=Paper=
{{Paper
|id=Vol-2519/paper7
|storemode=property
|title=Matching BFO, DOLCE, GFO and SUMO: an Evaluation of OAEI 2018 Matching Systems
|pdfUrl=https://ceur-ws.org/Vol-2519/paper7.pdf
|volume=Vol-2519
|authors=Daniela Schmidt,Cassia Trojahn,Renata Vieira
|dblpUrl=https://dblp.org/rec/conf/ontobras/SchmidtTV19
}}
==Matching BFO, DOLCE, GFO and SUMO: an Evaluation of OAEI 2018 Matching Systems==
https://ceur-ws.org/Vol-2519/paper7.pdf
Matching BFO, DOLCE, GFO and SUMO: an evaluation of
OAEI 2018 matching systems
Daniela Schmidt1 , Cassia Trojahn2 , Renata Viera1
1
Pontifical Catholic University of Rio Grande do Sul (Brazil)
daniela.schmidt@acad.pucrs.br, renata.vieira@pucrs.br
2
Institut de Recherche en Informatique de Toulouse (France)
cassia.trojahn@irit.fr
Abstract. Foundational ontologies play an important role in the construction
and integration of domain ontologies, providing a well-founded reference model
that can be shared across domains. Different foundational ontologies have been
developed, under different philosophical perspectives. Interoperability across
domain ontologies relying on different foundational ontologies depends hence
on the ability of interchanging foundational ontologies. The first step toward
this task is to find correspondences between them. This paper extends previ-
ous work in the analysis of automatic matching system in the task of matching
foundational ontologies. We discuss the weaknesses of existing proposals and
highlight the challenges to be addressed in the the field.
1. Introduction
Foundational ontologies play an important role in the construction and integration of do-
main ontologies, providing a well-founded reference model that can be shared across
domains. They describe general concepts (e.g., physical object, event) and relations (e.g.,
parthood, participation), which are independent of a particular domain. These ontologies,
also named upper or top-level, are usually equipped with a rich axiomatic layer. While
the clarity in semantics and a rich formalization of foundational ontologies are important
requirements for ontology development [Mika et al. 2004, Keet 2011] improving onto-
logy quality, they may also act as semantic bridges supporting interoperability between
ontologies [Mascardi et al. 2010, Keet 2011, Nardi et al. 2013].
While the purpose of a foundational ontology is to solve interoperability issues
among ontologies, the development of different foundational ontologies re-introduces
the ontology interoperability problem, as stated in [Khan and Keet 2013a]. Early works
addressed this problem [Grenon 2003, Seyed 2009, Temal et al. 2010] on different pers-
pectives. While fundamental issues and primitive relations between BFO and DOLCE
have been studied in [Grenon 2003] and [Seyed 2009], respectively, [Temal et al. 2010]
established an alignment between these ontologies in order to conciliate their respective
realistic and cognitive points of view. In [Muñoz and Grüninger 2016], the core charac-
terization of mereotopology of SUMO and DOLCE has been studied, relating their axi-
omatizations via ontology alignments, while in [Oberle et al. 2007] alignments between
DOLCE and SUMO have been established for supporting domain ontology integration.
In [Khan and Keet 2013a, Khan and Keet 2013b], alignments between BFO, DOLCE and
GFO were built both with automatic matching tools and manually, with substantially
fewer alignments found by the matching tools.
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
In this paper we analyze the behaviour of automatic matching systems in
the task of matching foundational ontologies. This work extends the work from
[Khan and Keet 2013a, Khan and Keet 2013b] in two ways: it considers more recent
matching systems, those participating in the Ontology Alignment Evaluation Initiative
(OAEI) 2018, and it considers a new pair of aligned foundational ontologies SUMO and
DOLCE [Oberle et al. 2007], which consists of subsumption relations. The alignments in
[Khan and Keet 2013a] and [Oberle et al. 2007] served as a reference alignment in order
to evaluate the matchers.
The aim here is not to evaluate the matchers themselves (as to point out the
best matcher) but rather to analyse how they behave in the task, and specially to com-
pare the new results to those obtained in [Khan and Keet 2013a]. As these previous
[Khan and Keet 2013a, Khan and Keet 2013b] works have pointed out the weaknesses
of matchers to deal with the task, we aim at analysing (quantitatively) whether a pro-
gress towards that question has been made so far. We discuss their results, point out their
weaknesses, and highlight the challenges to be addressed in the the field.
