We present the participation of LogMap and its variants in the OAEI 2017 campaign. The LogMap project started in January 2011 with the objective of developing a scalable and logic-based ontology matching system. This is our seventh participation in the OAEI and the experience has so far been very positive. LogMap is one of the few systems that participates in (almost) all OAEI tracks.
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 logical consequences. In [13] 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) conservativity 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 [22].
LogMap [12,14] 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 sophisticated 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 scalability behaviour (see [12,14] 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 information 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 propositional 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 implementing 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 [4,23]. 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.
As in previous campaigns, in the OAEI 2017 we have participated with two additional variants:
LogMapLt is a "lightweight" variant of LogMap, which essentially only applies (efficient) string matching techniques. 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].
In previous years we also participated with LogMapC4 .
LogMap's algorithm described in [12,14,16,15] has been adapted with the following new functionalities:
i Extended instance matching support. We have adapted LogMap's instance matching module to be more flexible and adaptable to new matching tasks. ii Overlapping estimation. We have also slighly improved the overlapping estimation module to reduce the search space. It now considers an extended set of labels necessary to apply the overlapping estimation in the new datasets of the Disease & Phenotype track [11].
iii Extended interactive support. The interactive algorithm makes now use of the functionalities of the SEALS client and allows to make several related questions in one go.
LogMap is open-source and released under GNU Lesser General Public License 3.0. 5LogMap 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://krrwebtools.cs.ox. ac.uk/. This interface has been very well received by the community since it was deployed in 2012. More than 2,800 requests coming from a broad range of users have been processed so far.
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 [18,7].
Please refer to http://oaei.ontologymatching.org/2017/results/ for the results of the LogMap family in the OAEI 2017 campaign.
LogMap has been one of the top systems in the OAEI 2017 and one of the few systems that participates in (almost) all tracks. 6 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.
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 [21,17,10,20]. This practical application presents a very challenging problem.
This work was partially funded by the BIGMED project (IKT 259055), the HealthInsight project (IKT 247784), and the SIRIUS Centre for Scalable Data Access (Research Council of Norway, project no.: 237889).
We would also like to thank Ian Horrocks, Alessandro Solimando, Anton Morant, Yujiao Zhou, Weiguo Xia, Xi Chen, Yuan Gong and Shuo Zhang, who have contributed to the LogMap project in the past.