a broad definition of it, then define specific properties it must have to be counted as a
term reference. We propose a new context-based semantic matching algorithm that ex-
tends an ontology matching system that takes a set of ontology alignments (e.g.
1 WordNet. https://wordnet.princeton.edu/
3
generated by an ontology matching system) and examines the contextual semantic sim-
ilarity (intensional meaning) for each candidate corresponding concepts. This method
can exclude alignments that do not have evidence for contextual semantic similarity.
This would improve the ontology reuse process by producing more accurate mappings
and assist the ontology engineer in the creation of a new ontology, by discovering con-
cepts/parts of existing source ontologies and proposing such sections to extend the tar-
get ontology.
The paper is organized as follows. A brief literature review of the existing ontology
matching systems is presented in section 2. In section 3, we describe our proposed al-
gorithm for identifying concepts likely to be semantically equivalent in the context of
their ontologies. In section 4, we present and discuss the results of an empirical evalu-
ation of our methodology. Finally, we conclude by identifying directions for future
work.
2 Related Work
Ontology matching considers two input ontologies and aims to find correspondences
between related entities (i.e., concepts, properties, and individuals) in both ontologies.
Due to the complexity of ontologies, in terms of their size and structure, existing ontol-
ogy matching algorithms adapt complex strategies to find accurate alignments between
ontology entities. COMA++ [3] is a tool that applies diverse lexical and taxonomic
matching algorithms to support matching different types of schemas and ontologies.
AgreementMakerLight (AML) [7] is an ontology matching system that comprises sev-
eral matching techniques and applies it on different levels for the components being
matched (conceptual vs. structural). YAM++ [14] encodes the structural information of
an ontology in a bitmap to accelerate calculating the similarity by comparing whole
ontologies and avoiding pairwise comparisons of concepts. In addition, it discovers new
mappings by applying similarity propagation methods on the initial list of mappings.
Extensible Mapping (XMAP) [5] is a scalable ontology matching system aims to map
very large ontologies efficiently (in terms of computation time) and effectively (in
terms of precision and recall scores). It applies different matchers such as string, lin-
guistic, and structure-bases and use mathematical methods to aggregate similarity
scores resulted by each matcher.
There are several approaches addressing this. After getting an initial set of mappings
by performing lexical and structural matching, an ontology reasoner can be used to
recognize sets of correspondences that prove to be inconsistent, as in ASMOV[8], or to
discover new mappings and repair basic mappings that are not satisfiable as done by
LogMap [9]. Finally, STROMA [2] is a new ontology matching approach aiming to
more accurately specifying the kind of semantic relationship between two concepts. In
contrast to other ontology mapping approaches, its goal is to identify more expressive
relationships between concepts, such as is-a, part-of, and equality relations between
concepts.
4
3 Evidence-Based, Contextual Alignment Validation
Our contextual semantic matching algorithm aims to find concepts in two ontologies
that are semantically equivalent and exclude concepts that are not proven to be seman-
tically equivalent in the context of their ontologies even if they have similar labels. In
order to achieve this, we take the alignments identified by a given ontology matching
system and check if there is evidence in the ontologies that supports the equivalence of
the concepts referred to in an alignment. Mappings without supporting evidence are
filtered out. To achieve this, we take the alignments identified by a given ontology
matching system and check if there is evidence in the ontologies that supports the equiv-
alence of the concepts referred to in an alignment in terms of encoded knowledge.
As shown in Algorithm 1 below, we use the AgreementMakerLight matcher (AML)
[7] as the base matcher of our algorithm to derive an initial set of candidate correspond-
ences. The reasons for this are: (1) AML has maintained its position among the top
systems in all areas of the Ontology Alignment Evaluation Initiative (OAEI) competi-
tion. (2) AML is available on GitHub as an open source code project2 and thus can be
reused and extended. (3) AML applies lexical and structural matching methods and
uses WordNet as an external source of knowledge to find correspondent concepts. (4)
AML can handle very large ontologies in in efficient computing time. But, as other
ontology matching systems, it relies on comparing concept labels, even at the structural
level, which alone is not sufficient for determining contextually semantic equivalent
entities. Our evidence-based, contextual semantic matching algorithm is summarized
in Algorithm 1.
