Comparison Between Evolutionary Programming and Particle Swarm Optimization
Evolutionary and Swarm Computing for scaling up the Semantic Web
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Transcript of Evolutionary and Swarm Computing for scaling up the Semantic Web
Evolutionary and Swarm Computing for scaling up the Semantic Web
Evolutionary and Swarm Computing
for scaling up the Semantic Web
Christophe Guret (@cgueret), Stefan Schlobach, Kathrin Dentler,Martijn Schut, and Gusz Eiben
24th Benelux Conference on Artificial IntelligenceMaastricht University, October 25-26, 2012
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
Two concrete examples of re-formulated problems
Short paper based on this publication
When solutions do not (quite) fit the problem ...
Copyright: sfllaw (Flickr, image 222795669)
Linked Data
Graph/facts based knowledge representation tool
Connect resources to properties / other resources
Web-based: resources have a URITry http://dbpedia.org/resource/Amsterdam
Interacting with Linked Data
Common goalsCompleteness: all the answers
Soundness: only exact answers
Motivation
In the context of Web data ?Issues with scale
Issues with lack of consistency
Issues with contextualised views over the World
Revise the goalsAs many answers as possible (or needed)
Answers as accurate as possible (or needed)
From logic to optimisation
Optimise towards the revised goals
Need methods that cope with uncertainty, context, noise, scale, ...
Answering queries over the data
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The problem
Match a graph pattern to the data
Most common approachJoin partial results for each edge of the query
Solving approaches
Logic-basedFind all the answers matching all of the query pattern
OptimisationFind answers matching as much of the query as possible
Important implications of the optimisationOnly some of the answers will be found
Some of the answers found will be partially true
An optimisation approach: eRDF
Guess the answers to the query
Evolutionary algorithmEvaluate validity of candidate solution
Optimise with a recombination + local search
Some results
Tested on queries with varied complexity
Works best with more complex queries
Find exact answers when there are some
Finding implicit facts in the data
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The problem
Deduce new facts from others
Most common approachCentralise all the facts, batch process deductions
Solving approaches
Logic-basedFind all the facts that can be derived from the data
OptimisationFind as many facts as possible while preserving consistency
Important implications of the optimisationOnly some of the facts will be found
Unstable content
An optimisation approach: Swarms
Swarm of micro-reasonersBrowse the graph, applying rules when possible
Deduced facts disappear after some time
Every author of a paper is a personEvery person is also an agent
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
Take home message
Logic problems can be turned into optimisation problems
Trade offGained: 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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