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SEMLIB Final Conference | DERI presentation

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SEMLIB Final Conference | DERI presentation

  1. 1. The SemLib Linked Data Recommendation Engine
  2. 2. Our participation — and motivation — in the project involved the research & development of a recommendation engine that... ● leveraged the ubiquitousness and richness of linked data from the Web of Data ● would produce new linked data as a result of those recommendations. In addition, this would provide data interlinking
  3. 3. In general, we were concerned with... ●How to perform recommendation computations with the linked data? Furthermore, how to do this scalably? ● How to input linked data into such a system? ●How to output linked data from those recommendations?
  4. 4. For recommendation types, we focused on implementing the primary types: Collaborative filtering & Content-based With an array of algorithms including — Cosine Similarity, Pearson Correlation, Jaccard Distance, Co-occurrence, etc. ●An initial direction for the computation of recommendations ✓ Challenge: adapting these algorithms for linked ● data ✓
  5. 5. SPARQL for the Input of Linked Data SELECT ?s ?nationality ?influences WHERE { ?s dbpedia-ontology:occupation dbpedia- resource:Poet. ?s dbpedia-property:influences ?influences. ?s dbpedia-ontology:nationality ?nationality. } ●Declarative and expressive method for data materialisation ✓ ●SPARQL endpoint communication ✓
  6. 6. Output computed recommendations as linked RDF data. ⟨http://www.grouplens.org/user/1⟩ semlibproject:hasRecommendation _:node175. _:node175 ⟨semlibproject:recommends⟩ ⟨http://www.grouplens.org/movie/2858⟩. _:node175 ⟨semlibproject:hasScore⟩ 240.0. RDF creation and interlinking ✓ ●
  7. 7. Sometimes... Linked data → Big data Therefore, we went in the direction of a distributed and parallel framework — MapReduce
  8. 8. Overview of Results ● SPARQL execution, RDF materialisation and output → design the system using established tools and libraries ● The adaptation of the recommendation algorithms for RDF → formalisations presented in a paper [ECAI 2012] ● Scalability with the possibly large amount of data that can be input → a parallel and distributed framework
  9. 9. Implementation Our Framework SPARQL Query Extraction/Communication Machine Learning/Recommendation Algorithms As well as other technologies and libraries
  10. 10. Deployment and Use To get SLDR running, a JSP web server, such as Tomcat or Jetty is required. SLDR is deployed as a web application (WAR). From there, the recommendation engine is fully accessible from your web browser to start creating and running jobs.
  11. 11. The Recommendation Job Control Panel Saved Jobs Active Status Output
  12. 12. A Recommendation Job Algorithm Selection SPARQL Endpoints Query Configuration
  13. 13. The Backend System Workflow
  14. 14. Retrieving Recommendations Users have the option of viewing computed recommendations through either SPARQL and the output triplestore or through a REST API implemented into the systems backend. The REST API can be utilised for better integration into already existing systems (e.g. HTML, JavaScript, etc.)
  15. 15. Summary ● Ongoing improvement and development ● Have tested sucessfully with some of the SME's ● More information available at http://sldr.deri.ie

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