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SEMLIB Final Conference | DERI presentation
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SEMLIB Final Conference | DERI presentation

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SLRD, the SEMLIB Linked Data Recommendation Engine

SLRD, the SEMLIB Linked Data Recommendation Engine

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SEMLIB Final Conference | DERI presentation SEMLIB Final Conference | DERI presentation Presentation Transcript

  • The SemLib Linked Data RecommendationEngine
  • 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
  • In general, we were concerned with...●How to perform recommendation computationswith the linked data? Furthermore, how to do thisscalably?● How to input linked data into such a system?●How to output linked data from thoserecommendations?
  • For recommendation types, we focused onimplementing 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 ofrecommendations ✓Challenge: adapting these algorithms for linked●data ✓
  • 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 ✓
  • 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 ✓ ●
  • Sometimes...Linked data → Big data Therefore, we went in the direction of a distributed and parallel framework — MapReduce
  • 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
  • ImplementationOur FrameworkSPARQL Query Extraction/CommunicationMachine Learning/Recommendation AlgorithmsAs well as other technologies and libraries
  • Deployment and UseTo 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.
  • The Recommendation Job Control Panel Saved Jobs Active Status Output
  • A Recommendation Job Algorithm SelectionSPARQL Endpoints Query Configuration
  • The Backend System Workflow
  • Retrieving RecommendationsUsers 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.)
  • Summary● Ongoing improvement and development● Have tested sucessfully with some of the SMEs● More information available at http://sldr.deri.ie