Evolutionary and Swarm Computing for scaling up the Semantic Web
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The document discusses the challenges of scaling up the Semantic Web through evolutionary and swarm computing techniques. It emphasizes optimizing logical deduction to manage issues like data consistency and completeness, and presents two examples: an evolutionary approach using algorithms to guess answers and a swarm-based model for deducing facts. The conclusion highlights the trade-offs of applying optimization, including increased scalability and speed at the expense of determinism and completeness.
Evolutionary and Swarm Computing for scaling up the Semantic Web
- 1. Evolutionary and Swarm Computing for scaling up the Semantic Web Christophe 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. What are we going to talk about? Linked Data Changing our point of view on soundness and completeness Consider optimisation as an alternative to logical deduction Short paper Two concrete examples of re- based on this publication formulated problems 2/18
- 3. When solutions do not (quite) fit the problem ... 3/18 Copyright: sfllaw (Flickr, image 222795669)
- 4. Linked Data Graph/facts based knowledge representation tool Connect resources to properties / other resources Web-based: resources have a URI Try http://dbpedia.org/resource/Amsterdam 4/18
- 5. Interacting with Linked Data Common goals Completeness: all the answers Soundness: only exact answers 5/18
- 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. From logic to optimisation Optimise towards the revised goals Need methods that cope with uncertainty, context, noise, scale, ... 7/18
- 8. Answering queries over the data 8/18 Copyright: jepoirrier (Flickr, image 829293711)
- 9. The problem Match a graph pattern to the data Most common approach Join partial results for each edge of the query 9/18
- 10. Solving approaches Logic-based Find all the answers matching all of the query pattern Optimisation Find answers matching as much of the query as possible Important implications of the optimisation Only some of the answers will be found Some of the answers found will be partially true 10/18
- 11. An optimisation approach: eRDF Guess the answers to the query Evolutionary algorithm Evaluate validity of candidate solution Optimise with a recombination + local search 11/18
- 12. Some results Tested on queries with varied complexity Works best with more complex queries Find exact answers when there are some 12/18
- 13. Finding implicit facts in the data Copyright: 13/18 givingnot@rocketmail.com (Flickr, image 6990161491)
- 14. The problem Deduce new facts from others Most common approach Centralise all the facts, batch process deductions 14/18
- 15. Solving approaches Logic-based Find all the facts that can be derived from the data Optimisation Find as many facts as possible while preserving consistency Important implications of the optimisation Only some of the facts will be found Unstable content 15/18
- 16. An optimisation approach: Swarms Swarm 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. Some results If they stay, most of the implicit facts are derived Ants need to follow each other to deal with precedence of rules Several ants per rule are needed 17/18
- 18. Take home message Logic problems can be turned into optimisation problems 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