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scrazzl: semantic technology for evaluating research tools and products in scholarly literature
Author(s) -
KAVANAGH David J.,
PHILLIPS Paul
Publication year - 2012
Publication title -
learned publishing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.06
H-Index - 34
eISSN - 1741-4857
pISSN - 0953-1513
DOI - 10.1087/20120303
Subject(s) - work (physics) , value (mathematics) , computer science , knowledge management , data science , engineering , mechanical engineering , machine learning
ABSTRACT Scholarly research content contains vast stores of valuable, untapped knowledge and records the practical experiences of millions of research scientists. Analysis of this data to create useful actionable information promises to yield not only significant benefits for individual researchers, but also to provide a valuable market intelligence tool for companies that view scientists as their main customers. This article outlines some of the work that we are doing to unlock the intrinsic value of research content for the benefit of readers and advertisers. We report some findings of our internal market research, which illustrates the importance of documentary evidence and collegial recommendations to scientists when it comes to evaluating materials for scientific research. Furthermore, we outline some of the steps that we are taking to deliver this valuable, structured information to scientists and customers, in a fashion that is commercially viable and mutually beneficial for our company and the publishers with which we work.