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 developing 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. Currently, LogMap is the only system that participates in all OAEI tasks.
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. [
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
propositional representation. Furthermore, LogMap implements the classic Dowling-Gallier
algorithm for propositional Horn satisfiability [
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 [
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 (efficient) 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 [
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
according 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
[
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 containing the matching parameters.
LogMap, including support for interactive ontology matching, can also be used directly 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 processed so far. 1.4
Only a very few systems participating in the OAEI competition implement repair techniques. 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 ontology 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
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/
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 implementing 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 information LogMap is at a disadvantage. 3.2
LogMap is now a stable and mature system that has been made available to the community 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.
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. 7 http://www.optique-project.eu/ 7. Faria, D., Jime´nez-Ruiz, E., Pesquita, C., Santos, E., Couto, F.M.: Towards annotating potential incoherences in bioportal mappings. In: 13th Int’l Sem. Web Conf. (ISWC) (2014) 8. Fridman Noy, N., Shah, N.H., Whetzel, P.L., Dai, B., et al.: BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Research 37, 170–173 (2009) 9. Ghazvinian, A., Noy, N.F., Jonquet, C., Shah, N.H., Musen, M.A.: What four million mappings can tell you about two hundred ontologies. In: Int’l Sem. Web Conf. (ISWC) (2009) 10. Giese, M., Soylu, A., Vega-Gorgojo, G., Waaler, A., Haase, P., Jimenez-Ruiz, E., Lanti, D., Rezk, M., Xiao, G., Ozcep, O., Rosati, R.: Optique — Zooming In on Big Data Access.
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