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About 10,000 papers for “knowledge representation”

Knowledge representation

Bittencourt, Guilherme, Centre National de la Recherche Scientifique (CNRS), 38 - Saint-Martin-d'Heres (France). Lab. d'Informatique Fondamentale et d'Intelligence Artificielle · OpenGrey (Institut de l'Information Scientifique et Technique) · 1988

Cited by 0

Knowledge representation

Brachman, Ronald J. 1949-, Levesque, Hector J. 1951-, Reiter, Raymond 1939-2002 · 1992

Cited by 9

KNOWLEDGE REPRESENTATION

Husnara khan, Daljeet Kaur · 2025

The demand for effective Knowledge Representation has surged alongside the rise of smart technologies. This process encompasses gathering data, conveying information, and accessing accurate knowledge. It's evident that a straightforward and lucid representation model is crucial for efficient data capture and retrieval. There's now a pressing need for enhanced knowledge representation models to align with contemporary needs. This research delves into the evolution of representation models throughout history. Its primary aim is to synthesize the advancements made in knowledge representation models and explore various methodologies approaches utilized in this field. and In the realms of artificial intelligence and natural language processing, knowledge representation stands as a pivotal element. Its symbiotic relationship with automated reasoning underscores its significance, as effective representation facilitates coherent reasoning. Scholars in the domain of knowledge representation and reasoning have devised techniques and methodologies that serve as cornerstone advancements in computer science. These innovations have fostered substantial progress across a spectrum of practical domains, from natural language processing to robotics and software engineering. Yet, there remains a call for further inquiry to empower a more proactive role in steering the reasoning process through the framework of knowledge representation. This discourse has explored the intricacies of knowledge representation and reasoning, scrutinizing the key challenges and emergent opportunities that novel research in this realm has engendered.

Cited by 0

Knowledge representation

Hjørland, Birger · UA Campus Repository (The University of Arizona) · 2006

This is one example of concepts introduced in the free source: The Epistemological Lifeboat. Epistemology and Philosophy of Science for Information Scientists. Birger Hjørland & Jeppe Nicolaisen (eds.) - Web design: Pernille Brandt.

Cited by 0

Knowledge Representation

Cecil Eng Huang Chua, Veda C. Storey, Roger H.L. Chiang · Journal of Database Management · 2012

Substantial work in knowledge engineering has focused on eliciting knowledge and representing it in a computational form. However, before elicited knowledge can be represented, it must be integrated and transformed so the knowledge engineer can understand it. This research identifies the need to separate knowledge representation into human comprehension and computational reasoning and shows that this will lead to better knowledge representation. Modeling of human comprehension is called conceptual knowledge representation. The Conceptual Knowledge Representation Scheme is developed and validated by conducting a combined qualitative/quantitative repeated-measures experiment comparing the Conceptual Knowledge Representation Scheme to two computation-oriented ones. The results demonstrate that the Conceptual Knowledge Representation Scheme better facilitates human comprehension than existing representation schemes. Four principles of the Conceptual Knowledge Representation Scheme emerge that help to attain effective knowledge representation. These are: (1) a focus on human comprehension only, (2) design around natural language, (3) addition of constructs common in the domain, and (4) constructs for representing abstract versions of detailed concepts.

Cited by 17

Knowledge Representation

Paul Taylor · 2006

This chapter contains sections titled: Developing the Web Ontologies in health informatics Building an ontology Description logics Modelling health care GALEN OpenEHR NHS Health Care Modelling Programme Ontologies and standards

Cited by 3

KNOWLEDGE REPRESENTATION

Husnara khan, Daljeet Kaur · 2025

The demand for effective Knowledge Representation has surged alongside the rise of smart technologies. This process encompasses gathering data, conveying information, and accessing accurate knowledge. It's evident that a straightforward and lucid representation model is crucial for efficient data capture and retrieval. There's now a pressing need for enhanced knowledge representation models to align with contemporary needs. This research delves into the evolution of representation models throughout history. Its primary aim is to synthesize the advancements made in knowledge representation models and explore various methodologies approaches utilized in this field. and In the realms of artificial intelligence and natural language processing, knowledge representation stands as a pivotal element. Its symbiotic relationship with automated reasoning underscores its significance, as effective representation facilitates coherent reasoning. Scholars in the domain of knowledge representation and reasoning have devised techniques and methodologies that serve as cornerstone advancements in computer science. These innovations have fostered substantial progress across a spectrum of practical domains, from natural language processing to robotics and software engineering. Yet, there remains a call for further inquiry to empower a more proactive role in steering the reasoning process through the framework of knowledge representation. This discourse has explored the intricacies of knowledge representation and reasoning, scrutinizing the key challenges and emergent opportunities that novel research in this realm has engendered.

Cited by 0Open access

Knowledge representation

David C. Yen, William S. Davis · 2019

This chapter describes typical expert system applications and briefly describes knowledge representation techniques. The procedure/subroutine approach is good for representing heuristic knowledge, modeling complex meta-problems, and performing extended logical inferences and reasoning. The purpose of knowledge representation is to organize the required information into a form that the expert system can readily access for decision making, planning, recognizing objects and situations, analyzing scenes, drawing conclusions, and other cognitive functions. Using direct representation, certain properties of a new situation can be described by reference to a known situation, and the known situation’s recommended course of action can be borrowed by an expert system faced with a new but similar situation. In the procedure/subroutine approach, knowledge about the world is contained in procedures, small programs that know how to do specific things. The property lists approach uses objects and lists of their properties to describe the state of the world.

Cited by 1