Ontologies to facilitate revolution in scientific publishing

In an article in Science entitled Strategic Reading, Ontologies, and the Future of Scientific Publishing, authors Allen Renear and Carole Palmer argue that ontologies will facilitate a revolution in scientific publishing whereby scientists will interact increasingly with the literature on a particular topic as whole and less frequently with entire, individual articles.

They state:

The revolution in scientific publishing that has been promised since the 1980s is about to take place. Scientists have always read strategically, working with many articles simultaneously to search, filter, scan, link, annotate, and analyze fragments of content. An observed recent increase in strategic reading in the online environment will soon be further intensified by two current trends: (i) the widespread use of digital indexing, retrieval, and navigation resources and (ii) the emergence within many scientific disciplines of interoperable ontologies. Accelerated and enhanced by reading tools that take advantage of ontologies, reading practices will become even more rapid and indirect, transforming the ways in which scientists engage the literature and shaping the evolution of scientific publishing.

A key enable of the revolution is the development of scientific ontologies, which serve as computational scientific theories. The authors note that:

Originally motivated by the need for data integration, scientific ontologies are now being explored for STM publishing to support information retrieval and text mining, with applications for hypothesis generation and knowledge discovery well underway.

They also highlight the need for collaborative development of ontologies to ensure interoperability, noting that Although many biological ontologies were originally developed independently, the need for interoperability has driven collaboration, a good example being the Open Biomedical Ontologies (OBO), which currently has 54 participating projects (18), including Microarray Gene Expression Data (MGED), BioPAX, for biological pathways data, and Foundational Model of Anatomy (FMA).