Enhancing Semantic Web Data Access

The Semantic Web is coined by Tim Berners-Lee in 1998 as a web of data for machine consumption. Its applicability in supporting real world applications on the World Wide Web, however, remains unclear to this day mainly because its Web aspect has been neglected in past research. Most existing works usually model the Semantic Web as one universal RDF graph, and they either ignore the storage layer or investigate ad hoc storage layers (e.g. databases and peer-to-peer systems) other than the Web.

In order to access Semantic Web data published on the Web, information consumers must be aware of the context of the data and need effective tools for locating and retrieving the wanted data.

Therefore, we propose an enhanced model of the Semantic Web to characterize both the content and the context of Semantic Web data. Based on this model, we have harvested a significant and less biased sample set of Semantic Web data from the Web to measure the global properties of the Semantic Web. Moreover, we build a novel search and navigation model to characterize the unique Semantic Web data access behaviors and then develop a Semantic Web search engine as enabling tool. Finally, we investigate two important technologies for evaluating the quality of Semantic Web data: (i) a rational ranking algorithm which orders the popularity of Semantic Web documents and terms using our search and navigation model, and (ii) a new level of granularity, RDF molecule, to support lossless decomposition of an RDF graph and finding its (partial) supportive evidences on the Semantic Web. This dissertation systematically investigates the Web aspect of the Semantic Web. Its primary contributions are the enhanced conceptual model of the Semantic Web, the novel Semantic Web search and navigation model, and the Semantic Web search engine.