"Complex Systems Analysis on the Semantic Web"
The Semantic Web is envisioned as the next generation of the web. The World Wide Web, in it current form, was conceived and evolved as a mechanism for presenting information in human readers. As media and data have become more important on the web, a need has arisen for a way to encode that data so that computers can process it, understand how pieces fit together, and present it in a variety of ways. Web standard languages, such as RDF, RDFS, and OWL, allow users to create ontologies with interlinked classes and properties, to extend existing ontologies and modify them to fit the needs of a given project, and to create instances of those classes, relate them to one another. The result is a second level of web. On top of the interlinked pages of data on the hypertext web are interlinked descriptions of data and the content of those pages on the Semantic Web. This network of interconnected data is a complex system, and because the links have clear meaning – unlike links on the hypertext web – the systems can be analyzed in great depth. This paper will present background on the semantic web and illustrate how it emerges as a complex system of data. Using the Semantic Web project FOAF (Friend-of-a-friend) and an extension to represent trust and reputation ratings between individuals, we will show the results and applications of social network analysis when applied to the networks that arise on the semantic web. This paper will go on to outline several areas where traditional complex systems analysis can benefit from the Semantic Web, and how the results can be useful to understanding the relationships between data, projects, and fields of science.