We have developed the Chemical Semantic Web so that computers can understand primary publications and act upon them. An autonomous machine could read and understand an issue from J. Med. Chem., extract the information, run high-throughput computations and systematize the results leading to new scientific insights.
For robots the most exciting and most tractable part of scientific publications are formalized presentations of data (e.g. analytical proof of synthesis) and supplemental data (e.g. crystallography and spectra). We argue that these are "facts" under the Berne Copyright convention and therefore re-usable without hindrance. For many decades humans have manually abstracted articles and produced compilations and we argue that robots can do the same to great communal benefit. However it appears that some publishers now see a journal as a database and may regard chemically-aware robots as unacceptable under their license terms.
The public Semantic Web currently depends on complete absence of barriers to the re-use of information. Robots cannot currently negotiate license agreements, logon to sites, or make micropayments. We see Open Access, especially to data, as an exciting opportunity to transform chemical informatics and provide a global knowledge base. We shall present arguments that funders, researchers, editors and readers should promote a model of publication for Open Data.
We shall provide online demonstrations of the power and potential of the Chemical Semantic Web based on Open Access to primary publications.