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.

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
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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
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3. When solutions do not (quite) fit the problem ...
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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
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5. Interacting with Linked Data
Common goals
Completeness: all the answers
Soundness: only exact answers
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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)
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7. From logic to optimisation
Optimise towards the revised goals
Need methods that cope with uncertainty, context,
noise, scale, ...
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8. Answering queries over the data
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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
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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
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11. An optimisation approach: eRDF
Guess the answers to the query
Evolutionary algorithm
Evaluate validity of candidate solution
Optimise with a recombination + local search
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12. Some results
Tested on queries with
varied complexity
Works best with more
complex queries
Find exact answers
when there are some
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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
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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
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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
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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
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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...)
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