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Memory and Training
Published in Christopher D. Wickens, Justin G. Hollands, Simon. Banbury, Raja. Parasuraman, Engineering Psychology and Human Performance, 2015
Christopher D. Wickens, Justin G. Hollands, Simon. Banbury, Raja. Parasuraman
Once user’s knowledge is acquired, how is it best represented? One technique that has been especially successful with respect to training is conceptual graph analysis (CGA; Gordon, Schmierer, & Gill, 1993). A conceptual graph uses nodes and links of different types to characterize the user’s knowledge of a system. Gordon et al. used CGA to develop an instructional text for a topic in engineering dynamics. First, a document written by an expert was constructed as a conceptual graph. After construction, the graph was translated into a standard text format. Students using this knowledge-engineered text solved more dynamics problems than students who received the original text.
Introduction to Expert Systems
Published in Chris Nikolopoulos, Expert Systems, 1997
A conceptual graph is a directed, connected graph whose nodes and arcs represent concepts, concept instances (called referents), conceptual relations and actors. The first three represent declarative type of knowledge, while an actor represents procedural knowledge. An actor is a process whose input and output are concepts and which can change the referents of the output concepts based on the input concepts.
From CAD assemblies toward knowledge-based assemblies using an intrinsic knowledge-based assembly model
Published in Computer-Aided Design and Applications, 2018
Harold Vilmart, Jean-Claude Léon, Federico Ulliana
To be able to describe, store, and process knowledge, a knowledge base, JENA, developed by Apache, is connected to MyProductFabrica to describe the ontology as RDF triplets stored in a triple store. A reasoner to process inferences described as RDF triplets, CoGUI, developed by GraphiK Inria team (see Figure 4) is connected to the knowledge base. Indeed, the reasoner can be substituted by other equivalent modules, e.g., the inference engine GRAAL, developed by GraphiK Inria team, or other inference engines to process RDF triplets. CoGUI, however, is not only a reasoner but it contains also conceptual graph editing capabilities. Equivalently, the architecture could use Protégé [31] rather than CoGUI.
Capturing simulation intent in an ontology: CAD and CAE integration application
Published in Journal of Engineering Design, 2019
Flavien Boussuge, Christopher M. Tierney, Harold Vilmart, Trevor T. Robinson, Cecil G. Armstrong, Declan C. Nolan, Jean-Claude Léon, Federico Ulliana
Then, the CoGUI reasoner infers the new RDF triples from the set of pre-defined rules (modelling practices). The new symbolic information automatically generated by the reasoner is then used to drive the automatic geometric transformation operators in the CAD/CAE software (see Section 4). In addition to the CoGUI reasoner capability, a conceptual graph editing tool is available to design the inference rules replicating the CAD to CAE pre-processing practices (see Appendix). Changing these rules allows the user to quickly test and iterate different modelling practices. Figure 14 summarises the software architecture used in this paper.
From natural language text to rules: knowledge acquisition from formal documents for aircraft assembly
Published in Journal of Engineering Design, 2019
N. Madhusudanan, Balan Gurumoorthy, Amaresh Chakrabarti
The above authors also described several supervised, semi-supervised and bootstrapping methods for relation extraction. Khoo, Chan, and Niu (2000) used a linear conceptual graph format and looked for patterns indicating causality.