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Issue No.05 - May (2008 vol.20)
pp: 692-702
In this paper, we measure and analyze the graph features of Semantic Web (SW) schemas with focus on power-law degree distributions. Our main finding is that the majority of SW schemas with a significant number of properties (resp. classes) approximate a power-law for total-degree (resp. number of subsumed classes) distribution. Moreover, our analysis revealed some emerging conceptual modeling practices of SW schema developers, namely: a) each schema has a few focal classes that have been analyzed in detail (i.e., having numerous properties and subclasses) which are further connected with focal classes defined in other schemas, b) the class subsumption hierarchies are mostly unbalanced (i.e., some branches are deep and heavy, while others are shallow and light), c) most properties have as domain/range classes that are located highly at the class subsumption hierarchies and d) the number of recursive/multiple properties is significant. The knowledge of these features is essential for guiding synthetic SW schema generation, which is an important step towards benchmarking SW repositories and query languages implementations.
Semantic Web, power-laws, conceptual schemas morphology
Yannis Tzitzikas, Dimitris Kotzinos, Vassilis Christophides, "On Graph Features of Semantic Web Schemas", IEEE Transactions on Knowledge & Data Engineering, vol.20, no. 5, pp. 692-702, May 2008, doi:10.1109/TKDE.2007.190735
[1] D. Brickley and R.V. Guha, Resource Description Framework Schema (RDF/S) Specification 1.0, World Wide Web Consortium (W3C) candidate recommendation, Mar. 2000.
[2] M. Dean and G. Schreiber, OWL Web Ontology Language Reference, World Wide Web Consortium (W3C) recommendation, Feb. 2004.
[3] M. Faloutsos, P. Faloutsos, and C. Faloutsos, “On Power Law Relationships of the Internet Topology,” Proc. ACM SIGCOMM, 1999.
[4] R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins, “Extracting Large Scale Knowledge Bases from the Web,” Proc. Eighth Int'l World Wide Web Conf. (WWW), Apr. 1999.
[5] A. Barabási and R. Albert, “Emergence of Scaling in Random Networks,” Science, vol. 286, no. 509, 1999.
[6] A.A. Nanavati, S. Gurumurthy, G. Das, D. Chakraborty, K. Dasgupta, S. Mukherjea, and A. Joshi, “On the Structural Properties of Massive Telecom Call Graphs: Findings and Implications,” Proc. 15th ACM Int'l Conf. Information and Knowledge Management (CIKM '06), pp. 435-444, 2006.
[7] L. Ding and T. Finin, “Characterizing the Semantic Web on the Web,” Proc. Fifth Int'l Semantic Web Conf. (ISWC), 2006.
[8] R. Gil, R. García, and J. Delgado, “Measuring the Semantic Web,” Semantic Web Challenges for Knowledge Management: Towards the Knowledge Web, vol. 1, pp. 69-72, July 2004.
[9] C. Tempich and R. Volz, “Towards a Benchmark for Semantic Web Reasoners—An Analysis of the DAML Ontology Library,” Proc. Second Int'l Workshop Evaluation of Ontology-Based Tools (EON), 2003.
[10] L. Ding, T. Finin, and A. Joshi, “Analyzing Social Networks on the Semantic Web,” IEEE Intelligent Systems, Jan. 2005.
[11] H. Halpin, V. Robu, and H. Shepherd, “The Dynamics and Semantics of Collaborative Tagging,” bottom.html, 2006.
[12] K. Shen and L. Wu, “Folksonomy as a Complex Network,” Cornell Univ. Library, , 2008.
[13] L. Tari, C. Baral, and P. Dasgupta, “Understanding the Global Properties of Functionally-Related Gene Networks Using the Gene Ontology,” Proc. Pacific Symp. Biocomputing (PSB '05), vol. 10, pp.209-220, 2005.
[14] A. Hogan, A. Harth, and S. Decker, “Performing Object Consolidation on the Semantic Web Data Graph,” Proc. WWW Workshop I3: Identity, Identifiers, Identification, 2007.