The rest of this paper is organized as follows. Section 2 introduces the main foun-
dational ontologies and Section 3 discusses the related work. We present the experiments
in Section 4 and discuss the results in Section 5. Finally, Section 6 ends the paper pointing
out directions for future work in the field.
2. Foundational ontologies
A foundational ontology is a high-level and domain independent ontology whose con-
cepts (e.g., physical object, event, quality, etc.) and relations (e.g., parthood, participa-
tion, etc.) are intended to be basic and universal to ensure generality and expressive-
ness for a wide range of domains. It is often characterized as representing commonsense
concepts. Diverse foundational ontologies have been developed, influenced by different
philosophies and views on the reality. Several comparisons can be found in the lite-
rature [Semy et al. 2004, Mascardi et al. 2007, Khan and Keet 2012], which mostly fo-
cus on software engineering criteria (dimensions, representation languages, modularity)
[Mascardi et al. 2007] or ontological commitments and subject domain and applications
[Khan and Keet 2012]. We introduce here the main insights behind the foundational on-
tologies most cited in the literature. We are aware however that other ones have been
proposed, such as SOWA’s ontology, YAMATO, GIST, and KIOTO, but were left out of
the scope.
• BFO [Grenon et al. 2004, Arp et al. 2015]1 (Basic Formal Ontology) is a founda-
tional ontology that adopts a realistic approach in terms of the existence in time
of entities populating the world. It represents the reality into two disjoint cate-
gories of continuant (objects, attributes, and locations) and occurrent (processes
and temporal regions. With different versions, BFO 2.0 represents major updates
to BFO not strictly backwards compatible with BFO 1.1 and a manual alignment
was required to express their incompatibilities.
• DOLCE [Gangemi et al. 2002]2 (Descriptive Ontology for Linguistic and Cog-
nitive Engineering) is an ontology of particulars which adopts a descriptive ap-
1
https://github.com/bfo-ontology/BFO/wiki
2
http://www.loa.istc.cnr.it/old/DOLCE.html
proach with a clear cognitive bias, as it aims at capturing the ontological cate-
gories underlying natural language and human commonsense. DOLCE is based
on a fundamental distinction between endurant and perdurant entities. Endurants
represent objects or substances while perdurants corresponds to events or proces-
ses. The main relation between endurants and perdurants is that of participation.
DOLCE and its different variants have been used in diverse proposals, as many ef-
forts have been dedicated to the development of this ontology. DOLCE has been
exposed with reduced axiomatization and extensions with generic or domain plu-
gins, such as for DOLCE-Lite [Gangemi et al. 2003], DOLCE-Lite-Plus3 or still
DOLCE+DnS Ultralite4 .
• OpenCyc [Guha and Lenat 1993] is a foundational ontology involving thousands
of “microtheories”. It is meant for the representation of facts, rules, and heuristics
to reason about the objects and events of everyday life in the Cyc knowledge base.
It is the open source version of Cyc, a commercial product by Cycorp.
• GFO [Herre et al. 2007]5 (General Formal Ontology) is a foundational ontology
that considers basic distinctions between individuals. Concrete individuals exist
in time or space whereas abstract individuals do not. While an endurant is an
individual that exists in time, but cannot be described as having temporal parts or
phases; processes, on the other hand, are extended in time.
• PROTON [Terziev et al. 2005]6 (PROTo ONtology) serves as a lightweight foun-
dational ontology organized in three levels, including four modules describing.
The top ontology module, for instance, distinguishes entity types, such as object
as existing entities (agents, locations, vehicles); happening as events and situati-
ons; and abstract as abstractions that are neither objects, nor happenings.
• SUMO [Niles and Pease 2001]7 (Suggested Upper Merged Ontology) is as on-
tology of particulars and universals which has two top-level concepts. Physical
represents an entity that has a location in space-time. An abstract can be said to
exist in the same sense as mathematical objects such as sets and relations, but they
cannot exist at a particular place and time without some physical encoding.
• UFO [Guizzardi 2005, Guizzardi and Wagner 2010]8 (Unified Foundational On-
tology) started as an unification of the GFO and the foundational ontology of uni-
versals underlying OntoClean9 . UFO is divided in three parts representing diffe-
rent aspects of reality: an ontology of endurants (objects), an ontology of perdu-
rants (events and processes), and an ontology of social entities, with notions such
as beliefs, desires, intentions, etc.
