Knowledge Mining
What is Knowledge Mining?
In recent years, many organisations have needed help dealing with the ever-increasing amount of data under their control. These challenges become more difficult due to the "wide variety of content types that make up the bulk of data, such as PDFs, images, and videos. Transforming all this content into insights requires extensive time, resources, and data science expertise. This process led to a new wave of AI-powered digital transformation with knowledge mining at heart."
Knowledge mining is an emerging discipline in artificial intelligence (AI) that uses a combination of intelligent services to learn from vast amounts of information quickly. "It allows organisations to deeply understand and easily explore information, uncover hidden insights, and find relationships and patterns at scale."
Benefits of Knowledge Mining
Enable knowledge extraction
The basis for some of the greatest future innovations may be locked, unseen, in different types of files and sources. Knowledge mining allows the extraction of text-based content from file formats such as PDF, Microsoft Word, PowerPoint, and CSV. In addition, it allows you to extract information from video, image or audio files.
(HBR, 2019; Azure Microsoft, n.d.)
Gain faster insight
Most organisations still process nearly all of their documents and information manually. Knowledge mining allows organisations to work across different document types and formats, including images, audio files, forms, web pages, and office documents. They do not need workers to manually search for information and then copy, paste, or reenter it into another form.
(HBR, 2019; Azure Microsoft, n.d.)
Customise for your industry
Lastly, knowledge mining is easily customisable as it can easily integrate custom models or classifiers and build these with any language or framework. Explore insights with pre-configured search or through analytics tools and business applications.
There are 3 Steps to knowledge mining:
Ingest :
The first step to knowledge mining is providing the sources from which you want to extract data or information. The sources could be spreadsheets, PDFs, presentations, word-processing documents, paper forms, images/photographs, social media/online reviews, videos, industry-specific data formats, audio, and so on.
It could also include structured data, which is already formatted in a way that a machine or computer can understand. (HBR, 2019; Azure Microsoft, n.d.)
Enrich:
After the content is entered into the system, the next step would be to enrich the content with AI capabilities. These let you extract information, find patterns, and deepen your understanding. It could be custom AI models or pre-built AI services like vision, speech, language recognition, sentiment analysis, or translation. (HBR, 2019; Azure Microsoft, n.d.)
Explore:
The final step would be to explore the enriched, structured data via search, bots, existing business applications, and data visualisations. From here, it's possible to uncover the latest insights from the sources of content that you've input into the machine or computer at the start of this process. (HBR, 2019; Azure Microsoft, n.d.)
Everyday applications
NLP can conjure images of futuristic robots or may seem like a complicated technology only used in corporations. However, there are already basic examples of NLP at work.
Email filters
Predictive text
Smart assistants
Language translations
Healthcare
A great example of NLP in healthcare is the Amazon Comprehend Medical services, which extract disease conditions. It can handle meditation sessions and monitor treatment results using clinical trial reports, electronic health records, and patient notes.
In health analytics, NLP can predict diseases using pattern recognition methods, patients' speech and electronic health records.
(Kaur, J., 2022)
Recruitment
Have you ever wondered why there are so many rounds of interviews to get a job?
NLP is used in both the search and selection phases of job recruitment. In fact, the chatbot can handle job-related queries at the initial level. This includes identifying the required skills for a specific job and managing the initial level tests and exams.
(Kaur, J., 2022)
Sentiment analysis
The sentiments behind the words can be determined using sentiment analysis, which is only possible through NLP. The sentiment analysis offers detailed knowledge about the customer's behaviour and choices, which can be considered significant decision drivers.
(Kaur, J., 2022)