Evolutionary and Swarm Computing for scaling up the Semantic Web


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There are the slides from my presentation at BNAIC2012. The talks is about why we need to look at optimization techniques to deal with Linked Data and how this can be done.

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Evolutionary and Swarm Computing for scaling up the Semantic Web

  1. 1. Evolutionary and Swarm Computing for scaling up the Semantic WebChristophe Guéret (@cgueret), Stefan Schlobach, Kathrin Dentler, Martijn Schut, and Gusz Eiben 24th Benelux Conference on Artificial Intelligence Maastricht University, October 25-26, 2012 1/18
  2. 2. What are we going to talk about? Linked Data Changing our point of view onsoundness and completeness Consider optimisation as analternative to logical deduction Short paper Two concrete examples of re- based on this publicationformulated problems 2/18
  3. 3. When solutions do not (quite) fit the problem ... 3/18 Copyright: sfllaw (Flickr, image 222795669)
  4. 4. Linked DataGraph/facts based knowledge representation toolConnect resources to properties / other resourcesWeb-based: resources have a URI Try http://dbpedia.org/resource/Amsterdam 4/18
  5. 5. Interacting with Linked Data Common goals Completeness: all the answers Soundness: only exact answers 5/18
  6. 6. Motivation In the context of Web data ? Issues with scale Issues with lack of consistency Issues with contextualised views over the World Revise the goals As many answers as possible (or needed) Answers as accurate as possible (or needed) 6/18
  7. 7. From logic to optimisation Optimise towards the revised goals Need methods that cope with uncertainty, context,noise, scale, ... 7/18
  8. 8. Answering queries over the data 8/18 Copyright: jepoirrier (Flickr, image 829293711)
  9. 9. The problemMatch a graph pattern to the dataMost common approach Join partial results for each edge of the query 9/18
  10. 10. Solving approachesLogic-based Find all the answers matching all of the query patternOptimisation Find answers matching as much of the query as possibleImportant implications of the optimisation Only some of the answers will be found Some of the answers found will be partially true 10/18
  11. 11. An optimisation approach: eRDFGuess the answers to the queryEvolutionary algorithm Evaluate validity of candidate solution Optimise with a recombination + local search 11/18
  12. 12. Some results Tested on queries withvaried complexity Works best with morecomplex queries Find exact answerswhen there are some 12/18
  13. 13. Finding implicit facts in the data Copyright: 13/18 givingnot@rocketmail.com (Flickr, image 6990161491)
  14. 14. The problemDeduce new facts from othersMost common approach Centralise all the facts, batch process deductions 14/18
  15. 15. Solving approachesLogic-based Find all the facts that can be derived from the dataOptimisation Find as many facts as possible while preserving consistencyImportant implications of the optimisation Only some of the facts will be found Unstable content 15/18
  16. 16. An optimisation approach: SwarmsSwarm of micro-reasoners Browse the graph, applying rules when possible Deduced facts disappear after some time Every author of a paper is a person Every person is also an agent 16/18
  17. 17. Some results If they stay, most ofthe implicit facts arederived Ants need to followeach other to deal withprecedence of rules Several ants per ruleare needed 17/18
  18. 18. Take home message Logic problems can be turned into optimisationproblems Trade off Gained: scalability, speed, robustness Lost: determinism, completeness, soundness A lot of research still to be done! (and done quickly, Linked Data is growing fast...) 18/18