3. Related work
Early works have addressed the problem of comparing or aligning foundational on-
tologies [Grenon 2003, Seyed 2009, Temal et al. 2010] on different perspectives. In
[Grenon 2003], fundamental issues (as significant discrepancies related to universals and
3
http://www.loa.istc.cnr.it/old/ontologies/DLP\_397.owl
4
http://www.ontologydesignpatterns.org/ont/dul/DUL.owl
5
http://www.onto-med.de/ontologies/gfo/
6
http://ontotext.com/proton
7
https://github.com/ontologyportal/sumo
8
http://dev.nemo.inf.ufes.br/seon/UFO.html
9
http://www.ontoclean.org
particulars, qualities, constitution and spatio-temporality, etc.) and how similar notions
apply differently in BFO and DOLCE have been studied. This manual analysis was based
on the preliminaries versions of BFO (BFO 1.0). A comparison between these ontologies
has also carried out in [Seyed 2009], where the primitive relations (dependence, quality,
and constitution) between these BFO 1.0 and DOLCE have been discussed. While these
works studied the discrepancies and similarities between the ontologies under their philo-
sophical perspectives and conceptual points of views, giving some insights to correspon-
ding their concepts, in [Temal et al. 2010], a manual alignment in terms of equivalences
and subsumption correspondences between BFO (BFO 1.0) and DOLCE concepts has
been established. This alignment has been used to integrate an ontology of telecardio-
logy based on DOLCE to ontologies based on BFO such as FMA (Foundational Model
of Anatomy).
Other studies have addressed other foundational ontologies. In the work from
[Oberle et al. 2007], alignments between DOLCE and SUMO have been generated,
where two core ontologies, the SmartDOLCE and SmartSUMO ontologies, have been
developed on the basis of DOLCE and SUMO, respectively. The alignment of the SUMO
taxonomy to DOLCE involved prunning the upper-level of the SUMO taxonomy and the
non-trivial task of aligning the remaining concepts to appropriate DOLCE categories. In
[Muñoz and Grüninger 2016], the core characterization of mereotopology of SUMO and
DOLCE has been studied, relating their axiomatizations via ontology alignments. This
included corrections and additions of axioms to the analyzed theories which eliminate
unintended models and characterize missing ones. The resulting alignments have been
expressed in FOL.
The closest work to ours is from [Khan and Keet 2013a], where alignments
between BFO (BFO 1.1), DOLCE (DOLCE-Lite) and GFO have been established with
automatic matching tools (H-Match, PROMPT, LogMap, YAM++, HotMatch, Hertuda
and Optima) and also manually. During the process, it was found that differences in
foundational ontologies, such as their hierarchical structure, conflicting axioms due to
complement and disjointness, and incompatible domain and range restriction, cause lo-
gical inconsistencies in foundational ontology alignments, thereby greatly reducing the
number of correspondences. While the accuracy and percentage of alignments that were
found vary greatly among the tools, exploiting the aligned entities whilst keeping a con-
sistent ontology reduces the feasible set of alignments. The resulting alignments have
been made available at the ROMULUS platform [Khan and Keet 2013b] 10 . From this
experiment and a set of manually curated alignments, [Khan and Keet 2014] developed
the SUGOI tool (Software Used to Gain Ontology Interchangeability) which allows a
user to interchange automatically a domain ontology, by choosing the foundational one.
Aligning foundational ontologies reveals also the problem of matching their diffe-
rent versions. In [Seppälä et al. 2014], a method for tracking, explaining and measuring
changes between successive versions of BFO 1.0, BFO 1.1, and BFO 2.0 was applied.
The aim was to provide a more comprehensive analysis of the changes with respect to the
BFOConvert tool11 which provides an alignment between previous BFO versions, as this
resource is limited to allow for a full understanding of the impact of the changes.
10
http://www.thezfiles.co.za/ROMULUS/
11
http://ontobull.hegroup.org/bfoconvert (last viewed on April 1st, 2019)
4. Experiments
The aim of our experiment is to analyse how current matchers behave in the task of mat-
ching foundational ontologies. In our experiments, we used the set of matchers parti-
cipating in the OAEI 2018 campaign. Previous evaluation [Khan and Keet 2013c] has
considered a different set of matching tools (as described below) and manually evaluated
the generated alignments. Here, we analyse whether matchers have evolved in that task.
In the following, we describe the ontologies and the alignments used as ‘reference’ for
automatically evaluating the alignments generated by the matchers.
