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, We present the participation of LogMap and its variants in the OAEI 2018 campaign. The LogMap project started in January 2011 with the objective of developing a scalable and logic-based ontology matching system. This is our eight participation in the OAEI and the experience has so far been very positive

, Presentation of the system

. Logmap, 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

, 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

, 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

, 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

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