=Paper=
{{Paper
|id=Vol-2228/short6
|storemode=property
|title=Mapping Refinement based on Ontology Evolution: A Context-Matching Approach
|pdfUrl=https://ceur-ws.org/Vol-2228/short6.pdf
|volume=Vol-2228
|authors=Julio Cesar dos Reis
|dblpUrl=https://dblp.org/rec/conf/ontobras/Reis18
}}
==Mapping Refinement based on Ontology Evolution: A Context-Matching Approach==
https://ceur-ws.org/Vol-2228/short6.pdf
Mapping Refinement based on Ontology Evolution: A
Context-Matching Approach
Julio Cesar dos Reis1
1
Institute of Computing, University of Campinas (UNICAMP)
13083-852, Campinas, SP, Brazil
jreis@ic.unicamp.br
Abstract. Ontologies and their associated mappings play a central role in se-
veral semantic-enabled tasks. However, the continuous evolution of ontologies
requires updating existing concept alignments. Whereas mapping maintenance
techniques have mostly handled revision and removal type of ontology changes,
the addition of concepts and attributes demand further studies. This article pro-
poses techniques to refine set of established mappings based on the evolution
of ontologies. We investigate ways of suggesting correspondences with the new
version of the ontology without applying a matching operation to the whole set
of ontology entities. Obtained results explore the neighbourhood of concepts in
the alignment process to update mapping sets.
1. Introduction
Over the last decade several domains have exploited ontologies and their capabilities for
various purposes ranging from information retrieval to data management and sharing.
However, the size of these domains often requires the use of several ontologies whose
elements are linked through mappings. Mappings are the materialization of semantic
relations between elements of interrelated ontologies [Shvaiko and Euzenat 2013].
Creating mappings between ontologies is a complex task especially due to the
increasing size of ontologies. To this end, several automatic ontology alignment te-
chniques have been proposed [Mascardi et al. 2010]. Nevertheless, significant ma-
nual efforts of validation are still demanded if a certain level of quality is required
[Shvaiko and Euzenat 2013]. This problem prevents software applications relying on
mappings to fully take advantage on them.
Ontologies evolve over time to keep them up-to-date according to the domain kno-
wledge. Ontology changes may affect mappings already established or can be a source
for treating mapping refinement. In this context, in order to avoid the costly ontology
re-alignment process, it is crucial to have adequate mapping adaptation strategies to
keep mappings semantically valid over time [Reis et al. 2015]. Manual mapping main-
tenance is possible only if modifications are applied to a restricted number of mappings.
Otherwise automatic methods are required for large and highly dynamic ontologies. For
example, biomedical ontologies usually contain hundred of thousands of concepts inter-
connected via mappings.
Coping with the mapping reconciliation problem in a semi-automatic way entails
many research challenges. First, it is difficult to evaluate the real impact of the ontology
evolution on mappings. For instance, changing an attribute value may lead to invalidate
a mapping in some cases. In these situations, the challenging issue is to identify and
classify the different cases. Second, several types of ontology changes can be applied
to an ontology, but it is unknown how these different types of operations should be duly
taken into consideration for mapping reconciliation [Groß et al. 2013]. Thus, generally
only the removal of concepts is addressed. Third, the reconciliation of mappings with
several types of semantic relations need deeper studies [Reis et al. 2015].
The design of techniques for mapping adaptation according to the different ty-
pes of ontology changes has been coped within existing approaches. Previous work in
literature [Reis et al. 2015] presented a mapping adaptation strategy for two out of three
categories of ontology evolution: removal of knowledge and revision of knowledge. For
example, when concepts are removed, heuristics were designed to automatically apply
adaptation actions over mappings. The addition of knowledge (third category) is the most
frequent type of change occurred in ontology evolution. New concepts or attributes in
concepts are added to comply with new domain knowledge. Such new knowledge needs
to be aligned with the interrelated ontologies, but we need to avoid applying a matching
operation with the whole target ontology.
Literature presents several interpretations for ontology mapping refinement. The
first interpretation is in the sense of helping to expand the types of semantic relations
identified during the matching process. Refinement can modify or enrich semantic re-
lations; for instance, during the refinement process, an equivalence (≡) relation (i.e., a
relationship defining that two interrelated concepts are equivalent) can be modified to an
is-a (v) relationship (i.e., representing a relationship where one concept is a specializa-
tion of the other) [Arnold and Rahm 2014]. Another sense is refinement operations as a
way of adding (deriving) or removing mappings, for instance, based on defined pattern
rules [Hamdi et al. 2010]. These rules can be created manually or automatically, and are
used to validate the mapped relations. The second category aims to enrich relations in a
previously calculated mapping and the mapping set with new derived mappings. In this
paper, we explore the second option.
