Knowledge Acquisition

Knowledge acquisition is the process used to define the rules and ontologies required for a knowledge-based system. The phrase was first used in conjunction with expert systems to describe the initial tasks associated with developing an expert system, namely finding and interviewing domain experts and capturing their knowledge via rules, objects, and frame-based ontologies.

Expert systems were one of the first successful applications of artificial intelligence technology to real world business problems. Researchers at Stanford and other AI laboratories worked with doctors and other highly skilled experts to develop systems that could automate complex tasks such as medical diagnosis. Until this point computers had mostly been used to automate highly data intensive tasks but not for complex reasoning. Technologies such as inference engines allowed developers for the first time to tackle more complex problems.

As expert systems scaled up from demonstration prototypes to industrial strength applications it was soon realized that the acquisition of domain expert knowledge was one of if not the most critical task in the knowledge engineering process. This knowledge acquisition process became an intense area of research on its own. One of the earlier works on the topic used Batesonian theories of learning to guide the process.

One approach to knowledge acquisition investigated was to use natural language parsing and generation to facilitate knowledge acquisition. Natural language parsing could be performed on manuals and other expert documents and an initial first pass at the rules and objects could be developed automatically. Text generation was also extremely useful in generating explanations for system behavior. This greatly facilitated the development and maintenance of expert systems.

A more recent approach to knowledge acquisition is a re-use based approach. Knowledge can be developed in ontologies that conform to standards such as the Web Ontology Language (OWL). In this way knowledge can be standardized and shared across a broad community of knowledge workers. One example domain where this approach has been successful is bioinformatics.

Semantic Networks

According to semantic network models, knowledge is organized based on meaning, such that semantically related concepts are interconnected. Knowledge networks are typically represented as diagrams of nodes (i.e., concepts) and links (i.e., relations). The nodes and links are given numerical weights to represent their strengths in memory.

Types of Knowledge

There are numerous types of knowledge, but the most important distinction is between declarative and procedural knowledge. Declarative knowledge refers to one’s memory for concepts, facts, or episodes, whereas procedural knowledge refers to the ability to perform various tasks. Knowledge of how to drive a car, solve a multiplication problem, or throw a football are all forms of procedural knowledge, called procedures or productions. Procedural knowledge may begin as declarative knowledge, but is proceduralized with practice. For example, when first learning to drive a car, you may be told to “put the key in the ignition to start the car,” which is a declarative statement. However, after starting the car numerous times, this act becomes automatic and is completed with little thought. Indeed, procedural knowledge tends to be accessed automatically and require little attention. It also tends to be more durable (less susceptible to forgetting) than declarative knowledge.

Knowledge Acquisition process

The raw materials of knowledge refer to the raw materials that you can use to build knowledge. The following paragraphs explain the primary forms of the raw material of knowledge.

1. Data

The data indicates the facts about things. In other words, it describes things. For example, length = 1.70 meters, weight = 80 kg, Old = 35 years. And so on.

2. Information

Data is a collection of individual facts without any relationship between them. Information refers to the process of extracting a relationship between specific facts.

When studying and analyzing data, discovering the relationship between that data is information. For example, someone who is 1.70 meters tall would be 35 years old.

3. Knowledge

As a result of the above, the organization obtains knowledge by linking information together and identifying recurring patterns.

The company considers this repetition of patterns as the basis for making various decisions. In short, this is the knowledge that organizations are looking for.

Knowledge refers to discovering a particular pattern through the study and analysis of information and data. The organization can use these patterns as bases for making various decisions.

Steps of knowledge acquisition process

1. Data gathering

Data gathering is the first step in knowledge acquiring. ِFirst, you to determine what data will be collected, how, and where this data is located. In other words, planning before anything else.

Determining the type of data required and the accuracy and correctness of this data significantly affect the quality and accuracy of the knowledge. Therefore, You should implement this step well.

Also, knowledge acquisition can depend on any data previously collected for another purpose. For example, you can rely on customer purchasing data. Or the financial statements of the organization. etc.

2. Data Organizing

In the previous step, the data state is often not well organized. Therefore, the knowledge management team must rearrange the data to obtain valuable information from it in this step.

For example, the data with a relationship are collected in one place, like collecting customer data from a specific geographic area. Collecting data for each age group of customers together, and so on.

Usually, the knowledge team stores the information in dedicated databases. Then, the knowledge team applies many different information technology methods to organize this data.

3. Summarizing

In this step, various statistics are extracted from databases. These statistics are presented in tables and graphs in multiple forms.

It should be noted here that upon completion of this step, the raw data has been converted into information that can be used further.

4. Analyzing

The information is analyzed, looking for recurring patterns that can be considered a new characteristic or a new knowledge.

For example, you might find that a particular age group of customers is interested in a specific product. For example, you can get a result that people between the ages of 8-28 are interested in buying video games. Another example, you notice that in a particular geographic region, the majority of customers are women. And so on.

5. Synthesizing

This step depends on joining statistics and patterns and coming out of them with fixed concepts that can be relied upon. These concepts are the knowledge you are looking for.

Knowledge management stores these results in rules or laws in the organization’s knowledge databases. So that everyone who wants it can reach it.

After this stage, knowledge management completes other knowledge management processes such as storing and distributing knowledge.