Algorithm 1 Context-based Semantic Matching Algorithm
Input: AML candidate correspondences
Output: set of context-based semantically equivalent concepts (alignments)
1: For each candidate mapping (X, X*) do
2: if (there exists at least one candidate mapping (Y, Y*)) such that
3: case1: Y and Y* have superclass relation with X and X* resp., or
4: case2: Y and Y* have subclasses relation with X and X* resp., or
5: case3: Y and Y* have equivalence relation with X and X* resp., or
6: case4: Y and Y* have sibling relation with X and X* resp.
7: Then this is an indicator for X and X* being semantically equivalent.
8: end if
9: end for
To illustrate this, suppose that there is a list of mappings between two ontologies.
Let concept X from one ontology and concept X* from the other ontology map to each
other with acceptable AML similarity score (e.g. above a given threshold). Then, our
algorithm will examine, for any pair of concepts (X, X*), whether one (or more) of the
2 https://github.com/AgreementMakerLight/AML-Project
5
following additional evidences of contextual semantic equivalence is given. We arrange
evidences according to their strength of indicating contextual equivalence.
1. Corresponding super-classes
For each candidate mapping (X, X*), if there exists another candidate mapping (Y, Y*),
where Y and Y* have a superclass relation (direct or indirect) with X and X* respec-
tively, then this is an indicator for X and X* being semantically equivalent in their
context, see Fig. 1(a). On the contrary, if there is no such mapping (Y, Y*), then there
is no evidence that X and X* are semantically equivalent.
Example. Let’s assume we have a concept Car, which is subclass of concept Automo-
bile, which in turn is a subclass of concept Vehicle in one ontology. And, Concept Mo-
tor Vehicle is a subclass of concept Vehicle in the other ontology. If there is a mapping
between the concepts Car and Motor Vehicle and between the concepts Vehicle and
Vehicle (their super classes), then the mapping (Motor Vehicle, Car) is supported by
additional evidence, represented in Fig. 1(b) as double dashed mapping. Here, both
concepts represent something related to a Vehicle.
(a) (b)
Fig. 1 a) Corresponding super-classes contextual semantic similarity evidence. b) An illustrative
example for contextual semantic similarity mapping evidence based on super-class relationship.
2. Corresponding sub-classes
For each candidate mapping (X, X*), if there exists another candidate mapping (Y, Y*),
where Y and Y* have a subclass relation with X and X* respectively, then this is an
indicator for X and X* being semantically equivalent, see Fig. 2(a). The higher the
number of candidate mappings which are sub-classes of (X, X*), the stronger the evi-
dence that concepts (X, X*) have the same contextual semantics. On the other hand, if
the candidate mapping (X, X*) does not have any corresponding sub-classes candidate
mapping, then (X, X*) has no evidence for a contextual equivalent semantics.
6
(a)
(b)
Fig. 2 a) Corresponding sub-classes semantic similarity evidence. b) An illustrative example
where there is no evidence for contextual semantic similarity mapping based on sub-class rela-
tionship.
Example. In one ontology, concept Property has subclasses Physical Property, Biolog-
ical Property, and Chemical Property. While, in the other ontology concept Property
has subclasses Object Property, Data Property, and Functional Property. If the con-
cepts Property in both ontologies map but none of their subclasses map to each other,
this would indicate that the two concepts Property represent different things in the con-
text of their ontologies (see Fig. 2(b)).
3. Corresponding equivalent concepts
For each candidate mapping (X, X*), see Fig. 3(a), if there exists another candidate
mapping (Y, Y*), where Y and Y* have equivalence relation with X and X* respec-
tively, then this is an indicator for X and X* being semantically equivalent.
