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DistEL: A Distributed Ontology Classifier Raghava - - PowerPoint PPT Presentation

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DistEL: A Distributed Ontology Classifier Raghava Mutharaju Pascal Hitzler Prabhaker Mateti Department of Computer Science Wright State University, Dayton, OH, USA October 20 13 SSWS20 13 @ ISWC20 13, Sydney, Australia

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October 20 13 – SSWS20 13 @ ISWC20 13, Sydney, Australia – Pascal Hitzler

DistEL: A Distributed Ontology Classifier

Raghava Mutharaju Pascal Hitzler Prabhaker Mateti Department of Computer Science Wright State University, Dayton, OH, USA

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Setting

is a fragment of OWL EL e.g., SNOMED and tractable (polytime) We investigate distributed memory reasoning with . Distributed memory reasoning is generally very hard.

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Evaluation data

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Results: single memory reasoners

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Results: DistEL load times

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Results: DistEL reasoning times

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DistEL reasoning: small ontologies

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DistEL reasoning: large ontologies

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Results: DistEL reasoning times

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Results: DistEL reasoning times

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October 20 13 – SSWS20 13 @ ISWC20 13, Sydney, Australia – Pascal Hitzler

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Results: DistEL reasoning times

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October 20 13 – SSWS20 13 @ ISWC20 13, Sydney, Australia – Pascal Hitzler

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Results: DistEL reasoning times

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October 20 13 – SSWS20 13 @ ISWC20 13, Sydney, Australia – Pascal Hitzler

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Results: DistEL reasoning times

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Results: DistEL reasoning times

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Reasoning task: classification Compute all for named classes .

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Completion-rule-based algorithm

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Distribution

Node assignment to rules (rectangles) and I/O dependencies between

  • rules. Ovals are groups of

nodes processing the same rules.

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Implementation/Optimizations

  • Redis key-value store.
  • Choice of keys and values.
  • Data encoding (numerical identifiers)
  • Selective data duplication
  • Highly targeted communication (to relevant nodes
  • nly)
  • Etc.
  • Each node in our test cluster has two quad-core AMD

Opteron 2300 MHz processors with 16GB RAM.

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Future Work

  • Automated load balancing.
  • Automated assignment of rules to nodes.
  • Add ABox reasoning
  • Other rulesets
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Related Work

Very little on distributed memory OWL reasoning. Even less with convincing evaluations. But see e.g.

  • Urbani et al on WebPIE and QueryPIE.
  • Schlicht and Stuckenschmidt on distributed

resolution for description logics.

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Conclusions

  • This is the very first presentation of a distributed

memory reasoner with convincing evaluation regarding parallelization and control of communication overhead.

  • We seem to have significant scope for further
  • ptimizations.
  • Generalizability of the architecture remains to be

investigated.

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Thanks!

Thanks! Implementation-specific questions should best go to Raghava Mutharaju, mutharaju.2@wright.edu.

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References

  • Jacopo Urbani, Robert Piro, Frank van Harmelen, Henri Bal, Hybrid

Reasoning on OWL RL. Semantic Web journal, to appear.

  • Jacopo Urbani, Spyros Kotoulas, Jason Maassen, Frank van

Harmelen, Henri E. Bal: WebPIE: A Web-scale Parallel Inference Engine using MapReduce. J. Web Sem. 10: 59-75 (2012)

  • Anne Schlicht, Heiner Stuckenschmidt: Peer-to-Peer Reasoning for

Interlinked Ontologies. Int. J. Semantic Computing 4(1): 27-58 (2010)

  • Anne Schlicht, Heiner Stuckenschmidt: MapResolve. In: Proc. RR

2011: 294-299

  • Raghava Mutharaju, Frederick Maier, Pascal Hitzler, A MapReduce

Algorithm for EL+. In: Volker Haarslev, Davind Toman, Grant Weddell (eds.), In: Proc. DL2010, pp. 464-474.

  • Kathrin Dentler, Ronald Cornet, Annette ten Teije, Nicolette de

Keizer: Comparison of reasoners for large ontologies in the OWL 2 EL profile. Semantic Web 2(2): 71-87 (2011)

  • Franz Baader, Sebastian Brandt, Carsten Lutz: Pushing the EL
  • Envelope. In: Proc. IJCAI 2005: 364-369