=Paper=
{{Paper
|id=Vol-1545/oaei15_paper10
|storemode=property
|title=LogMap family results for OAEI 2015
|pdfUrl=https://ceur-ws.org/Vol-1545/oaei15_paper10.pdf
|volume=Vol-1545
|dblpUrl=https://dblp.org/rec/conf/semweb/Jimenez-RuizGSC15
}}
==LogMap family results for OAEI 2015==
https://ceur-ws.org/Vol-1545/oaei15_paper10.pdf
LogMap family results for OAEI 2015
E. Jiménez-Ruiz1 , B. Cuenca Grau1 , A. Solimando2 , and V. Cross3
1
Department of Computer Science, University of Oxford, Oxford UK
2
Inria Saclay and Université Paris-Sud, France
3
Computer Science and Software Engineering, Miami University, Oxford, OH, United States
Abstract. We present the results obtained in the OAEI 2015 campaign by our
ontology matching system LogMap and its variants: LogMapC, LogMapBio and
LogMapLt. The LogMap project started in January 2011 with the objective of de-
veloping a scalable and logic-based ontology matching system. This is our sixth
participation in the OAEI and the experience has so far been very positive. Cur-
rently, LogMap is the only system that participates in all OAEI tasks.
1 Presentation of the system
Ontology matching systems typically rely on lexical and structural heuristics and the
integration of the input ontologies and the mappings may lead to many undesired log-
ical consequences. In [12] three principles were proposed to minimize the number of
potentially unintended consequences, namely: (i) consistency principle, the mappings
should not lead to unsatisfiable classes in the integrated ontology; (ii) locality principle,
the mappings should link entities that have similar neighbourhoods; (iii) conservativ-
ity principle, the mappings should not introduce alterations in the classification of the
input ontologies. Violations to these principles may hinder the usefulness of ontology
mappings. The practical effect of these violations, however, is clearly evident when
ontology alignments are involved in complex tasks such as query answering [19].
LogMap [11, 13] is a highly scalable ontology matching system that implements the
consistency and locality principles. LogMap also supports (real-time) user interaction
during the matching process, which is essential for use cases requiring very accurate
mappings. LogMap is one of the few ontology matching system that (i) can efficiently
match semantically rich ontologies containing tens (and even hundreds) of thousands of
classes, (ii) incorporates sophisticatedhttp://iswc2015.semanticweb.org/ reasoning and
repair techniques to minimise the number of logical inconsistencies, and (iii) provides
support for user intervention during the matching process.
LogMap relies on the following elements, which are keys to its favourable scalabil-
ity behaviour (see [11, 13] for details).
Lexical indexation. An inverted index is used to store the lexical information contained
in the input ontologies. This index is the key to efficiently computing an initial set of
mappings of manageable size. Similar indexes have been successfully used in informa-
tion retrieval and search engine technologies [2].
Logic-based module extraction. The practical feasibility of unsatisfiability detection
and repair critically depends on the size of the input ontologies. To reduce the size of
the problem, we exploit ontology modularisation techniques. Ontology modules with
well-understood semantic properties can be efficiently computed and are typically much
smaller than the input ontology (e.g. [5]).
Propositional Horn reasoning. The relevant modules in the input ontologies together
with (a subset of) the candidate mappings are encoded in LogMap using a Horn propo-
sitional representation. Furthermore, LogMap implements the classic Dowling-Gallier
algorithm for propositional Horn satisfiability [6]. Such encoding, although incomplete,
allows LogMap to detect unsatisfiable classes soundly and efficiently.
Axiom tracking. LogMap extends Dowling-Gallier’s algorithm to track all mappings
that may be involved in the unsatisfiability of a class. This extension is key to imple-
menting a highly scalable repair algorithm.
Local repair. LogMap performs a greedy local repair; that is, it repairs unsatisfiabilities
on-the-fly and only looks for the first available repair plan.
Semantic indexation. The Horn propositional representation of the ontology modules
and the mappings is efficiently indexed using an interval labelling schema [1] — an
optimised data structure for storing directed acyclic graphs (DAGs) that significantly
reduces the cost of answering taxonomic queries [4, 20]. In particular, this semantic
index allows us to answer many entailment queries as an index lookup operation over
the input ontologies and the mappings computed thus far, and hence without the need
for reasoning. The semantic index complements the use of the propositional encoding
to detect and repair unsatisfiable classes.
1.1 LogMap variants in the 2015 campaign
As in the 2014, in the 2015 campaign we have participated with 3 variants:
LogMapLt is a “lightweight” variant of LogMap, which essentially only applies (effi-
cient) string matching techniques.
