**1. Introduction**

246 Semantics – Advances in Theories and Mathematical Models

Yue, K. and Huber, D. and Akinci, B. and Krishnamurti, R. The ASDMCon project: The

1048-1055, 2006.

challenge of detecting defects on construction sites, *International Symposium on 3D Data Processing Visualization and Transmission,* IEEE Computer Society, Vol. 0, pp.

> This Chapter focuses on 'Command—causalities—consequences Wisdom' (C3W) semantics temporal entanglement modeling using 'Wisdom open system semantic intelligence' (WOSSI) methodology (Ronczka, 2009). A feature might be a Rubik–Schlangen type three– dimensional system and wisdom–based delivery engine acting as a continuum with engagements.

> WOSSI is a mapping system that allows identification of wisdom from lower order delivery engines and associated domains to acquire the information, knowledge, reasoning, and understanding whilst in an open–system context. WOSSI mapping has the outcome of minimising the influence of 'de Montaigne' paradoxes. That is, a possible negative outcome: '*nothing is so firmly believed as that which we least know'*, (Collins, 2002) that may drive conflicting tangibles and intangibles such as 'actual monetary benefits' and 'Willingness–to– pay' but still providing foresight based on evidence based 'knowledge—information learning delivery engines' (KILDEE's) for the user.

> A modified Semantics approach based on WOSSI provides a mapping process that could account for the many complexities that interfaces are required to adjust too such as data mapping of the associated Ontologies, taxonomy of Semantics User Interfaces and Semantic Adaptive Systems. Interfacing Semantic and Semiotic may assist when it is required for various inclusion of natural languages to adjust to the intended user but yet overcome any adverse informatics outcomes when translated for oher users.
