The paper presents a novel framework for the optimisation and synthesis of complex reactor networks that combines superstructure-based optimisation, semantic models and analytical tools.
There is ample evidence [ 21, 22] that analytical, semantic models alone do not fully explain diagnostic reasoning.
Semantic models (ontologies) and analytical tools are combined to simplify the superstructures and interpret solutions.
We describe a method and tool set for representing the analytical knowledge through semantic web ontologies that map between the metamodels of both the DSM and analytical tools.
The model incorporates concepts like emotions, drives, desires, perceptual memory, procedural memory, episodic memory, and semantic memory and provides significant schematical and analytical insights into processes taking place in the mind; this has been unseen so far in its clarity.
Without intending to minimize the utility and effectiveness of other text-analytical approaches, we propose that semantic network analysis becomes an invaluable and time-efficient tool for exposing subtle patterns of large text corpora.
One approach is to explore semantic similarity estimation as a basis of an analytical framework.
We propose a uniform spatial data access middleware that can provide a combination of high-level, multi-modal, semantic, and quantitative-qualitative spatial data access and analytical capability.
In addition to data representation, Semantic Web standards have also been applied to analytical tools, for example through the creation of Semantic Web services.
The Research Track solicited novel and significant research contributions addressing theoretical, analytical, empirical, and practical aspects of the Semantic Web.