Example. Let the concept Physical Quality be equivalent to the concept Quality. And,
Concept Physical Characteristics be equivalent to the concept Characteristics, see Fig.
3(b). If the concepts Physical Quality and Physical Characteristics map to each other,
and also the concepts Quality and Characteristics map to each other, according to the
ontology matching algorithm, then this is an indicator that the concept Physical Quality
in one ontology has the same contextual semantics as the concept Physical Character-
istics in the other ontology. Here, the input mappings provide double support for the
equivalence by resulting in mappings for both concepts and its equivalent concepts.
7
(a) (b)
Fig. 3 a) Corresponding equivalent concepts semantic similarity evidence. b) An illustrative ex-
ample for contextual semantic similarity evidence based on equivalent relationships.
4. Corresponding sibling concepts
For each candidate mapping (X, X*), if there exists another candidate mapping (Y, Y*),
where Y and Y* have sibling relation with X and X* respectively, then X and X* are
(not necessarily) semantically equivalent, but they share a similar context. The higher
the number of candidate mappings which are siblings of (X, X*), the stronger the evi-
dence that concepts (X, X*) have the same contextual semantics, represented as double
dashed mapping in Fig. 4(a). On the contrary, if there are no candidate mappings among
(X, X*)’s siblings, then there is no evidence that X and X* have a contextual equivalent
semantics.
Example. In Fig. 4(b), the concept Family has sibling concepts Cluster and Swarm in
ontology A. Now let’s assume we have two other ontologies B and C. Ontology B,
defines a concept Family with sibling concepts Population of organisms, Collection of
humans, and Species, while in ontology C, the concept Family has sibling concepts
lifestyle, social status, and person. If there are the mappings (Collection of humans,
Cluster) and (Population of organisms, Swarm) in B and A, respectively. While there
are no mappings between the concept Family’s sibling concepts in ontology C and A.
Then, there is evidence that the concept Family in ontology B is contextually semanti-
cally equivalent to the concept Family in A, while there is no evidence that the concepts
Family in ontology C and A share the same contextual meaning.
(a)
8
(b)
Fig. 4 a) Corresponding sibling concepts semantic similarity evidence. b) An illustrative example
for contextual semantic similarity mapping evidence based on sibling relationships.
The algorithm aims to validate a set of input mappings (e.g. provided by AML) for two
ontologies. Any pair of corresponding concepts that does not satisfy any of the condi-
tions described above should be excluded from the final correspondences, since there
is no evidence indicating that the two concepts referred to in the mapping share the
same contextual meaning. This can improve the quality of any candidate set of corre-
sponding concepts by excluding corresponding pairs that do not share the same contex-
tual semantics. As mentioned above, an important application for this kind of algorithm
is when suggesting ontology fragments for reuse. The algorithm provides ontology en-
gineers with a set of validated mappings between a source and a target ontology (i.e.,
possible extension points). If a pair of corresponding concepts satisfies one or more of
the conditions above, then the source concept is listed in the suggestions list, to extend
the corresponding concept in the target ontology. In addition, the presented conditions
are arranged from the strongest evidence for contextual semantic equivalence to the
weakest, so the suggestions list of concepts from different source ontologies that could
be used to extend the corresponding concept in the target ontology, could also be
ranked. For, example, a concept is highly ranked in the suggestions list if it satisfies
one of the strong evidences (e.g., 1 or 2), or if it satisfies more than one condition at the
same time (e.g., 1 and 4).
4 Empirical Evaluation and Discussion
In this section we provide a quantitative and a qualitative analysis for our evidence-
based approach, comparing it with the mapping results produced by the AML match-
maker in the context of the annual competitions organized by the Ontology Alignment
Evaluation Intuitive (OAEI)3. Those competitions address different ontology matching
systems to compare their performance based on benchmark matching tasks. OAEI has
different tracks, each providing a specific set of ontologies, where ontology matching
systems are tested on.
