The publishing initiative appears ready to simplify text mining.
Data Mining and Text Mining
For many years, Tim Berners-Lee, the inventor of the World Wide Web, dreamed of machines being able to assist humans in using his invention. This would enable advanced search tools to extract not just words or phrases but also for other search engines to find meanings and patterns. This “semantic web” has been gradually assembled. The final step brings users of scientific literature closer to realizing this dream by enhancing computer access to the full text of scientific literature.
Text Mining in Life Sciences
Some researchers have begun to use text mining. For example, biologists have developed software to explore open “text bases,” particularly the PubMed database. They scan numerous publications to discover relationships based on phrases or sentences that, when analyzed together, link one entity (like a disease) to another (like a molecule). At the University of California, Berkeley, the BioText project is used to explore proteomics, for example (http://biotext.berkeley.edu). At the University of Illinois at Chicago, the Arrowsmith program explores disease causation (http://arrowsmith.psych.uic.edu/arrowsmith_uic/index.html). And at the European Bioinformatics Institute near Cambridge in the UK, the EBIMed search engine explores protein-protein interactions (http://www.ebi.ac.uk/Rebholz-srv/ebimed/index.jsp).
Text Annotation Standards
However, publishers have yet to develop a unified standard for annotating their content that allows computers to access the full text. Earlier this month, the Nature Publishing Group launched a preliminary proposal for this standard. The proposal is not a commercial product but a potential service for the community. It is open for comment and does not aim to provide a competitive advantage to us: rather, it will succeed only if adopted by other publishers.
Open Text Mining Interface
The proposal is the Open Text Mining Interface (OTMI), which was first presented at the Life Sciences Conference and Expo in Boston earlier this month. A description and examples can be found at http://blogs.nature.com/wp/nascent/2006/04/open_text_mining_interface_1.html. The proposal will make the encoded text freely available to everyone. If all publishers adopted this standard or a similar one, all literature would become accessible for mining.
Business Models and Text Mining
How does this proposal relate to the different business models of publishers? Publishers who pay authors will be able to use this approach for machine reading and help users find their content more easily. Publishers who pay subscribers will follow the Nature Publishing Group in making this release of full text machine-explorable but not human-readable. (Charging for machine access through various publisher walls will make machine text mining impossible.) The OTMI approach involves encoding and mixing sentences while preserving semantic relationships as much as possible.
Critics will point out that this also limits the machine’s reading capability; for example, proximity searching becomes impossible. But the subscription payment model is strongly supported in the market. The OTMI represents a potential solution balancing business needs and open access. Nature and its publishers welcome feedback on this initiative, which should be sent either to [email protected] or to the aforementioned blog.
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Machine readability. Nature 440, 1090 (2006). https://doi.org/10.1038/4401090a
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Publication Date: April 26, 2006
Issue Date: April 27, 2006
DOI: https://doi.org/10.1038/4401090a
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