4.1. Ontologies and reference alignments
In our experiments we have used the following foundational ontologies Table 1: BFO,
DOLCE-Lite, GFO and SUMO. For BFO12 and GFO13 , we have used the versions refe-
renced and available at the ROMULUS repository14 . For DOLCE-Lite, whose link was
unreached at ROMULUS, the version was the one available on LOA15 (whose version has
the same base namespace than the one in the alignments from ROMULUS).
For the experiments involving SUMO, for sake of results reproducibility and
compatibility, we use the same DOLCE-Lite and SUMO ontology versions used in
[Oberle et al. 2007].
Table 1. Foundational ontologies. ∗ Logical axioms count from Protege.
Ontology Version #Classes #ObjProp #Axioms∗
BFO 1.1 39 0 95
DOLCE DOLCE-Lite 82 121 356
DOLCE-Lite [Oberle et al. 2007] 82 162 619
GFO 1.0 78 67 323
SUMO 630 236 1307
The pairs of alignments in Table 2 have been considered. As stated before, this
choice is based on the available existing alignments between the ontologies. The refe-
rence alignments for the first three pairs have been obtained from ROMULUS, while
the reference alignment for the pair DOLCE-Lite and SUMO has been the one from
[Oberle et al. 2007]. We refer to these alignments as reference, as they are the align-
ments manually curated and available for comparison. However, an analysis of them
shows that they are not exhaustive. First, the alignments in ROMULUS result from a
manual evaluation, selection and enrichment of correspondences generated by automatic
matchers. In that way, they may introduce a bias as we evaluate the tools on the ba-
sis of alignments that have been partially generated from automatic tools as well. The
alignments from [Oberle et al. 2007] have been manually generated to support the task of
ontology integration and for their purpose, they are composed of subsumptions (as shown
in Table 2.Besides the fact that the ontologies are equipped with object properties, with
12
https://raw.githubusercontent.com/BFO-ontology/BFO/releases/1.1.1/
bfo.owl
13
http://www.onto-med.de/ontologies/gfo.owl
14
http://www.thezfiles.co.za/ROMULUS/downloads.html (last view 3rd April 2019)
15
http://www.loa.istc.cnr.it/ontologies/DOLCE-Lite.owl
exception of the BFO 1.1 version, the alignment of this kind of ontology entity is covered
to a lesser extent.
Table 2. Reference alignments
Pair #Concepts #ObjProp
BFO–DOLCE-Lite 7≡ 0
BFO–GFO 11 ≡ 0
DOLCE-Lite–GFO 9≡ 6 ≡ 10 @
DOLCE-Lite [Oberle et al. 2007]–SUMO 41 @ 0
4.2. Matchers
All tools participating in the 2018 edition of the OAEI campaigns for schema-based tracks
were selected: ALIN, ALOD2Vec, AML, DOME, FCAMapX, Holontology, KEPLER,
Lily, LogMap, LogMapLt, POMAP++ and XMap. These tools implement different mat-
ching strategies. The reader can refer to the OAEI papers for details on the tools16 . All
the tools were run with their default configuration settings. As stated before, the aim here
is not to evaluate the matching systems themselves, for that reason, in the following we
anonymized the systems.
5. Results and discussion
Table 3 shows the results reported in [Khan and Keet 2013c] for the pairs BFO–DL, BFO–
GFO and DOLCE-Lite–GFO. As stated in Section 3, in that work the alignments genera-
ted by a set of matching systems have been manually evaluated in terms of precision.
Table 4 presents the results for the matchers which were able to generate a non
empty alignment for a least one pair of ontologies. In terms of F-measure, the best
results were observed for the pair involving BFO and GFO ontologies, from which
6 out 11 correspondences refer to lexically close terms (e.g., bfo:SpatialRegion and
gfo:Spatial Region) (2 out of 7 for BFO-DL and 4 out of 26 for DOLCE-Lite–GFO). For
the pair BFO–DOLCE-Lite, we observe that all matchers report the same recall, as they
were able to retrieve 2 out of 7 correct correspondences (bfo:Quality and dolce:quality,
and bfo:Process and dolce:process). Overall, matchers still mostly output corresponden-
ces between concepts whose associated terms are similarly written (for instance, invol-
ving exact match or substring match e.g., dolce:boundary and gfo:has boundary or invol-
ving head modifier as for bfo:Object gfo:Material object). While some are correct, such
as the examples above, the ones involving different terms are in general incorrect (e.g,
dolce:overlaps and gfo:requirement of).