3 http://oaei.ontologymatching.org/
9
4.1 Evaluation Task
For our evaluation, we use the alignment tasks and ontologies of OAEI’s Conference
track. This track provides 16 ontologies about conferences developed by the OntoFarm
project4, popularly used as a benchmark dataset. The track consists of 21 alignment
tasks. Each task comprises two ontologies for which a set of reference alignments is
given which corresponds to the complete alignment space between 7 ontologies. To
compare our algorithm’s matching results with AML’s results, we use the original ref-
erence alignments downloaded from the Conference track of (OAEI’s 2018) edition of
the competition5. We just consider the reference alignments for concepts (the competi-
tion also considers alignments between object properties, which our algorithm does not
support). We run AML (version- 3.1) to get the initial set of mappings as input for our
algorithm.
4.2 Results
After applying our evidence-based, contextual semantic matching algorithm to all 21
benchmark tasks, we compare AML ontology matching results and the results produced
by our contextual semantic matching based on AML input, all results are included in
Table 1. AML Results were downloaded from the results page of OAEI’s 2018 confer-
ence track6. We compare the results in terms of precision (fraction of correct mappings
returned by the algorithm), recall (fraction of the reference mappings returned by the
algorithm), and F-measure (2*((precision * recall) / (precision + recall)). The best we
can expect when applying our algorithm, is to improve precision by eliminating false
positive alignments returned by AML, that do not represent the same context. The best
we can achieve in terms of recall is to keep it the same value as AML’s, as our algorithm
cannot discover new true positive alignments, but only checks the contextual semantics
of the alignments produced by the AML matchmaker. In Table 1, the dotted cells mean
increasing in the score, white cells mean the score remains the same, and gray cells
means decreasing in the score.
As shown in the Table 1, we can divide the table to four groups according to the in-
crease/decrease in precision and recall:
• Group 1, precision increases and recall remains the same (no. 3 and 7): this means
that our algorithm successfully eliminates AML mappings that are false positives
(not in the reference alignments) without losing any true positives (true alignments).
This happens when the two ontologies have similar contexts and describe their con-
cepts in almost similar ways. These ontologies are typical candidates in an ontology
reuse scenario, described in the introduction, as they are describing the conference
domain from the same context.
4 https://owl.vse.cz/ontofarm/
5 http://oaei.ontologymatching.org/2018/results/conference/#crisp-ra
6 http://oaei.ontologymatching.org/2018/conference/index.html
10
• Group 2, precision increases but at the same time recall decreases (no. 2, 8, 9, 10,
12, 13, 14, 16, and 17): this means that the algorithm successfully eliminates false
positive mappings but also eliminates some true positive mappings. These are the
majority of the cases; they happen when the two ontologies are generally having the
same context, but they describe their concepts in slightly different ways.
• Group 3, precision decreases and recall decreases (no. 1, 4, 5, 6, 19, and 20): this
means that the algorithm eliminates true positive mappings without discovering false
positive mappings. That happens when the two ontologies are from the same domain,
but they describe the concepts in different ways, or they are from interdisciplinary
domains sharing common vocabularies.
• Group 4, precision and recall remain the same (no. 11 and 21), this means that AML
mappings are all true positives and our algorithm successfully discovers all of them,
and the two ontologies describe the same context.
Table 1. Comparison AMLs and our algorithms’ precision, recall, and F-measure results for the
alignment tasks of the Conference track.
