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Discovering Meaningful Connections between Resources in the Web of Data

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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.

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 Lab Discovering Meaningful Connections between Resources in the Web of Data Everything is connected: behind the scenes Laurens De Vocht Sam Coppens, Miel Van der Sande, Ruben Verborgh, Erik Mannens, Rik Van de Walle
  2. 2. ELIS – Multimedia Lab Barack Obama ? Paris Barack ObamaBertrand Delanoë Catholic Church Joe Biden mayor religion religion of vicepresident of Query Result Paris Everything is Connected
  3. 3. ELIS – Multimedia Lab Reource Description Resource Lookup Pathfinding Resource Description Everything is Connected
  4. 4. ELIS – Multimedia Lab 1. Pathfinding 2. Optimizations 3. Performance Evaluation 4. Future Work & Discussion Overview
  5. 5. ELIS – Multimedia Lab 1. Pathfinding 2. Optimizations 3. Performance Evaluation 4. Future Work & Discussion Overview
  6. 6. ELIS – Multimedia Lab Pathfinding Use of A* Algorithm Requires: Adjacency Matrix Weighted links Heuristic
  7. 7. ELIS – Multimedia Lab Pre-processing RDF Linked Data (NQ) <http://dbpedia.org/resource/Aristotle> <http://xmlns.com/foaf/0.1/name> "Aristotle"@en <http://en.wikipedia.org/wiki/Aristotle?oldid=494 147695#absolute-line=10> . … INCOMING TRIPLES ... (Triples pointing to subject) METADATA http://dbpedia.org/resource/A ristotle (subject) http://en.wikipedia.org/wiki/Ar istotle?oldid=494147695#ab solute-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 Lab Pre-processing SIREn Indexing
  9. 9. ELIS – Multimedia Lab Pathfinding Use of A* Algorithm Requires:  Adjacency Matrix Weighted links Heuristic
  10. 10. ELIS – Multimedia Lab Adjacency Matrix Initialisation Paris Barack Obama Betrand Delanoë France ... Joe Biden ... United States global set of all resources increases every iteration
  11. 11. ELIS – Multimedia Lab Adjacency Matrix Initialisation List indices correspond with row/column numbers in adjacency matrix Generation of list of all resources
  12. 12. ELIS – Multimedia Lab Pathfinding Use of A* Algorithm Requires:  Adjacency Matrix  Weighted links Heuristic
  13. 13. ELIS – Multimedia Lab Weighted Links Weight encourage rare nodes in paths (Moore et al.)
  14. 14. ELIS – Multimedia Lab Pathfinding Use of A* Algorithm Requires:  Adjacency Matrix  Weighted links  Heuristic
  15. 15. ELIS – Multimedia Lab Heuristic Heuristic: Jaccard distance measures dissimilarity between sample sets FranceParis :monument :mayor :language :capital
  16. 16. ELIS – Multimedia Lab 1. Pathfinding 2. Optimizations 3. Performance Evaluation 4. Future Work & Discussion Overview
  17. 17. ELIS – Multimedia Lab Node centrality based rank reduction Blacklisting irrelevant links Optimizations
  18. 18. ELIS – Multimedia Lab 1. Pathfinding 2. Optimizations 3. Performance Evaluation 4. Future Work & Discussion Overview
  19. 19. ELIS – Multimedia Lab HITRATE COMPLEXITY EXECUTION TIME Performance Evaluation
  20. 20. ELIS – Multimedia Lab Hitrate above 90% Testset of 10 000 paths among 200 popular cities, artists and countries in DBPedia (10M entities)
  21. 21. ELIS – Multimedia Lab Exponential space complexity
  22. 22. ELIS – Multimedia Lab Linear time complexity
  23. 23. ELIS – Multimedia Lab Over 60% of paths found in less than 2000ms
  24. 24. ELIS – Multimedia Lab 1. Pathfinding 2. Optimizations 3. Performance Evaluation 4. Future Work & Discussion Overview
  25. 25. ELIS – Multimedia Lab Synchronisation Index <> Source Repositories/Endpoints Performance Further improve iterative reduction (selection of potentially relevant entities) Personalization Adapt blacklist, heuristic and link weights to user preference and context Future Work & Discussion
  26. 26. ELIS – Multimedia Lab Optimized pathfinding for linked data to obtain meaningful results. Results within a tolerable time for users. Conclusions http://pathfinding.restdesc.org http://www.everythingisconnected.be @laurens_d_v #mmlab laurens.devocht@ugent.be http://slideshare.net/laurensdv http://semweb.mmlab.be/ Contact

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