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

Slides:



Advertisements
Similar presentations
Homework Answers P. 570 P   28. 6 4. /9
Advertisements

Angstrom Care 培苗社 Quadratic Equation II
AP STUDY SESSION 2.
UPV- S.Lucas ALFA LERnet Kick-Off meeting, Braga, Jun , 2005 ALFA LERnet Node Presentations Technical University of Valencia (UPV) Salvador Lucas.
Copyright © 2003 Pearson Education, Inc. Slide 1 Computer Systems Organization & Architecture Chapters 8-12 John D. Carpinelli.
Copyright © 2013 Elsevier Inc. All rights reserved.
Performance in Decentralized Filesharing Networks Theodore Hong Freenet Project.
STATISTICS INTERVAL ESTIMATION Professor Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering National Taiwan University.
Rhizomik Semantic Integration and Retrieval of Multimedia Metadata Roberto García and Universitat de Lleida, Lleida, Spain Òscar Celma Universitat Pompeu.
Copyright Management for the LUISA Semantic Learning Content Management System Roberto García Universitat de Lleida, Spain Tomas Pariente ATOS Origin SAE,
Ontological Infrastructure for a Semantic Newspaper Roberto García 1, Ferran Perdrix 1,2, Rosa Gil 1 1 GRIHO – Human Computer Interaction Research Group.
Improving Human-Semantic Web Interaction: The Rhizomer Experience Roberto García and Rosa Gil GRIHO - Human Computer Interaction Research Group Universitat.
Semantic Integration and Retrieval of Multimedia Metadata Facilitating Business Interoperability from the Semantic Web Roberto García, Rosa Gil Universitat.
Building a Semantic IntraWeb with Rhizomer and a Wiki Roberto Garcia and Rosa Gil GRIHO (Human Computer Interaction Research Group) Universitat de Lleida,
Publishing XBRL as Linked Open Data
Copyright 2008 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute 1 From OntoSelect to OntoSelect-SWSE.
We need a common denominator to add these fractions.
Exploring Traversal Strategy for Web Forum Crawling Yida Wang, Jiang-Ming Yang, Wei Lai, Rui Cai, Lei Zhang and Wei-Ying Ma Chinese Academy of Sciences.
CALENDAR.
Measurement and Analysis of Online Social Networks 1 A. Mislove, M. Marcon, K Gummadi, P. Druschel, B. Bhattacharjee Presentation by Shahan Khatchadourian.
Chapter 7 Sampling and Sampling Distributions
1 00/XXXX © Crown copyright Carol Roadnight, Peter Clark Met Office, JCMM Halliwell Representing convection in convective scale NWP models : An idealised.
A Fractional Order (Proportional and Derivative) Motion Controller Design for A Class of Second-order Systems Center for Self-Organizing Intelligent.
Break Time Remaining 10:00.
PP Test Review Sections 6-1 to 6-6
1 Generating Network Topologies That Obey Power LawsPalmer/Steffan Carnegie Mellon Generating Network Topologies That Obey Power Laws Christopher R. Palmer.
Topology and Dynamics of Complex Networks FRES1010 Complex Adaptive Systems Eileen Kraemer Fall 2005.
Introduction to Complex Numbers
Complex Networks: Complex Networks: Structures and Dynamics Changsong Zhou AGNLD, Institute für Physik Universität Potsdam.
Exarte Bezoek aan de Mediacampus Bachelor in de grafische en digitale media April 2014.
Factors, Prime Numbers & Composite Numbers
Scale Free Networks.
1 Dynamics of Real-world Networks Jure Leskovec Machine Learning Department Carnegie Mellon University
Copyright © 2012, Elsevier Inc. All rights Reserved. 1 Chapter 7 Modeling Structure with Blocks.
Artificial Intelligence
1 Joseph Ghafari Artificial Neural Networks Botnet detection for Stéphane Sénécal, Emmanuel Herbert.
1 Using Bayesian Network for combining classifiers Leonardo Nogueira Matos Departamento de Computação Universidade Federal de Sergipe.
: 3 00.
5 minutes.
1 hi at no doifpi me be go we of at be do go hi if me no of pi we Inorder Traversal Inorder traversal. n Visit the left subtree. n Visit the node. n Visit.
Clock will move after 1 minute