AML Results Our Algorithm Results
Ontologies Precision Recall Fmeasure Precision Recall Fmeasure
1 cmt-conference 0.64 0.58 0.61 0.6 0.5 0.55
2 cmt-confOf 0.89 0.8 0.84 1 0.7 0.82
3 cmt-edas 0.89 1 0.94 1 1 1
4 cmt-ekaw 0.75 0.75 0.75 0.71 0.63 0.67
5 cmt-iasted 0.8 1 0.89 0.75 0.75 0.75
6 cmt-sigkdd: 0.9 0.9 0.9 0.89 0.8 0.84
7 conference-confOf: 0.73 0.8 0.76 0.8 0.8 0.8
8 conference-edas: 0.69 0.64 0.67 0.75 0.43 0.55
9 conference-ekaw: 0.78 0.78 0.78 0.84 0.7 0.76
10 conference-iasted: 0.83 0.38 0.53 1 0.23 0.38
11 conference-sigkdd: 0.83 0.83 0.83 0.83 0.83 0.83
12 confOf-edas: 0.9 0.6 0.72 1 0.47 0.64
13 confOf-ekaw: 0.94 0.8 0.86 1 0.75 0.86
14 confOf-iasted: 0.8 0.44 0.57 1 0.33 0.5
15 confOf-sigkdd: 1 0.83 0.91 1 0.33 0.5
16 edas-ekaw: 0.79 0.58 0.67 0.9 0.47 0.62
17 edas-iasted: 0.82 0.47 0.6 0.86 0.32 0.46
18 edas-sigkdd: 1 0.69 0.82 1 0.54 0.7
19 ekaw-iasted: 0.88 0.7 0.78 0.8 0.4 0.53
20 ekaw-sigkdd: 0.8 0.73 0.76 0.78 0.64 0.7
21 iasted-sigkdd: 0.81 0.87 0.84 0.81 0.87 0.84
0.83 0.72 0.76 0.87 0.59 0.68
Dotted cells indicate an increase in the score
Gray cells indicate a decrease in the score
White cells indicate that the score remains the same
11
4.3 Discussion
In this section we discuss individual mapping results, in particular those where our al-
gorithm mistakenly discards true positives, but also where it correctly discards false
positives detected by AML.
Case 1. This is an example where our algorithm succeeds to confirm a true positive
mapping identified by AML correctly. Consider the two ontologies Cmt and Confer-
ence (part of the competition tasks of the OAEI’s Conference track), shown in Fig. 5.
Consider the concept mappings (Cmt:SubjectArea, Conference:Topic) and
(Cmt:Chairman, Conference:Chair) identified by AML.
Mapping (Cmt:SubjectArea, Conference:Topic): This mapping is supported by evi-
dence of type 4 (having correspondent siblings) as defined in Sec.3. The mappings
(Cmt:Document, Conference:Conference_document), (Cmt:Person, Conference:Per-
son), and (Cmt:Conference, Conference:Conferece) referring to siblings of Cmt:Sub-
jectArea and, Conference:Topic, provide evidence that the concepts Subject-Area and
Topic share the same contextual meaning in the two input ontologies.
Mapping (Cmt:Chairman, Conference:Chair): This mapping is supported by evidence
of type 1 (having correspondent super-classes). Since there is a mapping (Cmt:Person,
Conference:Person), which refers to super-classes of Cmt:Chairman and Confer-
ence:Chair respectively, this is an evidence that the concepts Chairman and Chair
share the same contextual meaning within the given input ontologies.
Cmt Ontology Conference Ontology
Fig. 5. Concepts SubjectArea and Chairman in Cmt ontology, and concepts Topic and Chair in
Conference ontology
Case 2. This is as example where our algorithm succeeds to detect false positive map-
pings, which were mistakenly detected by AML as true mappings. Consider the
12
ontologies Conference and Iasted (part of the competition tasks of the OAEI’s Confer-
ence track), shown in Fig. 6., and ontologies Edas and Ekaw, shown in Fig. 7.
Mapping (Conference:Presentation, Iasted:Presentation): This mapping is not sup-
ported by any of the evidences defined in Sec.3. (there are no corresponding super-
classes, sub classes, equivalent classes, or siblings). Thus, the algorithm discards this
mapping which is a false positive one as it is not included in the reference alignments.
Looking closely to both ontologies we can observe that concept Presentation is repre-
sented differently in each ontology, as in ontology Conference a presentation is a spe-
cial type of conference documents while in Iasted ontology a presentation is a special
type of a conference activity. To conclude, although the two concepts Presentation have
exactly the same labels, they have different contextual semantics in both ontologies.
