Knowledge Extraction

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What is Knowledge Extraction?

Knowledge extraction is a process used to extract structured information – or knowledge – from a variety of data sources, including structured data (i.e., databases or spreadsheets, unstructured data (i.e., text documents or emails, and semi-structured data (i.e., XML files or HTML pages) sources.


The main purpose of knowledge extraction is to convert data into actionable knowledge, making it easier for humans or machines to interpret and use the data to answer complex questions, make decisions, or power artificial intelligence (AI) applications. When used in AI, knowledge extraction is achieved using a variety of techniques, including data mining, machine learning (ML), and natural language processing (NLP).

Techopedia Explains the Knowledge Extraction Meaning

Techopedia Explains the Knowledge Extraction Meaning

The meaning of knowledge extraction is to retrieve knowledge from unstructured and semi-structured data sources. By identifying patterns and relationships, the extracted data is converted into actionable knowledge that can be applied to make decisions, solve problems, or power AI. It’s used for various application, including knowledge bases, business intelligence (BI), and question-answering systems.

History of Knowledge Extraction

The initial concepts of knowledge extraction are rooted in early, manual data storage and retrieval methods: think of early systems like library catalog archives, and, later, Boolean search systems that evolved into sophisticated automated systems with the advent of computers, artificial intelligence, and data processing technologies.

One of the earliest automated knowledge extraction systems was the General Problem Solver (GPS), a computer program and associated theoretical framework created in 1957 by Herbert A. Simon, J. C. Shaw, and Allen Newell, intended to work as a universal problem solver machine.

How Knowledge Extraction Works

The knowledge extraction definition covers the process of extracting and transforming data into meaningful knowledge. It operates through a series of steps, beginning with the collection of data from various sources. The data undergoes preprocessing (i.e., cleaning to remove errors and inconsistencies) to enhance the data set. Subsequently, the data is integrated and transformed into a format for algorithmic analysis.

Techniques such as data mining, machine learning, and natural language processing are applied to identify patterns, trends, and relationships within the data. This information is then organized into a structured framework, evaluated, and displayed in a format accessible to end-users.

How Knowledge Extraction Works
Source: Research Gate

Steps of Knowledge Extraction

Key steps in the knowledge extraction process to obtain useful information can vary, depending on data sources and the intended use.

Generally, this includes:

  1. Data Collection

    Gather data from various sources to create a comprehensive dataset.
  2. Data Preprocessing

    Clean, normalize, and prepare the data to improve the quality of the dataset.
  3. Data Integration and Transformation

    Combine data into a unified dataset for comprehensive analysis. Data is transformed into a suitable format for processing by algorithms.
  4. Pattern and Relationship Identification

    Identify patterns, trends, and relationships in the data using techniques such as data mining, machine learning, and natural language processing.
  5. Knowledge Structuring

    Organize the identified patterns and relationships into a structured framework that represents the extracted knowledge.
  6. Evaluation and Refinement

    Assess the accuracy and relevance of the extracted knowledge. Refine the process based on evaluation results.
  7. Knowledge Representation

    Present the structured knowledge in accessible formats for end-users to be applied to decision-making or problem-solving tasks.

Knowledge Extraction Techniques

Knowledge extraction techniques vary, covering a broad spectrum from general approaches to specific applications. Depending on the data and goals of the knowledge extraction process, techniques can be used individually or in combination.

Example techniques include:

Types of Data Sources Used in Knowledge Extraction

Types of Data Sources Used in Knowledge Extraction
Source: Astera

A wide range of data sources are used in knowledge extraction that can be broadly categorized based on their structure and type of content.

Examples include:

  • Structured Data Sources: spreadsheets, databases, catalogs.
  • Unstructured Data Sources: images, videos, text documents, emails, audio files.
  • Semi-Structured Data Sources: XML or HTML documents, JSON files.

Knowledge Extraction Use Cases

Knowledge extraction is a key process in fields like machine learning and data science, covering various domains — essentially any field where there’s a need to gain actionable knowledge for decision-making, predictions, and innovation.

For instance, in market research, this process involves extracting and analyzing data to identify market trends, brand perceptions, and consumer behaviors, such as purchasing habits insights from social media posts and online reviews.

The extracted data is transformed into actionable knowledge that businesses leverage to make informed decisions regarding product or service development and marketing strategies.

Knowledge Extraction Examples

While this list isn’t exhaustive, common knowledge extraction examples include:

Knowledge Extraction Pros and Cons


  • Access to comprehensive insights
  • Enhanced decision-making
  • Improved accuracy of derived insights
  • Leads to new discoveries and innovations
  • Personalization of services and products


  • Algorithms can amplify biases in the data
  • Dependent on the data quality
  • Privacy concerns around sensitive data
  • Requires costly investment in complex technology
  • Requires ongoing maintenance and updates

Future of Knowledge Extraction

Just as the field of knowledge extraction has evolved with the advent of computers and technology, its future is poised to be shaped by advancements in machine learning algorithms, artificial intelligence, and the exponential growth of data.

As commonly used technologies like natural language processing become more sophisticated, knowledge extraction will play a key role in driving more precise and enhanced decision-making processes.

According to Allied Market Research, the application of these technologies in data extraction is expected to present significant opportunities for growth. The global data extraction market, valued at $2.14 billion in 2019, is projected to reach $4.90 billion by 2027.

The Bottom Line

Knowledge extraction is a key process for transforming complex data into actionable knowledge to answer questions, make decisions, or enhance AI applications. As data generation continues at an unprecedented rate, the importance and usage of knowledge extraction across numerous domains will also increase. It’s important for organizations to carefully consider and address issues related to data privacy, security, and the ethical use of extracted information.


What is knowledge extraction in simple terms?

What are the steps of knowledge extraction?

What are examples of knowledge extraction?

What is the difference between knowledge extraction and information extraction?

What is the difference between structured and unstructured data?


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Vangie Beal
Technology Expert
Vangie Beal
Technology Expert

Vangie Beal is a digital literacy instructor based in Nova Scotia, Canada, who has recently joined Techopedia. She’s an award-winning business and technology writer with 20 years of experience in the technology and web publishing industry.  Since the late ’90s, her byline has appeared in dozens of publications, including CIO, Webopedia, Computerworld, InternetNews, Small Business Computing, and many other tech and business publications.  She is an avid gamer with deep roots in the female gaming community and a former Internet TV gaming host and games journalist.