Ontology Module Extraction via Datalog Reasoning

Authors: Ana Armas Romero, Mark Kaminski, Bernardo Cuenca Grau, Ian Horrocks

AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental An evaluation on widely-used ontologies has shown very encouraging results.
Researcher Affiliation Academia Ana Armas Romero, Mark Kaminski, Bernardo Cuenca Grau and Ian Horrocks Department of Computer Science, University of Oxford, UK
Pseudocode No No structured pseudocode or algorithm blocks were found.
Open Source Code No The paper does not provide an explicit statement or link for the open-sourcing of the code for the described methodology.
Open Datasets Yes We have evaluated our system on representative ontologies, including SNOMED (SCT), Fly Anatomy (FLY), the Gene Ontology (GO) and Bio Models (BM). The ontologies used in our tests are available for download at http://www.cs.ox.ac.uk/isg/ontologies/UID/ under IDs 794 (FLY), 795 (SCT), 796 (GO) and 797 (BM).
Dataset Splits No The paper does not provide specific dataset split information (e.g., exact percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning.
Hardware Specification Yes All experiments have been performed on a server with two Intel Xeon E5-2643 processors and 90GB of allocated RAM, running RDFox on 16 threads.
Software Dependencies No The paper mentions using 'RDFox datalog engine' and 'PAGOd A' but does not provide specific version numbers for these software components.
Experiment Setup Yes The choice of substitution θ and the ABoxes A0 and Ar, captured by the 'module setting' χ (Definition 1), determine the extracted module. Specific settings like χi (Definition 2), χf (Definition 4), χq (Definition 6), χm (Definition 8), χb (Definition 11), and χc (Definition 12) define the concrete configuration for different inseparability relations.