1. September 2017

2. Source: LOD-Cloud (http://lod-cloud.net/ )
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Disk
In-memory
Iteration 1 Iteration 2 Iteration n
Intermediate
Dataset
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Intermediate
Dataset
(in cluster
memory)
Output

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6. “Big Data” Processing (Spark/Flink) Semantic Technology Stack
Data Integration Manual pre-processing Partially automated,
standardised
Modelling Simple (often flat feature vectors) Expressive
Support for data
exchange
Limited (heterogeneous formats
with limited schema information)
Yes (RDF & OWL W3C
Standards)
Business value Direct Indirect
Horizontally
scalable
Yes No
Idea: combine advantages of both worlds

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val graph: TripleRDD = NTripleReader.load(spark, uri)
graph.find(ANY, URI("http://dbpedia.org/ontology/influenced"), ANY)
val rdf_stats_prop_dist = PropertyUsage(graph, spark).PostProc()

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val rdd = ManchesterSyntaxOWLAxiomsRDDBuilder.build(spark, "file.owl")
// get all subclass-of axioms
val sco = rdd.filter(_.isInstanceOf[OWLSubClassOfAxiom])

13. val graphRdd = NTripleReader.load(spark,input)
val partitions = RdfPartitionUtilsSpark.partitionGraph(graphRdd)
val rewriter = SparqlifyUtils.createSparqlSqlRewriter(spark, partitions)
val qef = new QueryExecutionFactorySparqlifySpark(spark, rewriter)
SANSA Engine
RDF Layer
Data Ingestion
Partitioning
Query Layer
Sparqlifying
Distributed Data
Structures
ResultsViews Views

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16. val graph = RDFGraphLoader.loadFromDisk(spark, uri)
val reasoner = new ForwardRuleReasonerOWLHorst(spark.sparkContext)
val inferredGraph = reasoner.apply(graph)
RDFGraphWriter.writeToDisk(inferredGraph, output)
RDFS rule
dependency graph
(simplified)

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20. Visit our demo at 6pm!

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23. Web: http://sansa-stack.net
Twitter: @SANSA_Stack
Github: https://github.com/SANSA-Stack
Mail: sansa-stack@googlemail.com

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