Knowledge Reasoning: Fundamentals and Applications

By Fouad Sabry

What Is Knowledge Reasoning

Knowledge representation and reasoning is a subfield of artificial intelligence (AI) that is devoted to the challenge of describing information about the world in a format that a computer system can use to solve complex problems, such as diagnosing a medical condition or having a conversation in a natural language. Examples of complex problems that can be solved by knowledge representation and reasoning include diagnosing a medical condition and having a conversation in a natural language. In order to construct formalisms that will make it easier to design and build complicated systems, knowledge representation combines discoveries from the field of psychology regarding how humans solve issues and represent knowledge. The application of rules and the interactions between sets and subsets are two examples of the forms of reasoning that can be automated with the help of knowledge representation and reasoning, which also incorporates results from logic.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Knowledge representation and reasoning

Chapter 2: Knowledge management

Chapter 3: Semantic technology

Chapter 4: Knowledge graph

Chapter 5: Logico-linguistic modeling

Chapter 6: Conceptual graph

Chapter 7: Commonsense knowledge (artificial intelligence)

Chapter 8: Ontology engineering

Chapter 9: Knowledge-based systems

Chapter 10: Functional completeness

(II) Answering the public top questions about knowledge reasoning.

(III) Real world examples for the usage of knowledge reasoning in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of knowledge reasoning' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of knowledge reasoning.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 4, 2023

Book preview

Chapter 1: Knowledge representation and reasoning

The representation and reasoning of knowledge (sometimes abbreviated as KRR), KR&R, KR²) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language.

In order to construct formalisms that will make it simpler to design and build complicated systems, knowledge representation includes discoveries from the field of psychology into the ways in which people solve issues and represent information.

In addition, the results of logic studies are incorporated into knowledge representation and reasoning in order to automate different types of reasoning, include things like the execution of rules and the connections between sets and subsets.

Semantic nets, system architecture, frames, rules, and ontologies are a few examples of several types of formalisms that may be used to describe knowledge. Inference engines, theorem provers, and classifiers are all examples of automated reasoning engines.

The earliest work in computerized knowledge representation was focused on general problem-solvers like the General Problem Solver (GPS) system, which was developed by Allen Newell and Herbert A. Simon in 1959. Other early work in computerized knowledge representation focused on specific problem domains. These systems included data structures for planning and deconstruction in their functionality. A objective would serve as the starting point for the system. After that, it would break that aim down into a series of smaller goals, and then it would work to devise tactics that would allow it to achieve each of those smaller goals.

During this early period of artificial intelligence development, broad search algorithms like A* were also developed. However, because of the vague nature of the problems that needed to be solved, technologies like GPS could only be made to function well in very limited play domains (e.g. the blocks world). Researchers in artificial intelligence, such as Ed Feigenbaum and Frederick Hayes-Roth, came to the realization that in order to solve non-toy issues, it was important to concentrate systems on problems with more constraints.

These efforts led to the cognitive revolution in psychology and to the phase of artificial intelligence that focused on knowledge representation. This phase of AI resulted in the development of expert systems in the 1970s and 1980s, production systems, frame languages, and a variety of other innovations. Expert systems that could match human expertise on a specialized job, such as medical diagnosis, were the primary emphasis of artificial intelligence research and development rather than broad problem solvers.

During the middle of the 1980s, a number of scholars independently explored the idea of frame-based languages in addition to expert systems. A frame is analogous to an object class in that it is an abstract description of a category that describes objects that exist in the world, as well as issues and possible solutions to those problems. Frames were first used on systems that were designed to interact with humans, such as those that needed to understand natural language or social settings in which various default expectations, such as placing an order for food in a restaurant, narrowed the search space and allowed the system to choose appropriate responses to dynamic situations.

It did not take long at all before both the rule-based researchers and the members of the frame groups discovered that there was a connection between the two methodologies. Frames were useful for portraying the actual world because they could be specified as classes, subclasses, and slots (data values) with a variety of limits on what values might be used. The process of making a medical diagnosis is an example of a situation that might benefit from the representation and application of rules. The development of integrated systems that merged frameworks and rules led to this. One of the most effective and well-known was Intellicorp's Knowledge Engineering Environment (KEE) from 1983. This environment was first introduced. KEE included a fully functional