In this paper, we propose a mapping refinement methodology to update mapping
sets taking ontology changes into account (based on new concepts added in ontology evo-
lution). We study the use of conceptual information related to neighbour concepts for
enhancing the mapping adaptation quality. For this purpose, we investigate techniques
to reuse already established mappings and to explore the role of neighbour concepts to
derive new mappings. Our proposal allows suggesting new correspondences without ap-
plying a matching operation with the whole set of ontology entities. The remainder of this
article is organized as follows: Section 2 presents the formal definitions and problem sta-
tement. Section 3 reports on our approaches to refine ontology mappings under ontology
evolution. Section 4 draws conclusions and future work.
2. Preliminaries
Ontology. An ontology O specifies a conceptualization of a domain in terms of concepts,
attributes and relationships [Gruber 1993]. Formally, an ontology O = (CO , RO , AO )
consists of a set of concepts CO interrelated by directed relationships RO . Each
concept c ∈ CO has a unique identifier and is associated with a set of attributes
AO (c) = {a1 , a2 , ..., ap }. Each relationship r(c1 , c2 ) ∈ RO is typically a triple (c1 , c2 , t)
where t is the relationship (e.g., “is a”, “part of”, “adviced by”, etc.) interrelating c1 and
c2 .
Context of a concept. We define the context of a particular concept ci ∈ CO as a set of
super concepts, sub concepts and sibling concepts of ci , as following:
CT (ci , λ) = sup(ci , λ) ∪ sub(ci , λ) ∪ sib(ci , λ) (1)
where
sup(ci , λ) = {cj |cj ∈ CO , r(ci , cj ) = “ @ ” ∧ length(ci , cj ) ≤ λ ∧ ci 6= cj }
sub(ci , λ) = {cj |cj ∈ CO , r(cj , ci ) = “ @ ” ∧ length(ci , cj ) ≤ λ ∧ ci 6= cj } (2)
sib(ci , λ) = {cj |cj ∈ CO , sup(cj ) = sup(ci ) ∧ length(ci , cj ) ≤ λ ∧ ci 6= cj }
where λ is the length between two concepts (in terms of their relationship distance in the
hierarchy of concepts) and the “@” symbol indicates that “ci is a sub concept of cj ”. This
definition of CT (ci , λ) is specially designed as the relevant concepts to be taken into
account in the settings of this investigation on mapping refinement.
Similarity between concepts. Given two particular concepts ci and cj , the simila-
rity between them can be defined as the maximum similarity between each couple of
attributes from ci and cj . Formally:
sim(ci , cj ) = arg max sim(aix , ajy ) (3)
where sim(aix , ajy ) is the similarity between two attributes aix and ajy denoting concepts
ci and cj , respectively. We can compute this similarity at different linguistic levels:
character, string and semantic level [Dinh et al. 2014].
Mapping. Given two concepts cs and ct from two different ontologies, a mapping
mst can be defined as:
mst = (cs , ct , semT ype, conf ) (4)
where semT ype is the semantic relation connecting cs and ct . In this article, we diffe-
rentiate relation from relationship, where the former belongs to a mapping and the later
to an ontology. The following types of semantic relation are considered: unmappable
[⊥], equivalent [≡], narrow-to-broad [≤], broad-to-narrow [≥] and overlapped [≈]. For
example, concepts can be equivalent (e.g., “head”≡“head”), one concept can be less or
more general than the other (e.g., “thumb”≤“finger”) or concepts can be somehow seman-
tically related (≈). The conf is the similarity between cs and ct indicating the confidence
of their relation [Euzenat and Shvaiko 2007]. We define MjST as a set of mappings mst
between ontologies OS and OT at a given time j. We assume j ∈ N the version of the
ontology release OSj . Therefore, ontology OS0 is the version 0 whereas OS1 is the version
1 of the same ontology.
Ontology change operations (OCO). An ontology change operation (OCO) is
defined to represent a change in an attribute, in a set of one or more concepts or in a re-
lationship between concepts. OCOs are classified into two main categories: atomic and
complex changes. Each OCO in the former cannot be divided into smaller operations
while each one of the latter is composed of more than one atomic operation. For ins-
tance, the operation chgA(c, a, v) is composed of two atomic operations delA(a, c) and
addA(a, c). In this paper, we pay further attention to the operations of concept addition.
Problem statement. Consider two versions of the same source ontology OSj at
time j and OSj+1 at time j + 1, a target ontology OTj , and an initial set of mappings MjST
between OSj and OTj at time tj . Suppose that the frequency of new releases of OS and OT
is different and at time j + 1 only OS evolves. Since this evolution is likely to impact the
mappings MjST , it is necessary to refine MjST to guarantee the quality and completeness
of Mj+1ST . The quality is related to the consistency of mappings. For instance, mappings
cannot be established between removed concepts. The completeness refers to the recall of
aligned concepts in Mj+1 j
ST . In this investigation, we study how MST can be refined (e.g.,
new mappings derived) based on ontology changes related to addition of knowledge. In
particular, we address the following research questions:
• How to exploit existing mappings for mapping refinement?