[15] B. Aleman-Meza, C. Halaschek, A. Sheth, I. Arpinar, and G. Sannapareddy, “SWETO: Large-Scale Semantic Web Test-Bed,” Proc. SEKE Workshop Ontology in Action, pp. 490-493, June 2004.
[16] P. Hayes, RDF Semantics, World Wide Web Consortium (W3C) recommendation, /, Feb. 2004.
[17] G.M. Kuper and J. Siméon, “Subsumption for XML Types,” Proc. Eighth Int'l Conf. Database Theory (ICDT '01), pp. 331-345, 2001.
[18] L. Li, D. Alderson, J.C. Doyle, and W. Willinger, “Towards a Theory of Scale-Free Graphs: Definition, Properties, and Implications,” Internet Math., vol. 2, no. 4, pp. 431-523, 2005.
[19] G.K. Zipf, Human Behavior and the Principle of Least Effort. Addison-Wesley, 1949.
[20] Z. Bi, C. Faloutsos, and F. Korn, “The “DGX” Distribution for Mining Massive, Skewed Data,” Proc. ACM SIGKDD, Aug. 2001.
[21] L.A. Adamic, “Zipf, Power-Laws, and Pareto—A Ranking Tutorial,” rankingranking.html, Apr. 2000.
[22] W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery, Numerical Recipes in C, second ed. Cambridge Univ. Press, 1992.
[23] D. Chakrabarti and C. Faloutsos, “Graph Mining: Laws, Generators, and Algorithms,” ACM Computing Surveys, vol. 38, no. 1, 2006.
[24] S. Alexaki, V. Christophides, G. Karvounarakis, D. Plexousakis, and K. Tolle, “The ICS-FORTH RDFSuite: Managing Voluminous RDF Description Bases,” Proc. Second Int'l Workshop Semantic Web, May 2001.
[25] L. Ding, T. Finin, A. Joshi, R. Pan, R.S. Cost, Y. Peng, P. Reddivari, V.C. Doshi, and J. Sachs, “Swoogle: A Search and Metadata Engine for the Semantic Web,” Proc. 13th ACM Conf. Information and Knowledge Management (CIKM '04), Nov. 2004.
[26] Y. Theoharis, “On Power Laws and the Semantic Web,” master's thesis, Computer Science Dept., Univ. of Crete, Feb. 2007.
[27] T.D. Wang, “Gauging Ontologies and Schemas by Numbers,” Proc. Fourth Int'l Workshop Evaluation of Ontology-Based Tools (EON), 2006.
[28] Y. Theoharis, G. Georgakopoulos, and V. Christophides, “On the Synthetic Generation of Semantic Web Schemas,” Proc. Joint ODBIS and SWDB Workshop Semantic Web, Ontologies, Databases, Sept. 2007.
[29] A. Magkanaraki, S. Alexaki, V. Christophides, and D. Plexousakis, “Benchmarking RDF Schemata for the Semantic Web,” Proc. First Int'l Semantic Web Conf. (ISWC), 2002.
[30] B. Hoser, A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme, “Semantic Network Analysis of Ontologies,” Proc. Third European Semantic Web Conf. (ESWC), 2006.
[31] H. Alani and C. Brewster, “Ontology Ranking Based on the Analysis of Concept Structures,” Proc. Third Int'l Conf. Knowledge Capture, Oct. 2005.
[32] S. Tartir, I.B. Arpinar, M. Moore, A.P. Sheth, and B. Aleman-Meza, “OntoQA: Metric-Based Ontology Quality Analysis,” Proc. IEEE ICDM Workshop Knowledge Acquisition from Distributed, Autonomous, Semantically Heterogeneous Data and Knowledge Sources, 2005.
[33] Y. Theoharis, V. Christophides, and G. Karvounarakis, “Benchmarking Database Representations of RDF/S Stores,” Proc. Fourth Int'l Semantic Web Conf. (ISWC), 2005.
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