What is knowledge engineering?
Knowledge engineering is a branch of artificial intelligence (AI) that develops rules that are applied to data in order to imitate the thought process of a human that is an expert on a specific topic. This field of artificial intelligence attempts to emulate the judgment and behavior of a human expert in a particular field.
In its initial form, knowledge engineering focused on the transfer process; transferring the expertise of a problem-solving human into a program that could take the same data and make the same conclusions.
It was determined that transfer processing had its limitations, as it did not accurately reflect how humans make decisions. It did not consider intuition and gut feeling, known as analogous reasoning and nonlinear thinking, that often may not be logical. Another reason why the transfer approach was not successful was that the systems did not have the collateral information (information that is not central to a specific issue or field but is still useful in making judgements and decisions) which human specialists and experts had.
Today, knowledge engineering uses a modeling process that creates a system that touches upon the same results as the expert without following the same path or using the same information sources.
The goal of knowledge engineering is for it to be implemented into software that will make decisions that human experts would, such as financial advisors.
Knowledge engineering is already being used in decision support software and it is expected that at some point it will be used to make better decisions than human experts. This is the technology that is used to build expert systems that assist humans with issues pertinent to the field of knowledge for which they are programmed. Knowledge engineering systems might also make use of machine learning to make it possible for these systems to learn from their experiences in a manner similar to the way humans learn.
Why do we need knowledge engineering?
In terms of its role in AI, knowledge engineering is the process of understanding and then representing human knowledge in data structures; semantic models, a conceptual diagram of the data as it relates to the real world; and heuristics, rules that lead to solutions to every problem taken in AI. Expert systems and algorithms are examples that form the basis of the representation and application of this knowledge. The knowledge engineering process includes knowledge acquisition, representation, and validation; inferencing; and explanation and justification.
The amount of collateral knowledge can be very large depending on the task. A number of advances in technology and technology standards have assisted in integrating data and making it accessible. These include the semantic web, an extension of the current web in which information is given a well-defined meaning; cloud computing, which enables access to large amounts of computational resources; and open datasets, freely available datasets for anyone to use and republish. These advances are crucial to knowledge engineering as they expedite data integration and evaluation.
What is the knowledge engineering process?
Knowledge Engineering for different domains is different but it follows, the same set of procedures in order to create expert systems.
1. Task Identification
This is the first step in the knowledge engineering process where the task to be performed is defined. In a domain, a specific problem or a combination of several problems would be taken. This task must be realistic and the subject matter expert needs to have a clear idea of what it is so that further process can be carried out.
2. Acquisition of Knowledge
Once the problem is well defined then the next step is to gather relevant knowledge and information about the problem. For some problems standard data is used that must be collected, for example, a problem on heat exchanger requires the standard steam table data at x temperature and y pressure what will be the value of enthalpy.
3. Prepare a road map
Once the goal and knowledge base are available the next step is to get the roadmap ready by breaking the goal down into small steps by questionnaires and relevant knowledge base. Here the subject matter expert puts his thoughts on how he would make decisions and what parameters would be considered at all stages. There could be several ways to solve some problems, and all should be considered.
4. Encode
Now it’s time to convert this knowledge into computer language. Here the knowledge is encoded by using different functions as well as in some cases, for a specific task, the algorithm is used to create a model. These models are able to make decisions based on available parameters as an expert does, surely the model must be trained and tested on a sufficient amount of data.
5. Evaluation and Debugging
In the process of creating an expert system, at each step, the model should be evaluated and debugged and then added to workflow. Once all small tasks are evaluated, they are assembled to create one whole expert system. This system is again evaluated on similar problems and Debugged if any issue is there.
What are the benefits of knowledge engineering?
In today’s world, people do not want to spend time waiting for a human expert to be available to resolve their issues. They want answers to their queries instantly and that’s where expert systems built with knowledge engineering can shine.
Here are the benefits of knowledge engineering:
- It helps in creating better, smarter, more effective expert systems.
- It is possible to pull knowledge from various domains together to handle complicated issues.
- Expert systems are models that are created using knowledge engineering processes tend to be more robust.
- When built with Natural Language Programming (NLP) and knowledge engineering together, expert systems have the ability to read the queries and provide answers and solutions, in a manner somewhat similar to the way a chatbot behaves.