• Is it possible to reach mapping refinement for alignment of new concepts without
applying a matching operation in the whole target ontology?
• What is the impact of using the context of concepts CT (ci , λ) in the target onto-
logy on the mapping refinement effectiveness?
We consider that OT has not evolved (thus OTj and OTj+1 are the same version of
the ontology OT ). OSj and OSj+1 are two distinct versions of the same ontology OS . At
time j + 1, newly added concepts appear in OSj+1 and we attempt to refine the original
j j+1
mapping set MST to provide a set of valid mappings MST .
3. Mapping Refinement under Concept Addition Changes
At the first step, our approach identifies all newly added concepts using the Conto-Diff
tool [Hartung et al. 2013]. This tool allows to identify atomic and complex ontology
changes. Next, we extract the contextual information, i.e., super, sub and sibling concepts
of those newly added concepts (cf. Formula 1). At this stage, we consider λ = 1 (i.e.,
direct parents and children concepts). We then examine the existing mappings between
the source concept in the context of the newly added concept and the corresponding target
concepts.
We propose adequate correspondences for each newly added concept. The
idea behind the context-oriented technique is that the candidate mapping is established
between a newly added concept and a target concept of an existing mapping at time j.
Figure 1 illustrates the general scenario for the local approach to mapping refinement
where we consider that OTj and OTj+1 are the same ontology while the source ontology
OSj evolved to OSj+1 and presents new added concepts at time j + 1.
Algorithm 1 computes the diff between two given versions of the source ontology
(line 1). For each newly added concept c1i , the algorithm considers a candidate con-
cept namely ct in the target ontology by exploiting already existing mappings related to
CT (c1i , 1) (lines 4-8). We determine a new refined mapping by calculating the similarity
between a new concept c1i of OSj+1 and a candidate ct . If the maximum similarity (among
the concept attributes) is greater than or equal to a threshold τ , the algorithm establishes a
mapping between the newly added concept and the candidate target concept. Algorithm 1
searches for the candidate ct that yields the maximum similarity value. The semantic
type of the new mapping is computed according the relationship between the newly ad-
ded concept and the optimal concept c1ij ∈ CT (c1i ). For instance, if c1ij ∈ sup(c1i ) so the
mit .semt ype ←≤. This aspect requires further research to determine the type of semantic
relation of the derived new mapping.
Figura 1. Context approach for mapping refinement.
Algorithm 1: Contextual approach to mapping refinement
Input: OSj , OSj+1 , OTj , OTj+1 , MjST , τ ∈ R
Output: MA = {m1 , m2 , ..., mN }
j j+1
1: Cadd ← dif fadd (OS , OS ) {newly added concepts}
2: Ct ← ∅ {initialize target concepts of candidate mappings}
3: for all c1i ∈ Cadd do
4: for all c1ij ∈ CT (c1i , 1) do
5: if ∃c0t ∈ COT0 , ∃m(c0ij , c0t ) ∈ M0ST then
S 0
6: Ct ← Ct {ct }
7: end if
8: end for
9: mit ← ∅
10: for all ct ∈ Ct do
11: mcand ← argmax sim(c1i , ct ) {Create a mapping between concepts c1i and ct }
12: if max(sim(c1i , ct )) ≥ τ then
13: mit ← mcand
14: τ ← max(sim(c1i , ct ))
15: end if
16: end for S
17: MA ← MA {mit }
18: end for
19: return MA
4. Conclusion
Ontology mappings play a central role for semantic data integration. However, domain
knowledge update leads to new concepts in ontology versions. This requires to maintain
mapping sets up-to-date according to the knowledge dynamics. In this article, we propo-
sed techniques to refine ontology alignments based on evolving ontologies. We defined
an algorithm to create contextual alignment to increase the chances of obtaining mat-
ching between concepts. Our developed approach relied on similarity computed between
concept attributes to maximize the similarity value (semantic confidence) between the
interrelated mappings proposed. Future work involves further investigating heuristics to
update the type of semantic relation in the refinement in the investigated procedures. We
plan to conduct exhaustive experiments in different domains to evaluate the quality of
detected refined mappings.
Acknowledgements
This work is financially supported by the São Paulo Research Foundation (FAPESP)1
(Grant #2017/02325-5).
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1
The opinions, hypotheses and conclusions or recommendations expressed in this material are the res-
ponsibility of the authors and do not necessarily reflect the views of FAPESP.