Results 241 to 250 of about 8,829,417 (294)

Knowledge Representation in EPNet

2015
Semantic technologies are rapidly changing the historical research. This paper focuses on the knowledge representation and data modelling initiative that has characterised the first year of the EPNet project in the context of the historical research. The so-called EPNet CRM and Ontology are introduced here, and put in connection with existing modelling
Alessandro Mosca 0001, José Remesal, Martin Rezk, Guillem Rull   +3 more
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SPATIAL KNOWLEDGE REPRESENTATION

International Journal of Pattern Recognition and Artificial Intelligence, 1989
The use of spatial knowledge is necessary in a variety of artificial intelligence and expert systems applications. The need is not only in tasks with spatial goals such as image interpretation and robot motion, but also in tasks not involving spatial goals, e.g. diagnosis and language understanding.
Sargur N. Srihari, Zhigang Xiang
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The Representation of Fuzzy Knowledge

Journal of Cybernetics, 1974
A new AI programming language (called FUZZY) is introduced which provides a number of facilities for efficiently representing and manipulating fuzzy knowledge. A fuzzy associative net is maintained by the system, and procedures with associated "procedure demons" may be defined for the control of fuzzy processes.
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And/or trees for knowledge representation

1995
Graph modelling is a modern branch of probability theory concerned with representations for the probability distributions as a product of functions of several variables as a base for possible ways to store high-dimensional distributions by means of a small number of parameters.
Luminita State, Radu State
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Psychology of knowledge representation

WIREs Cognitive Science, 2014
AbstractEvery cognitive enterprise involves some form of knowledge representation. Humans represent information about the external world and internal mental states, like beliefs and desires, and use this information to meet goals (e.g., classification or problem solving).
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Diagrammatic knowledge representation

IEEE Transactions on Systems, Man, and Cybernetics, 1992
Diagrams are used to facilitate problem solving in engineering, physics, geology, and other scientific areas. Diagrams store related elements adjacents to each other. For instance, it can be easily seen from a map that Iowa and Illinois are adjacent states, since they are placed next to each other.
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Knowledge representation support

Machine Translation, 1989
The representation of world knowledge in a machine translation system is discussed, especially in terms of the interlingua text that is adopted in KBMT-89. While knowledge about the world is at the core of a knowledge-based machine translation program, it cannot be extracted from a text without knowledge about how languages encode it syntactically. The
Sergei Nirenburg, Lori S. Levin
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Formal Representations of the Knowledge

2020
This paper, describes the problem of the representation of the knowledge. After a brief discussion on the phase of coding - decoding are mentioned some of the characteristics and conditions that must assemble all scheme of representation of the knowledge in artificial intelligence (AI).
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Knowledge representation as domain

Journal of Applied Non-Classical Logics, 1997
ABSTRACT This is a continuing attempt in a series of papers [KM 93, Mur 93, Mur 96] to show how computer-represented knowledge can be arranged as elements of an effectively represented semantic (or algebraic) domain in the sense of [GS 90]. We present a direct deductive description of the domain, which was defined semantically in [KM 93], via the Scott'
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Representations of metabolic knowledge.

Proceedings. International Conference on Intelligent Systems for Molecular Biology, 1995
Construction of electronic repositories of metabolic information is an increasingly active area of research. Encoding detailed knowledge of a complex biological domain requires finely honed representations. We survey representations used for several metabolic databases, including Eco-Cyc, and reach the following conclusions.
Peter D. Karp, Monica Riley
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