The sheer volume and variety of data are prompting enterprises to identify automated solutions for deriving insights and making decisions. One way would be to apply natural language processing (NLP) and knowledge graph technology to datasets. According to Thomson Reuters, this will help label, tag, and present even difficult to manage textual datasets, and uncover previously hidden connections and insights.
The ability to break down barriers to patent information is one of the defining features of the European Patent Office (EPO). The organization has always endeavored to make public information, useful and accessible. Precisely why, the EPO finds linked data a highly appealing tool says Benoît Battistelli, president of the Munich, Germany based organization.
In the past, there have been many technologies, such as expert systems, Groupware, enterprise search, social networking and many more, which were expected to replace knowledge management (KM) or make it obsolete. Nick Milton, director of Scotland, UK based company Knoco Limited, believes artificial intelligence (AI) cannot replace KM, but it will simplify, disrupt and accelerate it.
Text analytics processes and outcomes are difficult to fathom when discussed and not shown. As a result, there is a need to show how text analytics are employed along with a visualization of the results. According to Ahren E. Lehnert, senior manager, text analytics solution with the Colorado, USA, based Synaptica; the search interface is the natural place to demonstrate text analytics.
The exchange of scholarly knowledge continues to be primarily document-based. However, the proliferation of scientific-literature as granular text documents has sparked-off discussions on the reproducibility crisis. According to Sören Auer, director of the Hannover, Germany based Leibniz Information Centre for Science and Technology and University Library (TIB), there is a need to rethink document-centered knowledge exchange.