LogMapC is a variant of LogMap which, in addition to the consistency and locality
principles, also implements the conservativity principle (see details in [21, 22]).
The repair algorithm is more aggressive than in LogMap, thus we expect highly
precise mappings but with a significant decrease in recall.
LogMapBio includes an extension to use BioPortal [8, 9] as a (dynamic) provider of
mediating ontologies instead of relying on a few preselected ontologies [3].
1.2 Adaptations made for the 2015 evaluation
LogMap’s algorithm described in [11, 13, 14] has been adapted with the following new
functionalities:
i Local repair with global information. We have extended LogMap to include global
information in the local repairs, that is, repair plans of the same size are ordered ac-
cording to their degree of conflictness (i.e. number of cases where the mappings in
the repair are involved in an unsatisfiability). Hencee, LogMap prefers to remove
mappings that are more likely to lead to other unsatisfiabilities.
ii Extended multilingual support. We have extended our multilingual module to
use both google translate and microsoft translator.4 Additionally, in order to split
Chinese words, we rely on the ICTCLAS library5 developed by the Institute of
Computing Technology of the Chinese Academy of Sciences.
iii Extended instance matching support. We have also adapted LogMap’s instance
matching module to cope with the new OAEI 2014 tasks.
iv BioPortal module. In the OAEI 2015, LogMapBio uses the top-10 mediating (the
2014 version used only the top-5) ontologies given by the algorithm presented in
[3]. Note that, LogMapBio only participates in the biomedical tracks. In the other
tracks the results are expected to be the same as LogMap.
1.3 Link to the system and parameters file
LogMap is open-source and released under GNU Lesser General Public License 3.0.6
LogMap components and source code are available from the LogMap’s GitHub page:
https://github.com/ernestojimenezruiz/logmap-matcher/.
LogMap distributions can be easily customized through a configuration file contain-
ing the matching parameters.
LogMap, including support for interactive ontology matching, can also be used di-
rectly through an AJAX-based Web interface: http://csu6325.cs.ox.ac.uk/.
This interface has been very well received by the community since it was deployed in
2012. More than 2,000 requests coming from a broad range of users have been pro-
cessed so far.
1.4 Modular support for mapping repair
Only a very few systems participating in the OAEI competition implement repair tech-
niques. As a result, existing matching systems (even those that typically achieve very
high precision scores) compute mappings that lead in many cases to a large number of
unsatisfiable classes.
We believe that these systems could significantly improve their output if they were
to implement repair techniques similar to those available in LogMap. Therefore, with
the goal of providing a useful service to the community, we have made LogMap’s ontol-
ogy repair module (LogMap-Repair) available as a self-contained software component
that can be seamlessly integrated in most existing ontology matching systems [16, 7].
2 Results
Please refer to http://oaei.ontologymatching.org/2015/results/index.
html for the results of the LogMap family in the OAEI 2015 campaign.
4
Currently we rely on the (unofficial) APIs available at https://code.google.
com/p/google-api-translate-java/ and https://code.google.com/p/
microsoft-translator-java-api/
5
https://code.google.com/p/ictclas4j/
6
http://www.gnu.org/licenses/
3 General comments and conclusions
3.1 Comments on the results
LogMap has been one of the top systems in the OAEI 2015 and the only system that
participates in all tracks. Furthermore, it has also been one of the few systems imple-
menting repair techniques and providing (almost) coherent mappings in all tracks.
LogMap’s main weakness is that the computation of candidate mappings is based
on the similarities between the vocabularies of the input ontologies; hence, in the cases
where the ontologies are lexically disparate or do not provide enough lexical informa-
tion LogMap is at a disadvantage.
3.2 Discussions on the way to improve the proposed system
LogMap is now a stable and mature system that has been made available to the commu-
nity and has been extensively tested. There are, however, many exciting possibilities for
future work. For example we aim at improving the current multilingual features and the
current use of external resources like BioPortal. Furthremore, we are applying LogMap
in practice in the domain of oil and gas industry within the FP7 Optique7 [18, 15, 10,
17]. This practical application presents a very challenging problem.
Acknowledgements
This work was supported by the EPSRC projects MaSI3 , Score! and DBOnto, and by
the EU FP7 project Optique (grant agreement 318338).
We would also like to thank Ian Horrocks, Anton Morant, Yujiao Zhou Weiguo Xia,
Xi Chen, Yuan Gong and Shuo Zhang, who have contributed to the LogMap project in
the past.
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