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Release webinar: Sansa and Ontario

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Jens Lehmann's overview of the use of semantics in the Big Data Europe Integrator Platform. Including the Semantic Data Lake (Ontario), and the SANSA Analytics Engine.

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Release webinar: Sansa and Ontario

  1. 1. Distributed Knowledge Graph Processing in SANSA and Ontario Prof. Dr. Jens Lehmann Big Data Europe Platform ReleaseMay 3rd 2017
  2. 2. SANSA: Motivation ◎ Abundant machine readable structured information is available (e.g. in RDF) o Across BDE Societal Challenges, e.g. for Life Science Data o General: DBpedia, Google knowledge graph o Social graphs: Facebook, Twitter ◎ Need for scalable querying, inference and machine learning o Link prediction o Knowledge base completion o Predictive analytics o Forward chaining inference 2
  3. 3. SANSA Stack 4 ◎ SANSA includes several libraries: o Read / Write RDF / OWL library o Querying library o Inference library o ML- Machine Learning core library http://sansa-stack.net/ ◎ Framework for distributed RDF dataset processing aiming at high scalability and fault tolerance
  4. 4. SANSA: Read Write Layer 5
  5. 5. SANSA: Read Write Layer ◎ Ingest RDF and OWL data in different formats using Jena / OWL API style interfaces ◎ Goal: Represent/distribute data in multiple formats (e.g. RDD, Data Frames, GraphX, Tensors) ◎ Goal: Allow transformation among these formats ◎ Compute dataset statistics and apply functions to URIs, literals, subjects, objects → Distributed LODStats 6
  6. 6. SANSA: Query Layer 7
  7. 7. SANSA: Query Layer ◎ Improve performance of generic queries via: o Intelligent indexing o Splitting strategies o Distributed Storage ◎ SPARQL-to-SQL approaches, Virtual Views ◎ Next Step: SPARQL query engine evaluation ◎ SANSA provides a W3C SPARQL compliant endpoint 8
  8. 8. SANSA: Inference Layer 9
  9. 9. SANSA: Inference Layer ◎ W3C Standards for Modelling: RDFS and OWL ◎ Parallel in-memory inference via rule-based forward chaining ◎ Beyond state of the art: dynamically build a rule dependency graph for a rule set ◎ → Adjustable performance levels 10
  10. 10. SANSA: ML Layer 11
  11. 11. SANSA: ML Layer ◎ Distributed Machine Learning (ML) algorithms that work on RDF data and make use of its structure / semantics ◎ Work in Progress: o Tensor Factorization for e.g. KB completion (testing stage) o Graph Clustering (testing stage) o Association rule mining (evaluation stage) o Semantic Decision trees (idea stage) o Inference in Knowledge Graph Embeddings (idea stage) 12
  12. 12. SANSA Status and Releases ◎ A generic stack for (big) Linked Data o Build on top of a state-of-the-art distributed frameworks (Spark, Flink) o Easy to use: just add the dependencies to your existing Spark or Flink project ◎ Out-of-the-box framework for (1) querying, (2) inference and (3) machine learning over RDF datasets ◎ 0.1 Release in December ◎ Subsequent releases every 6 months 13
  13. 13. Semantic Data Lake (Ontario) ◎ Data Lake or Swamp? o Repository of data in its original formats o Structured, semi-structured, unstructured o Without unified schema ◎ Semantic Data Lake o Add a Semantic Layer on top of the source datasets ❖ The data is semantically lifted using ontology terms ❖ Provide a uniform view over nonuniform data 14
  14. 14. Semantic Data Lake (Ontario) ◎ A SPARQL query is decomposed into sub-queries o Keeping links between sub-queries to collect data returned later from each sub-query ◎ Data Lake is checked for candidate data sources that can answer each sub-query (consulting metadata) ◎ Each SPARQL sub-query is translated to a query on the query language of the selected candidate data source o Examples: SPARQL to SQL, or SPARQL to CQL ◎ Sub-resultes are joined together to form the end result 15
  15. 15. Metadata property -> data source (type) Semantic Data Lake (Ontario) 16 Decomposing User QuerySPARQL query Database XML File ?item gho:Country ?country . ?item gho:Disease ?disease . ... SELECT country, disease, ... FROM Observations Finding Relevant Data Sources + Converting Queries SQL XPathSQL MongoDB JSON Path SQL XML MongoDB
  16. 16. Semantic Data Lake (Ontario) 17 Database XML File Results Reconciliation Execution Plan Links between sub-queries MongoDB Final Results
  17. 17. Big Data Europe Integrator Platform Launch Wednesday 3 May @ 15:00 CEST Please type your questions at any time. Q&A will follow the presentations
  18. 18. Backup Slides
  19. 19. SANSA: Motivation 20 ◎ Over the last years, the size of the Semantic Web has increased and several large-scale datasets were published. Source: LOD-Cloud (http://lod-cloud.net/ ) ◎ Now days hadoop ecosystem has become a standard for BigData applications. ◎ We use this infrastructure for Semantic Web as well.
  20. 20. Thank You
  21. 21. Thank You @Gezim_Sejdiu http://sda.cs.uni-bonn.de/gezim-sejdiu/ SANSA Semantic Analytics Stack https://github.com/SANSA-Stack ● SANSA-RDF ● SANSA-OWL ● SANSA-Query ● SANSA-Inference ● SANSA-ML ● SANSA-Examples
  22. 22. SANSA Planning ◎ Current state: o SANSA 0.1 released in December o Can read RDF/OWL files, compute statistics, simple queries, lightweight inference, graph clustering, rule mining ◎ Subsequent releases every 6 months o SANSA 0.2 release planned in June 2017. 23
  23. 23. Conclusions and Next steps ◎ A generic stack for (big) Linked Data o Build on top of a state-of-the-art distributed frameworks (Spark, Flink) ◎ Out-of-the-box framework for scalable and distributed semantic data analysis combining semantic web and distributed machine learning for (1) querying, (2) inference and (3) analytics of RDF datasets. ◎ Next steps o Refinement of data structures (RDF/OWL Layer) o Add support for SPARQL 1.1 and other backend strategies (Query Layer) o Define a ML pipelines for Structured ML (ML Layer). 24
  24. 24. BDE & General Integration ◎ SANSA = Scala / Maven Repositories based on Spark / Flink ◎ Easy to include both in BDE platform and any Spark / Flink environment 25

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