Cluster Analysis – Is it Really Useful for Literature Discovery?

Cluster analysis – grouping similar objects together – is frequently used in data analysis and visualization. I’ve been seeing various types of clustering used for finding relevant documents more and more, both for visualizing document sets (essentially providing a landscape for the user to explore) and for finding related documents (essentially “more like this”). But, these [...]

2017-03-28T16:14:28+00:00 March 23rd, 2017|Artificial Intelligence, Informational|

Watson Cancelled at MD Anderson: No Surprise

The flap about IBM Watson’s Oncology Expert Advisor at MD Anderson is causing quite a stir.  A Forbes article called the benching of Watson a “setback for artificial intelligence in medicine”.  MD Anderson’s business process issues aside, the failure of Watson to meet its goals was neither a setback for AI nor a surprise.  Here’s why. [...]

2017-03-28T16:08:58+00:00 February 27th, 2017|Artificial Intelligence, Informational|

Searching and Knowledge Don’t Always MeSH

The National Library of Medicine has provided Medical Subject Headings (MeSH) for more than 60 years, with the first official list being published in 1954 (https://www.nlm.nih.gov/mesh/intro_preface.html#pref_hist). This has been an invaluable resource and a critical part of search strategies since computerization of literature began. But, as we discuss in a new whitepaper, new technologies such as [...]

2016-10-07T09:32:02+00:00 June 22nd, 2016|Informational|

Wish you had found that reference BEFORE the project?

30% of R&D budgets are wasted due to missed information According to statistics compiled by the European Patent Office, researchers spend up to 30% of their R&D budget rediscovering information that was already known. Sound familiar? Why do company and university researchers continue to make this costly mistake? There are two major contributors: […]

2016-10-07T08:29:23+00:00 January 18th, 2016|Informational|