Discovering Meaningful Connections between Resources in the Web of Data

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Slides of LDOW2013 presentation, May 14th, Rio De Janeiro, Brazil

We will show that semantically annotated paths lead to discovering meaningful, non-trivial relations and connections between multiple resources in large online datasets such as the Web of Data. Graph algorithms have always been key in path finding applications (e.g., navigation systems). They make optimal use of available computation resources to fi nd paths in structured data. Applying these algorithms to Linked Data can facilitate the resolving of complex queries that involve the semantics of the relations between resources. In this paper, we introduce a new approach for fi nding paths in Linked Data that takes into account the meaning of the connections and also deals with scalability. An efficient technique combining pre-processing and indexing of datasets is used for finding paths between two resources in largedatasets within a couple of seconds. To demonstrate our approach, we have implemented a testcase using the DBpedia dataset.

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Discovering Meaningful Connections between Resources in the Web of Data

  1. 1. ELIS – Multimedia LabDiscovering Meaningful Connections betweenResources in the Web of DataEverything is connected: behind the scenesLaurens De VochtSam Coppens, Miel Van der Sande, Ruben Verborgh, Erik Mannens, Rik Van de Walle
  2. 2. ELIS – Multimedia LabBarack Obama?Paris Barack ObamaBertrandDelanoëCatholicChurchJoe Bidenmayor religion religion of vicepresident ofQueryResultParisEverything is Connected
  3. 3. ELIS – Multimedia LabReourceDescriptionResourceLookupPathfindingResourceDescriptionEverything is Connected
  4. 4. ELIS – Multimedia Lab1. Pathfinding2. Optimizations3. Performance Evaluation4. Future Work & DiscussionOverview
  5. 5. ELIS – Multimedia Lab1. Pathfinding2. Optimizations3. Performance Evaluation4. Future Work & DiscussionOverview
  6. 6. ELIS – Multimedia LabPathfindingUse of A* AlgorithmRequires:Adjacency MatrixWeighted linksHeuristic
  7. 7. ELIS – Multimedia LabPre-processingRDF Linked Data (NQ)<http://dbpedia.org/resource/Aristotle><http://xmlns.com/foaf/0.1/name>"Aristotle"@en<http://en.wikipedia.org/wiki/Aristotle?oldid=494147695#absolute-line=10> .…INCOMING TRIPLES...(Triples pointing to subject)METADATAhttp://dbpedia.org/resource/Aristotle(subject)http://en.wikipedia.org/wiki/Aristotle?oldid=494147695#absolute-line=10(context)OUTGOING TRIPLES<http://dbpedia.org/resource/Aristotle><http://xmlns.com/foaf/0.1/name>"Aristotle"@en...(Triples subjects points to)
  8. 8. ELIS – Multimedia LabPre-processingSIREnIndexing
  9. 9. ELIS – Multimedia LabPathfindingUse of A* AlgorithmRequires: Adjacency MatrixWeighted linksHeuristic
  10. 10. ELIS – Multimedia LabAdjacency Matrix InitialisationParis Barack ObamaBetrand DelanoëFrance...Joe Biden...United Statesglobal set of allresources increasesevery iteration
  11. 11. ELIS – Multimedia LabAdjacency Matrix InitialisationList indices correspond with row/column numbers in adjacency matrixGeneration of list of all resources
  12. 12. ELIS – Multimedia LabPathfindingUse of A* AlgorithmRequires: Adjacency Matrix Weighted linksHeuristic
  13. 13. ELIS – Multimedia LabWeighted LinksWeight encourage rare nodes in paths (Moore et al.)
  14. 14. ELIS – Multimedia LabPathfindingUse of A* AlgorithmRequires: Adjacency Matrix Weighted links Heuristic
  15. 15. ELIS – Multimedia LabHeuristicHeuristic: Jaccard distancemeasures dissimilarity between sample setsFranceParis:monument:mayor:language :capital
  16. 16. ELIS – Multimedia Lab1. Pathfinding2. Optimizations3. Performance Evaluation4. Future Work & DiscussionOverview
  17. 17. ELIS – Multimedia LabNode centrality based rank reductionBlacklisting irrelevant linksOptimizations
  18. 18. ELIS – Multimedia Lab1. Pathfinding2. Optimizations3. Performance Evaluation4. Future Work & DiscussionOverview
  19. 19. ELIS – Multimedia LabHITRATECOMPLEXITYEXECUTION TIMEPerformance Evaluation
  20. 20. ELIS – Multimedia LabHitrate above 90%Testset of 10 000 pathsamong 200 popular cities, artists and countriesin DBPedia (10M entities)
  21. 21. ELIS – Multimedia LabExponential space complexity
  22. 22. ELIS – Multimedia LabLinear time complexity
  23. 23. ELIS – Multimedia LabOver 60% of paths found in less than 2000ms
  24. 24. ELIS – Multimedia Lab1. Pathfinding2. Optimizations3. Performance Evaluation4. Future Work & DiscussionOverview
  25. 25. ELIS – Multimedia LabSynchronisationIndex <> Source Repositories/EndpointsPerformanceFurther improve iterative reduction(selection of potentially relevant entities)PersonalizationAdapt blacklist, heuristic and link weights to user preference and contextFuture Work & Discussion
  26. 26. ELIS – Multimedia LabOptimized pathfinding forlinked data to obtainmeaningful results.Results within a tolerable timefor users.Conclusionshttp://pathfinding.restdesc.orghttp://www.everythingisconnected.be@laurens_d_v #mmlablaurens.devocht@ugent.behttp://slideshare.net/laurensdvhttp://semweb.mmlab.be/Contact

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