Knowledge Extraction

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

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.

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:

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:

• Data mining
• Deep learning
• Entity recognition
• Feature extraction
• Graph-based extraction
• Machine learning
• Natural language processing
• Ontology-based extraction
• Relationship extraction
• Rule-based extraction
• Semantic analysis
• Sentiment analysis
• Spatial data analysis
• Text analytics

Types of Data Sources Used in Knowledge Extraction

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:

• Bioinformatics
• Business intelligence (BI)
• Chatbot platforms
• Content recommendation systems
• Data analytics
• E-commerce
• Financial analysis
• Healthcare
• Knowledge bases
• Market research
• Predictive analytics
• Semantic search engines
• Social media analysis

Knowledge Extraction Pros and Cons

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.

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