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Measuring the Semantic Web

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Title: PowerPoint Presentation Last modified by: Roberto Garc a Gonz lez Created Date: 1/1/1601 12:00:00 AM Document presentation format: Presentaci n en pantalla – PowerPoint PPT presentation

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Title: Measuring the Semantic Web


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

2
Outline
  • Motivation why to measure?
  • Approach complex systems
  • Measuring applying statistical tools
  • Results is 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? Whats the metric?

4
Motivation
  • Why to measure?
  • From the TimBLWeaving 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
  • The WWW

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 gtgt
    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
Results
10
Results
  • It is a small worlddiameter smaller than random
    graph, d4.37 while drand7.23
  • It has high clusteringC0.152 while
    Crandom0.0000895
  • 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 ltroberto_at_rhizomik.netgtRosa Gil
    ltrgil_at_diei.udl.esgt
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