How Knowledge Mining plays an important role in AI implementations

With the buzz of Artificial Intelligence, companies should leverage it right away to gain that early mover advantage and stand apart from the competition. However, there are various components to consider when a company considers any AI application deployment. One of them is Knowledge Mining, which can help organizations leverage the data available to them.

Knowledge mining can play a key role for enterprises that are adapting to AI applications in their operations. Vast enterprises collect a lot of market data and consumer information that is left underutilized. Knowledge Mining is a key to understanding and leveraging that data. This blog covers how!

What is Knowledge Mining?

Before defining Knowledge Graphs, it’s crucial to understand that businesses deal with two types of data—unstructured and structured. As confirmed by Gartner, 80% of such data is unstructured, and only the remaining 20% is structured. Structured data is easier to compare, process, and analyze, while unstructured data, in the form of images, audio, video, etc., is not as easy to manage.

Knowledge Mining is a subset of AI; it is a domain that focuses on learning insights from a vast amount of unstructured information. Knowledge Mining brings the data from different sources together to analyze them for trends and patterns. This can enable companies with their big data operations to uncover new insights from their vast unstructured data.

So far, companies have relied on manual analysis to analyze the unstructured data. However, as per an IDC report, manual analysis requires 2.5 man hours a day to analyze unstructured data. It is almost a third of a workday spent on tasks that could be saved with Knowledge Mining.

How does Knowledge Mining work?

Knowledge Mining helps organizations tap into a pool of untapped, resourceful information. Knowledge Mining, as the name suggests, digs deeper into various data sources and formats to establish connections, gain knowledge, and apply them as per the organizations’ AI use cases.

Here are three phases to knowledge mining: ingest, enrich, and explore.

1. Collecting information - Ingestion:

Knowledge mining starts from collecting information from all data sources and ingesting it into one pool. Structured data is simpler to relate and manage however unstructured data is not predefined in any data model. Data from files such as PDFs, images, Word documents, and powerpoints is sourced from sources including non-relational databases, APIs, blob storage, and files.

In this phase of ingestion, document cracking is performed to gather data from various sources and break them in a common medium, which is often the text data through NLP and OCR techniques.

2. Applying AI & ML models - Enrichment:

The next step after knowledge ingestion is enriching that data through AI models to process the information. This step essentially learns from the available data to identify patterns and information. This step would mean that you can process available data through AI solutions from Azure Cloud & AWS, or you can train your own machine learning model with a specialized AI team.

NLP, Computer Vision, Sentiment Analysis, and various such AI techniques would help in understanding and making sense of information that is available to the company after the ingestion phase.

3. Exploration & output:

This last step of a Knowledge Mining process is analyzing enriched and ingested information. This step ensures that ML models train and learn from the information that is available to the company, unique to their business. 

This part of the process also ensures that the available data is well-indexed for searches and end-user applications. Analysis tools such as PowerBI can further add value here by analyzing the information and deriving deeper insights from the available information.

How does Knowledge Mining help any organization?

Available unstructured data in the form of documents or images can only serve the purpose of records that are mostly dormant and occupy their resources redundantly most of the time.

Knowledge Mining is the process of making the most of peculiar business data to derive insights that serve a better purpose, contribute to overall business decision-making, and leave a lasting impact as part of their ML model.

Here are some ways in which knowledge mining can take thousands of organization files and derive extensive and actionable insights:

• Understanding the meaning of available statistics & demographics

• Learning the depth and accuracy of data gathered through the insights it provides. It can also help identify any gaps into data collection.

• Aggregation of the available knowledge to find deeper insights through analysis using business intelligence tools

• Learning about the outcomes of unique business operations through Knowledge Mining over a period of time. 

That's not all of it. Knowledge Mining can be one part of any company’s AI implementation plan that emphasizes the data available to them. With better insights through it, organizations can then apply different AI applications for particular use cases, such as predictions and automation.

Sooner the better

When is the right time for a company to explore mining? The answer to that lies in the readiness of available data. If a company has the right amount and the quality of data, then it can start leveraging Knowledge Mining to unlock the value of dormant information.

If an organization doesn't have such insightful data at the moment, then they must start looking into different sources of data collection as soon as they can.