- K-fold cross-validation
- K-nearest neighbors algorithm
- Kalman filtering
- Kernel density estimation
- Kernel methods
- Kernel trick
- Key-frame-based action recognition
- Key-frame-based video summarization
- Keyframe extraction
- Keyphrase extraction
- Keyword spotting
- Kinect sensor-based human activity recognition
- Kinematic modeling
- Knowledge discovery
- Knowledge engineering
- Knowledge extraction
- Knowledge graph alignment
- Knowledge graph completion
- Knowledge graph construction
- Knowledge graph embedding
- Knowledge graph reasoning
- Knowledge graph visualization
- Knowledge graphs
- Knowledge graphs for language understanding
- Knowledge representation and reasoning
- Knowledge transfer
- Knowledge-based systems
- Kullback-Leibler divergence
What is Knowledge engineering
Knowledge Engineering
Knowledge Engineering (KE) is a subfield of Artificial Intelligence (AI) that deals with the development of knowledge-based systems. This field focuses on designing, developing, and maintaining intelligent systems that can reason, learn, and solve problems similar to human beings. KE involves the application of knowledge representation, reasoning, and decision-making to develop intelligent systems that can automate tasks and provide intelligent solutions.
History of Knowledge Engineering
Knowledge Engineering is a relatively new field of study that emerged in the early 1980s. It was developed as a response to the growing need for intelligent systems that can solve complex real-world problems. The development of KE is closely related to the development of Expert Systems (ES), which were the first successful applications of AI. Expert Systems were designed to mimic the decision-making capabilities of human experts in a particular domain. The development of Expert Systems required the representation of expert knowledge in a structured and formalized form. As a result, the field of KE was born, which focused on designing and implementing knowledge-based systems.
Knowledge Representation
Knowledge Representation is a critical component of KE, which involves the representation of knowledge in a structured and formalized way. The representation of knowledge is necessary to enable machines to reason, learn, and solve problems like humans. There are various knowledge representation techniques used in KE, which include; Rule-based systems, Frame-based systems, Ontologies, Semantic networks, and Production systems.
Rule-based Systems
Rule-based systems are a common knowledge representation technique used in KE. This technique involves representing knowledge in the form of a set of rules. Rules consist of two parts; an antecedent or condition, and a consequent or action. The antecedent specifies the conditions that must be satisfied for the rule to be triggered, and the consequent specifies the action that should be taken when the rule is triggered. Rule-based systems are commonly used in expert systems, where the rules represent the knowledge of human experts in a particular domain.
Frame-based Systems
Frame-based systems are another knowledge representation technique used in KE. This technique involves representing knowledge in the form of frames, which are similar to data structures. Frames consist of slots, which represent attributes of the object, and values, which represent the specific values of the attributes. Frame-based systems are commonly used in natural language processing, where frames represent the meaning of words and phrases.
Ontologies
Ontologies are a knowledge representation technique used in KE, which involves the formal definition of concepts and their relationships. An ontology represents a shared understanding of a domain, which can be used to reason about the domain. Ontologies are commonly used in Semantic Web applications, where they provide a formalized structure for representing knowledge.
Semantic Networks
Semantic networks are a knowledge representation technique used in KE, which involves representing knowledge in the form of a graph. The nodes of the graph represent concepts, and the edges represent relationships between the concepts. Semantic networks are commonly used in natural language processing, where they represent the meaning of words and phrases.
Production Systems
Production systems are a knowledge representation technique used in KE, which involves representing knowledge in the form of a set of rules and facts. The rules are condition-action rules, which specify the actions to be taken when the conditions are met. The facts are statements about the state of the world. Production systems are commonly used in expert systems, where they represent knowledge about a particular domain.
Knowledge Engineering Tools
There are various tools and technologies used in KE, which include; knowledge acquisition tools, knowledge representation tools, knowledge validation tools, and knowledge maintenance tools. Knowledge acquisition tools are used to elicit knowledge from human experts and represent them in a formalized form. Knowledge representation tools are used to represent knowledge in a structured and formalized way. Knowledge validation tools are used to validate the knowledge represented in a knowledge-based system, to ensure that it is accurate, complete, and consistent. Knowledge maintenance tools are used to maintain the knowledge represented in a knowledge-based system, to ensure that it remains up-to-date and relevant.
Applications of Knowledge Engineering
KE has various applications in different domains, which include; healthcare, finance, business, education, and government. In healthcare, KE is used to develop intelligent systems that can assist doctors in diagnosis, treatment, and patient management. In finance, KE is used to develop intelligent systems that can perform tasks such as fraud detection, portfolio management, and risk analysis. In business, KE is used to develop intelligent systems that can automate tasks such as customer service, inventory management, and supply chain management. In education, KE is used to develop intelligent systems that can assist students in learning and problem-solving. In government, KE is used to develop intelligent systems that can assist in policy development and decision-making.
Challenges in Knowledge Engineering
Despite the significant progress made in KE, there are still several challenges that need to be addressed, which include; knowledge acquisition, knowledge representation, knowledge integration, and knowledge maintenance. Knowledge acquisition involves eliciting knowledge from human experts and representing them in a formalized form, which can be a challenging task. Knowledge representation involves representing knowledge in a structured and formalized way, which can be difficult, especially for complex domains. Knowledge integration involves integrating knowledge from different sources and domains, which can be a challenging task. Knowledge maintenance involves maintaining the knowledge represented in a knowledge-based system, to ensure that it remains up-to-date and relevant, which can be a never-ending task.
Conclusion
Knowledge Engineering is a subfield of Artificial Intelligence that focuses on developing intelligent systems that can reason, learn, and solve problems similar to humans. KE involves the application of knowledge representation, reasoning, and decision-making to develop intelligent systems that can automate tasks and provide intelligent solutions. There are various knowledge representation techniques used in KE, which include rule-based systems, frame-based systems, ontologies, semantic networks, and production systems. KE has various applications in different domains, including healthcare, finance, business, education, and government. However, there are still several challenges that need to be addressed in KE, including knowledge acquisition, knowledge representation, knowledge integration, and knowledge maintenance. Overall, KE has significant potential to revolutionize various domains and improve human lives, and continued research in this field is necessary to realize its full potential.