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Chang Liu 1, Guilin Qi 2 1 Shanghai Jiao Tong University 2 Southeast University, China.

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Presentation on theme: "Chang Liu 1, Guilin Qi 2 1 Shanghai Jiao Tong University 2 Southeast University, China."— Presentation transcript:

1 Chang Liu 1, Guilin Qi 2 1 Shanghai Jiao Tong University 2 Southeast University, China

2 Motivation More interests to represent additional information on top of RDF Time, uncertainty, trust, and provenance => Annotated RDF Large amount of data YAGO2 Problem: Large Scale Reasoning

3 Motivation (cont’d) Recent work on scalable reasoning using MapReduce WebPIE (ISWC ‘09, ESWC ‘10) Fuzzy pD* (ISWC ‘11) Our idea Large scale annoated RDF reasoner using MapReduce

4 Background: Annotated RDF

5 Background: MapReduce

6 Naïve Implementation Mapper Reducer KeyValue P 1XY 2Q

7 Challenges and solutions

8 Challenges and solutions (cont’d)

9 … “Shortest” path

10 Experiment setup Dataset Fuzzified DBPedia core ontology fpdLUBM 1000, 2000, 4000, 8000 Cluster 25 machine with 75 mapper/reducer slots Liu et al.: Reasoning with Large Scale Ontologies in Fuzzy pD* Using MapReduce. Computational Intelligence Magazine, IEEE 7(2), (2012)

11 Experiment result - fuzzy DBPedia #units Time(sec.) Speedup Dataset: fuzzified DBPedia core ontology Results:

12 Experiment result – fpdLUBM Number of UniversitiesTime of FuzzyPD (minutes) Time of WebPIE (minutes) Experimental results of FuzzyPD and WebPIE

13 Experiment result– fpdLUBM (cont’d) Number of unitsTime(minutes)Speedup Scalability over number of units

14 Experiment result– fpdLUBM (cont’d) Scalability over number of units

15 Experiment result– fpdLUBM (cont’d) Number of universities Input (Mtriples) Output (Mtriples) Time (minutes) Throughput (Ktriples/secon d) Scalability over data volume

16 Conclusion and Future work We show how to design MapReduce algorithms to achieve scalable annotated RDFS reasoning Several challenges along with solutions Future work More experiments on annotated RDFS ontologies Annotated OWL 2 RL

17 Q&A


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