Knowledge mining will drive the next wave of AI
Where data mining has been restrictive, knowledge mining widens the length, breadth and density of intelligence models
Source | techhq.com
If 2019 taught us anything, it was that every technology vendor, large and small, had to have a stance on Artificial Intelligence (AI) and the software automation advantages it can deliver.
Some vendors got so excited about AI and the Machine Learning (ML) that allows intelligence engines to get smarter, they forgot to talk about so-called digital transformation. But that was just for a while, not for long, obviously.
Industry spin and subterfuge notwithstanding, AI may now have another chapter to deliver… and it comes in the shape of Knowledge Mining. But before we understand what it is, let’s remember how we got here.
Data mining roots
Knowledge Mining stems from Data Mining, a term that was popularized in the nineties and carried us through the millennium. Data Mining is an interdisciplinary process incorporating statistics, mathematical modelling and pattern recognition and other aspects of information analytics.
In basic terms, Data Mining involves sifting through massive data sets to establish patterns to create what are known as ‘association rules’ (rather like an IF/THEN statement) to direct action based upon the data relationships discovered. People do still talk about Data Mining, but AI has in many cases displaced the.
Widening our narrow models
While Data Mining has been useful, information scientists argue that it was restricted to creating comparatively narrow AI models i.e. it was useful for doing (and learning) one specific thing, such as a tracking one type of image, categorizing one work process or some other defined and essentially discrete task.
Knowledge Mining widens the length, breadth and density of the intelligence model being constructed.
Data Mining centralizes on the processing of relatively well-structured information sets, often held in databases where information is nicely deduplicated, verified and parsed into appropriate fields. Knowledge Mining goes deeper in that it involves the ingestion of massive datasets spanning structured, semi-structured and unstructured information.