Jan SedmidubskyOctober 28, 2011Scalability and Robustness in a Self-organizing Retrieval System Jan Sedmidubsky Vlastislav Dohnal Pavel Zezula On Investigating.
Physics for Scientists & Engineers, 3rd Edition
Select a time to count down from the clock above
3 - 1 Copyright McGraw-Hill/Irwin, 2005 Markets Demand Defined Demand Graphed Changes in Demand Supply Defined Supply Graphed Changes in Supply Equilibrium.
Hierarchy in networks Peter Náther, Mária Markošová, Boris Rudolf Vyjde : Physica A, dec
Topology Generation Suat Mercan. 2 Outline Motivation Topology Characterization Levels of Topology Modeling Techniques Types of Topology Generators.
Scale Free Networks Robin Coope April Abert-László Barabási, Linked (Perseus, Cambridge, 2002). Réka Albert and AL Barabási,Statistical Mechanics.
COMPLEX NETWORK APPROACH TO PREDICTING MUTATIONS ON CARDIAC MYOSIN Del Jackson CS 790G Complex Networks
Common Properties of Real Networks. Erdős-Rényi Random Graphs.
341: Introduction to Bioinformatics Dr. Natasa Przulj Deaprtment of Computing Imperial College London
Graph Theory in 50 minutes. This Graph has 6 nodes (also called vertices) and 7 edges (also called links)
LANGUAGE NETWORKS THE SMALL WORLD OF HUMAN LANGUAGE Akilan Velmurugan Computer Networks – CS 790G.
Complex Networks First Lecture TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA TexPoint fonts used in EMF. Read the.
Emergence of Scaling and Assortative Mixing by Altruism Li Ping The Hong Kong PolyU
Neural Network of C. elegans is a Small-World Network Masroor Hossain Wednesday, February 29 th, 2012 Introduction to Complex Systems.
Yongqin Gao, Greg Madey Computer Science & Engineering Department University of Notre Dame © Copyright 2002~2003 by Serendip Gao, all rights reserved.
March 3, 2009 Network Analysis Valerie Cardenas Nicolson Assistant Adjunct Professor Department of Radiology and Biomedical Imaging.
Hierarchical Organization in Complex Networks by Ravasz and Barabasi İlhan Kaya Boğaziçi University.
Cmpe 588- Modeling of Internet Emergence of Scale-Free Network with Chaotic Units Pulin Gong, Cees van Leeuwen by Oya Ünlü Instructor: Haluk Bingöl.
Lecture 23: Structure of Networks
Empirical analysis of Chinese airport network as a complex weighted network Methodology Section Presented by Di Li.
Lecture 23: Structure of Networks
Department of Computer Science University of York
Network Science: A Short Introduction i3 Workshop
Topology and Dynamics of Complex Networks
Peer-to-Peer and Social Networks
Lecture 23: Structure of Networks
Presentation transcript:

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 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?

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? –…

Approach Semantic Web as complex as many other systems: –metabolic networks –acquaintance networks –food webs –neural networks –The WWW –…

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

Approach Model the system as a graph. CS graph characteristics: –Degree Distribution power law, P(k) k - r –Small World small diameter, d d random –Clustering Coefficient high clustering, C >> C random

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.

Results NetworkNodes C DAMLOntos ( ) 56, DAMLOntos ( ) 307, WWW ~200 M WordNet66, WordsNetwork500,

Results It is a small world diameter smaller than random graph, d=4.37 while d rand =7.23 It has high clustering C=0.152 while C random = It is scale free power law degree distribution, P(k)k –1.19

Results CDF (Cumulative Distribution Function) Degree

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

Conclusions Just exploring applications: –Degree dynamics for trust computation. –Ontology alignment (clusters, centrality,…). –Metadata high volumes management. – etc. More information and tools at:

Thank you for your attention Roberto García Rosa Gil