Conference ontology Iasted ontology
Fig. 6. Concepts Presentation in Conference and Iasted ontologies
Mapping (Edas:Conference, Ekaw:Conference): This mapping is not supported by any
of the evidences defined in Sec.3. (there are no corresponding super-classes, sub clas-
ses, equivalent classes, or siblings). Thus, the algorithm discards this mapping which is
a false positive one as it is not included in the reference alignments. Looking closely to
both ontologies we can observe that concept Conference is represented differently in
each ontology, as in ontology Edas the concept Conference is sub-class of a Thing and
has sibling relations with concepts like ConferenceEvent, Document, Person, etc. On
the other hand, a Conference in Ekaw ontology is represented differently as a special
type of Event, namely Scientific Event and has sibling relations with the concepts Work-
shop, Session, Track, etc. Thus, the mapping (Edas:Conference, Ekaw:Conference),
was discarded and considered as having different contextual semantics in both ontolo-
gies.
13
Edas Ontology Ekaw Ontology
Fig. 7. Concepts Conference in Edas and Ekaw ontologies
Case 3. Finally, this is an example where our algorithm mistakenly discards mappings
that are true positives. As it turns out, this is because the knowledge encoded in the
ontologies does not provide evidence for this mapping. Consider the ontologies Cmt
and Sigkdd (part of the competition tasks of the OAEI’s Conference track), shown in
Fig. 8.
Mapping (Cmt:ProgramCommittee, Sigkdd:Program_Committee): This mapping is
not supported by any of the evidences defined in Sec.3. (there are no corresponding
super-classes, sub classes, equivalent classes, or siblings). This mapping is a true posi-
tive one as it is included in the reference alignments, but our algorithm incorrectly dis-
cards it as it lacks evidence in the ontologies. In Fig. 8., concepts Program Committee
are represented differently in the two ontologies, in Sigkdd ontology it is a type of com-
mittee and a committee is related to the conferences, while in Cmt ontology Program
Committee is a concept having sibling relations with concepts conferences, documents,
persons, etc.
Although our algorithm discards this type of mappings (the true positive ones), this
does not mean that those mappings are not valid. It just means that they lack evidence
in their ontologies and represented differently in the contexts of their ontologies (i.e.,
have different intensional meanings). In an ontology reuse scenario where we want to
reuse existing ontologies to extend an input ontology, as part of the future work, the
priority of suggested concepts will be given to concepts that are validated against evi-
dence of contextual semantic equivalence. Other non-validated pairs (like this case)
could be presented to the expert in a different list to evaluate their validity. This could
be done using confidence score, which is part of the future work.
14
Cmt Ontology Sigkdd Ontology
Fig. 8. Concepts Program Committee in Cmt and Sigkdd ontologies
5 Conclusion and Future Directions
Developing new ontologies by reusing ontology fragments or concepts from existing
ontologies is considered a best practice in ontology engineering. To support this, we
require methods that identify pairs of concepts in a given source ontology and target
ontology that share the same contextual meaning (intensional meaning). This would
help ontology engineers to choose appropriate concepts from source ontologies to ex-
tend their target ontology. Existing matchmakers do not distinguish different contextual
meanings of concepts. In this paper, we propose a new evidence-based, contextual ap-
proach to validate matchings obtained from an ontology mapping system (e.g. AML)
by checking whether they refer to concepts that share the same meaning within the
context of their source ontologies. Our evaluation based on OAEI’s competition tasks
in the Conference track shows that our algorithm successfully confirms true positives
and discards false negatives from the set of mappings returned by the AML matcher. In
some cases, however, it failed to confirm true positives when concepts presented dif-
ferently in their ontologies.
In the future we are intending to enhance our evidence-based, contextual matching
algorithm to avoid losing the true positive mappings. This could be done by examining
concepts’ annotations (i.e., comments, definitions, etc.), if exists, and other external
knowledge to verify contextual semantic equivalence of concepts. Then, we could build
an ontology reuse system that aims to reuse existing ontologies to develop new ontolo-
gies by extending an input ontology in a target domain. Examining the validity of our
algorithm in a real case scenario, the new developed ontology, would be assessed by
domain expert.
