ABSTRACT
Introduction ............................................................................................................ 151 The Semantic Web ................................................................................................. 152
Grammar ........................................................................................................... 152 Vocabulary ......................................................................................................... 153
Chemical Compounds and the Semantic Web .............................................. 155 Ontology IDs: The Case of OBO ................................................................. 156 BioRDF and Other Biological Identifi ers ..................................................... 157
Case Studies ........................................................................................................... 157 Web Services ..................................................................................................... 158 Databases: ChemBlast ....................................................................................... 159 Publishing: Semantic Eye ................................................................................. 159 Publishing: RSC Project Prospect ..................................................................... 159
History and Development Route .................................................................. 159 Semantic Content ......................................................................................... 160 Results and Applications .............................................................................. 161
Experimental Data Standards and the Semantic Web ............................................ 163 Future Directions ................................................................................................... 164 References .............................................................................................................. 165
The Semantic Web is a vision of the World Wide Web where the pages can be, in a manner of speaking, understood by computers (Berners-Lee et al. 2001). What this requires is a consistent set of machine-readable identifi ers for concepts and a defi ned set of logical relations that can be used to draw inferences about them and reason over papers. This is to be distinguished from the natural language processing problem of question answering, where queries in natural language, such as “Where are the pubs in Richmond?”, are answered by parsing web pages themselves. As we shall see later on, natural language processing is a promising way of bridging between the human-readable and machine-readable web.
