Recently, there has been a push among both funders and journals to share raw data. The response has been positive and that in itself has created a unique challenge. Voluminous amount of data have been produced. There is too much data for humans to sift through. The semantic confusion is not a simple one-to-one correspondence. It is multidimensional as it includes spatial, temporal, and methodological differences, along with functional definitions. An obvious response to overcome this challenge is to use machine learning. However, it is an enormous semantic challenge to parse hundreds of bespoke terms to facilitate machine learning. In this scenario, can ontologies offer a way out?
Enterprises are often at a loss to determine whether they should choose a classification system based on ontology or a classification system based on taxonomy. The hesitation arises from the fact that ontology is frequently confused with taxonomy and vice versa. Therefore, it is important for enterprises to identify the critical distinctions between ontology and taxonomy.
Keywords have been in widespread use for searching textual databases for more than half a century now. However, it is a wonder that, despite the technological advances in computing in the intervening years, it is still ubiquitous when it comes to search. So much so, Kalev Hannes Leetaru, an American internet entrepreneur and academic, ponders on why we are unable to replace keywords with something by which machines can understand the world.
The structured Contributor Role Taxonomy (CRediT) taxonomy, was introduced in 2014, in response to the shift to contribution to complement (or replace) the concept of authorship. The need emerged following increasing dissatisfaction with established bibliographic conventions for describing and listing authors on scholarly outputs. In addition, the bibliographic conventions were outdated and unable to convey the diversity of contributions that researchers made to published work.
Once upon a time, chatbots could merely serve a customer based on a static script coded into their rules. This was a challenge because customers would often employ slang, shorthand or expressions that were not part of the script, thereby limiting the ability of the chatbots to respond satisfactorily. Now, the advent of intelligent search powered by machine learning (ML) and natural language processing (NLP) has given rise to conversational chatbots. According to Allyson Barr, Chief Marketing Officer at Attivio, these chatbots deliver a more satisfactory productive experience.