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... Satisfiability Caching with Nominals: Caching the satisfiability status of labels of (blockable) nodes in completion graphs is an important optimisation technique for tableau-based reasoning systems =-=[3,4]-=-. If one obtains a fully expanded and clash-free completion graph, then the contained node labels can be cached and, if identical labels occur in other completion graphs, then their expansion (i.e., t...

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...ones. As we can see in Figure 1, the ‘loading ontology’ and ‘materialisation’ steps are query independent. Therefore, both of them are counted as pre-processing steps. ‘Computing query bounds’, ‘extracting subset’ and ‘full reasoning’ are query dependent, and are called query processing steps. Loading ontology and data. PAGOdA uses the OWL API to parse the input ontology O. The dataset D is given separately in Turtle format. The normaliser then transforms the ontology into a set of rules corresponding to the axioms in O. PAGOdA’s normaliser is an extension of HermiT’s clausification component [5], which transforms axioms into so-called DL-clauses [11]. The dataset D is loaded directly into (the four instances of) RDFox. After normalisation, the ontology is checked to determine if it is inside OWL 2 RL (resp. ELHOr⊥); if so, then RDFox (resp. KARMA) is already sound and complete, and PAGOdA simply processes O, D and subsequent queries using the relevant component. Otherwise, PAGOdA uses a variant of shifting—a polynomial program transformation commonly used in Answer Set Programming [4]— to enrich the deterministic part of the ontology with some additional information from disjunctive ...

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...es Ji to be a justification for η, than Ri is considered buggy, else the justification generating reasoner. (4) If Ri infers Ji 6|= η and initially returned η ∈ E(O,Ri), then we consider this a serious error and class this as possible bug, as we are uncertain where a problem has occurred. Such cases also require expert review. In our experiment, this case did not occur. 3 Experimental Design Corpus, Reasoners and Machines: For the experiment the OWL API (v. 3.5.0) implementations [6] of four state of the art reasoners were used: FaCT++ 1.6.3 [9], JFact 1.2.3, Pellet 2.3.1 [7] and HermiT 1.3.8 [3]. Aside from the fact that these are popular, FaCT++ and JFact were also picked because of their similarity, in an attempt to see if that similarity would produce any change to the MV and the results from the method. Moreover, the number of reasoners were set to 4 to provide enough variation between reasoners, but also to include the possibility of inducing a draw of two teams between them. Our corpus is a snapshot of 339 BioPortal ontologies (January 2015). All experiments are performed on a desktop computer running 4 i5-3479 CPU at 3.20 GHz with a 64 bit Ubuntu 14.04. Identifying potential o...

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...em is depicted on Figure 1. The initialization phase is modified by introducing the Binary Ontology Indexing (BOI). 1.2.1 Initialization phase 1. Ontology Loading: ServOMBI following the ServOMap approach relies on IR techniques for ontology matching. Each ontology to process is seen as a corpus of semantic documents to process. Each entity of the ontology is a document in the sense of IR. It is therefore necessary to identify the useful descriptors for indexing entities. The loading step perform the task of generating documents from entities. ServOMBI uses different reasoners (ELK [9], Hermit[11]) according to the size of the ontology to process. 2. Metadata and Metrics Generation: This step reuse the component implement in the ServOMap system and and identify 4 categories of matching tasks that are used to classify the input ontologies that are being processed : entity matching task (small, medium, big) and instances matching task. 2 Figure 2: Example of binary indexing. 3. Terminological Indexing: Following a generic metamodel provided by the ServO OR, a terminological based inverted index is built from the documents generated during the loading step. ServOMBI introduces a tasks par...

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...le 5: Statistics of ontology metrics for the evaluated ontology repositories (Ø stands for average and Q0.5 for median) of the DLs SROIQ, namely FaCT++ 1.6.3 (Tsarkov & Horrocks, 2006), HermiT 1.3.8 (=-=Glimm, Horrocks, Motik, Stoilos, & Wang, 2014-=-), MORe 0.1.6 (Armas Romero et al., 2012), and Pellet 2.3.1 (Sirin, Parsia, Cuenca Grau, Kalyanpur, & Katz, 2007). The evaluation uses a large test corpus of ontologies,4 which has been obtained by co...

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...ny different combinations of systems are possible. This is also supported by the fact that we have already provided implementations in Hydrowl that combine the OWL 2 RL reasoner OWLim [1] with HermiT =-=[10]-=-, with HermiT-BGP [5], and with Rapid [11]. 2 Techniques Used in Hydrowl In the current section we briefly outline the techniques used in Hydrowl. As mentioned above, for a given OWL 2 DL ontology T ,...