There is only one common system in both experiments, but with different versions,
what explains its different results in terms of precision (in particular for the pairs BFO–
GFO and DOLCE-Lite–GFO). No matcher were able to generate subsumption relations,
as no matcher was able to find the correspondences for DOLCE-Lite and SUMO. For
this pair, only one correspondence refers to similar terms (dolce:geographical-object and
sumo:GeographicArea). From the 41 correspondences in the reference alignment, 5 of
them could have been found via a head modifier method (e.g., dolce:organization and
sumo:EducationalOrganization or dolce:organization and sumo:PoliticalOrganization).
Table 3. Results in terms of precision, as reported in [Khan and Keet 2013c].
System BFO–DOLCE-Lite BFO–GFO DOLCE-Lite–GFO
# P # P # P
H-Match 4 .25 5 .16 4 .16
PROMPT 3 .25 7 .58 8 .66
LogMap 2 1.0 11 .91 3 1.0
YAM++ 4 1.0 6 .85 13 .52
HotMatch 3 1.0 7 1.0 10 .83
Hertuda 3 1.0 7 1.0 11 .84
Optima 4 .30 9 .52 7 .17
Average 3.28 .69 7.42 .60 8 .72
Table 4. Classical precision (P), recall (R) and F-measure (F) of matchers.
System BFO–DOLCE-Lite BFO–GFO DOLCE-Lite–GFO DOLCE-Lite–SUMO
# P F R # P F R # P F R # P F R
M1 2 1.0 .44 .29 7 .86 .67 .55 9 .56 .29 .20 0 .00 .00 .00
M2 4 .50 .36 .29 6 .83 .59 .45 5 .40 .13 .08 18 .00 .00 .00
M3 3 .67 .40 .29 7 .86 .67 .55 19 .26 .23 .20 22 .00 .00 .00
M4 2 1.0 .44 .29 7 .86 .67 .55 4 .75 .21 .12 11 .00 .00 .00
M5 4 .50 .36 .29 7 .71 .56 .45 13 .38 .26 .20 22 .00 .00 .00
M6 2 1.0 .44 .29 7 .71 .56 .45 9 .75 .21 .12 15 .00 .00 .00
M7 2 1.0 .44 .29 7 .86 .67 .55 9 .56 .29 .20 15 .00 .00 .00
M8 4 .50 .36 .29 7 .86 .67 .55 5 .60 .20 .12 17 .00 .00 .00
Average 2.88 .77 .41 .29 6.88 .82 .63 .51 9.13 .53 .23 .16 15 .00 .00 .00
In order to see how close the generated alignments were to the reference, we have
calculated the relaxed precision and recall [Ehrig and Euzenat 2005]. That will consider
if closer correspondences than the ones given by the reference were found. For the pairs
BFO-DL, BFO-GFO and DL-GFO, the results for relaxed precision and recall were the
same than the ones reported using the classical precision and recall. This shows that
no other correspondence closer to the reference were found. For the pair DL-SUMO,
however, we observed that closer correspondences have been generated, as Table 5 shows.
However, the results are still poorer for this pair with respect to the others.
Overall, there is a slightly improvement in the average results (.66 from those
reported results to .70 reported here). For the pairs BFO–DOLCE-Lite and BFO–GFO
we can observe a relatively significant improvement in terms of precision (from .69 up to
.77 and from .60 up to.82), the results for the pair DOLCE-Lite–GFO decreases (.53 and
.72, respectively).
For two ontology pairs we observe that more recent systems are performing with
better precision. However it has dropped in one case. However, for a new added pair
involving SUMO, annotated with subsumption relations only, the results were clearly
worse. Although we can see some progress, it is not consistent throughout the entire
alignments base, and no progress was made towards subsumption relations.
16
http://www.om2018.ontologymatching.org/#ap
Table 5. Relaxed precision (P), recall (R) and F-measure (F) of matchers.