References
1. Abdelreheim M., Klan F., Soliman T. (2017) Towards personalized support for ontology
selection. In: the 29th GI-Workshop Grundlagen von Datenbanken. pp 13–18
2. Arnold P., Rahm E. (2014) Enriching ontology mappings with semantic relations. Data
Knowl Eng 93:1–18. doi: https://doi.org/10.1016/j.datak.2014.07.001
15
3. Aumueller D., Do H-H, Massmann S., Rahm E. (2005) Schema and Ontology Matching
with COMA++. In: Proceedings of the 2005 ACM SIGMOD International Conference
on Management of Data. ACM, New York, NY, USA, pp 906–908
4. Caldarola EG., Picariello A., Rinaldi AM. (2015) An Approach to Ontology Integration
for Ontology Reuse in Knowledge Based Digital Ecosystems. In: Proceedings of the 7th
International Conference on Management of Computational and Collective intElligence
in Digital EcoSystems. ACM, New York, NY, USA.
5. Djeddi WE., Khadir MT. (2014) A Novel Approach Using Context-Based Measure for
Matching Large Scale Ontologies. In: Data Warehousing and Knowledge Discovery.
Springer International Publishing, Cham.
6. Faessler E., Klan F., Algergawy A., König-Ries B., Hahn U. (2017) Selecting and
Tailoring Ontologies with JOYCE. In: Knowledge Engineering and Knowledge
Management. Springer International Publishing, Cham, pp 114–118
7. Faria D., Pesquita C., Santos E., Palmonari M., Cruz IF., Couto FM. (2013) The
AgreementMakerLight Ontology Matching System. In: On the Move to Meaningful
Internet Systems: OTM 2013 Conferences. Springer Berlin Heidelberg, Berlin,
Heidelberg, pp 527–541
8. Jean-Mary YR., Shironoshita EP., Kabuka MR. (2009) Ontology matching with
semantic verification. J Web Semant 7:235–251. doi:
https://doi.org/10.1016/j.websem.2009.04.001
9. Jiménez-Ruiz E., Cuenca Grau B. (2011) LogMap: Logic-Based and Scalable Ontology
Matching. In: The Semantic Web -- ISWC 2011. Springer Berlin Heidelberg, Berlin,
Heidelberg, pp 273–288
10. Martínez-Romero M., Jonquet C., O’Connor MJ., Graybeal J., Pazos A., Musen MA.
(2017) NCBO Ontology Recommender 2.0: an enhanced approach for biomedical
ontology recommendation. J Biomed Semantics 8:21.
11. Martínez-Romero M., Vázquez-Naya JM., Pereira J., Pazos A. (2014) BiOSS: A system
for biomedical ontology selection. Comput Methods Programs Biomed 114:125–140.
doi: https://doi.org/10.1016/j.cmpb.2014.01.020
12. Shah T., Rabhi F., Ray P., Taylor K. (2014) A Guiding Framework for Ontology Reuse
in the Biomedical Domain. In: 2014 47th Hawaii International Conference on System
Sciences. pp 2878–2887
13. Simperl E. (2009) Reusing ontologies on the Semantic Web: A feasibility study. Data
Knowl Eng 68:905–925. doi: https://doi.org/10.1016/j.datak.2009.02.002
14. Tigrine AN., Bellahsene Z., Todorov K. (2015) Light-Weight Cross-Lingual Ontology
Matching with LYAM++. In: On the Move to Meaningful Internet Systems: OTM 2015
Conferences. Springer International Publishing, Cham, pp 527–544
15. Zulkarnain NZ., Meziane F., Crofts G. (2016) A Methodology for Biomedical Ontology
Reuse. In: Natural Language Processing and Information Systems. Springer
International Publishing, Cham, pp 3–14