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Measuring the Semantic Web

Measuring the Semantic Web. Rosa Gil Iranzo GRIHO, Universitat de Lleida, Spain Roberto García González rhizomik.net. Outline. Motivation why to measure? Approach complex systems Measuring applying statistical tools Results is the semantic web a complex system? Conclusions. Motivation.

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Measuring the Semantic Web

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  1. Measuring the Semantic Web Rosa Gil Iranzo GRIHO, Universitat de Lleida, Spain Roberto García González rhizomik.net

  2. Outline • Motivationwhy to measure? • Approachcomplex systems • Measuringapplying statistical tools • Resultsis the semantic web a complex system? • Conclusions

  3. Motivation • Semantic Web, an open evolving system. • TimBL: “Looking for a metric” in “The Fractal nature of the Web”, Design Issues. • How is it measured? What’s the metric?

  4. Motivation • Why to measure? • From the TimBL“Weaving the Web” Semantic Web plan… • Where we are now? • How is it evolving? • Are we going where it was planned? • …

  5. Approach • Semantic Web as complex as many other systems: • metabolic networks • acquaintance networks • food webs • neural networks • TheWWW • …

  6. Approach • This complex systems are studied using Complex Systems (CS) Analysis. • Statistical tools for graph models: • Degree Distribution • Small World • Clustering Coefficient • …

  7. Approach • Model the system as a graph. • CS graph characteristics: • Degree Distributionpower law, P(k) ≈ k - r • Small Worldsmall diameter, d ≈ drandom • Clustering Coefficienthigh clustering, C >> Crandom

  8. Measuring • Is the Semantic Web a CS? • It is already a graph. • Crawl all DAML Ontologies Library: • 2003: 56,592 nodes, 131,130 arcs • 2005: 307,231 nodes and 588,890 arcs • Statistical study of the graph.

  9. Network Nodes <k> C <d>  DAMLOntos(2003-4-11) 56,592 4.63 0.152 4.37 -1.48 DAMLOntos(2005-1-31) 307,231 3.83 0.092 5.07 -1.19 WWW ~200 M 0.108 3.10 -2.24 WordNet 66,025 0.060 7.40 -2.35 WordsNetwork 500,000 0.687 2.63 -1.50 Results

  10. Results • It is a small worlddiameter smaller than random graph, d=4.37 while drand=7.23 • It has high clusteringC=0.152 while Crandom=0.0000895 • It is scale freepower law degree distribution, P(k)≈k –1.19

  11. Results CDF (Cumulative Distribution Function) Degree

  12. Conclusions • The Semantic Web is a Complex System. • Behaves like a living system (neural network, food web, proteins net,…), i.e. the same dynamics. • Same behaviour 2003-2005.

  13. Conclusions • Just exploring applications: • Degree dynamics for trust computation. • Ontology alignment (clusters, centrality,…). • Metadata high volumes management. • etc. • More information and tools at: http://rhizomik.net/livingsw

  14. Thank you for your attention Roberto García <roberto@rhizomik.net>Rosa Gil <rgil@diei.udl.es>

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