System DOLCE-Lite–SUMO
P F R
M1 .00 .00 .00
M2 .33 .18 .15
M3 .39 .27 .21
M4 .77 .34 .21
M5 .32 .25 .17
M6 .28 .14 .12
M7 .57 .31 .21
M8 .50 .42 .21
Average .40 .23 .16
Overall, we could observe that:
• besides the fact that they are not specifically designed to the task – to the best of
our knowledge there is no matcher designed to match foundational ontologies –
general purpose matchers are not able to correctly deal with the level of abstraction
of foundational ontologies;
• in general there was only a small quantity of aligned concepts by the matchers
(column # in Table 4);
• there were many incorrect correspondences (in particular for DL-SUMO), many
string matching cases which are usually safe in same domain correspondences do
not has the same impact here;
• there is a lack of comprehensive evaluation data sets to evaluate this task, and
the reference alignments used in the experiments reported in this paper should be
further extended in order to be exploited in OAEI evaluation campaigns;
• knowledge on foundational ontologies is highly specialized, it is crucial that such
evaluation considers an overview of experts in this area;
• matching strategies for dealing with this task should consider a variety of input,
such as structural features of the ontologies, background knowledge from external
resources targeting subsumption correspondences, and logical reasoning techni-
ques for guarantee the consistency of the generated alignments;
• at last, but not least, current tools do not distinguish between subsumption and
equivalence correspondences, which in this kind of task is an essential point.
6. Conclusions and perspectives
This paper presented an analysis of the alignments between four top-level ontologies,
BFO, DOLCE, GFO and SUMO. Our goal was to analyse the behaviour of current state
of the art tools, which apply diverse matching techniques, with respect to this task. We
could observe that matching top-level ontologies automatically is a challenging task, in
particular when involving subsumption relations.
Overall, the results found here are in line to what has been reported when
evaluating the behaviour of matchers in the task of matching domain and founda-
tional ontologies, which would also require identification of subsumption relations
[Schmidt et al. 2016a]. Current tools fail on correctly capturing the semantics behind
the ontological concepts, what requires deeper contextualization of the concepts on the
basis of their hierarchy and axioms.
Besides that, the task requires the identification of other relations than equivalen-
ces, such as subsumption and meronym. The latter is largely neglected by current mat-
chers. Most of them typically still rely on string-based techniques as an initial estimate
of the likelihood that two elements refer to the same real world phenomenon, hence the
found correspondences represent equivalences with concepts that are equally or similarly
written. However, in many cases, this correspondence is wrong [Schmidt et al. 2016b].
Hence, matching systems need to be improved to better exploit the knowledge en-
coded in the ontologies, to include more abstract and philosophical semantic relations and
semiotic matching, to take advantage of structural features of the ontologies and axioms
in order to better compare their formal definitions, and to take advantage of background
knowledge from external resources targeting subsumption and other semantic relations.
These have to be combined with logical reasoning techniques for guarantee the consis-
tency of the generated alignments. The current approaches have to be hence revised to
better deal with the specificities of matching foundational ontologies.
While the automatic approaches have been mostly manually evaluated, with few
exceptions [Damova et al. 2010, Schmidt et al. 2018], systematically evaluations of mat-
ching systems have been so far dedicated to domain ontologies. Despite the variety of
tasks in the OAEI campaigns17 , the evaluation of matching involving foundational onto-
logies has not been addressed. Producing comprehensive evaluation data sets on which
matching solutions can be evaluated would foster the development of approaches invol-
ving foundational ontologies and support a next generation of semantic matching appro-
aches.
Another aspect refers to the evolution or the consistency of alignments with res-
pect to the evolution or the different variants of the ontologies. Evolving alignments to
cope with the different versions of the ontologies is still an open challenge.
Last, but not least, very few foundational ontologies are equipped with lexical
layers in other natural languages than English (e.g., BFO has been enriched with a lexical
annotation in Portuguese). However, with the increasing amount of multilingual data on
the Web and the consequent development of ontologies in different natural languages,
foundational ontologies should also be equipped with richer multilingual annotations in
order to facilitate the multilingual and cross-lingual ontology matching tasks.
As future work, we plan to work on an approach that makes use of the knowledge
encoded in the ontologies, using hypernym relation extraction strategies such as lexico-
synthactic patterns. For instance, when applied on the definition below, the hypernym
relations (Self Connected Object, planet), (Self Connected Object, star), (Self Connected
Object, asteroid) can be identified through such patterns.
The Class of all astronomical objects of significant
size. It includes Self Connected Objects like planets, stars, and
asteroids ...
17
http://oaei.ontologymatching.org/2018/
We also plan to involve evaluators with expertise in foundational ontologies to
extend the alignments used here as reference; and to propose an OAEI task in order to
promote the development of matchers able to deal with the task, within an interactive
matching process.
Acknowledgments
We warmly thank D. Oberle for sending us all the generated alignments between SUMO
and DOLCE-Lite.
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