Applied logic and semantics on indoor and urban adaptive design through knowledge graphs, reasoning and explainable argumentation

Evangelos A. Stathopoulos, Alexandros Vassiliades, Sotiris Diplaris, Stefanos Vrochidis, Ioannis Kompatsiaris

Keywords: AI for Science; Vision and Learning

TL;DR: Journal (https://www.cambridge.org/core/journals/knowledge-engineering-review/article/applied-logic-and-semantics-on-indoor-and-urban-adaptive-design-through-knowledge-graphs-reasoning-and-explainable-argumentation/43DBA4976A5CAD3F32A389E42AE9FFDB)

Abstract:

In the previous two decades, knowledge graphs (KGs) have evolved significantly, inspiring developers to build
ever-more context-related KGs. Due to this development, artificial intelligence (AI) applications can now access
open domain-specific information in a format that is both semantically rich and machine comprehensible. In this
article, a framework that depicts functional design for indoor workspaces and urban adaptive design, in order to
help architects, artists, and interior designers for the design and construction of an urban or indoor workspace,
based on the emotions of human individuals, is introduced. For the creation of online adaptive environments, the
framework may incorporate emotional, physiological, visual, and textual measures. Additionally, an information
retrievalmechanism that extracts critical information from the framework in order to assist the architects, artists, and
the interior designers is presented. The framework provides access to commonsense knowledge about the (re-)design
of an urban area and an indoor workspace, by suggesting objects that need to be placed, and other modifications that
can be applied to the location, in order to achieve positive emotions. The emotions referred reflect to the emotions
experienced by an individual when being in the indoor or urban area, which are pointers for the functionality, the
memorability, and the admiration of the location. The framework also performs semantic matching between entities
from the web KG ConceptNet, using semantic knowledge from ConceptNet and WordNet, with the ones existing
in the KG of the framework. The paper provides a set of predefined SPARQL templates that specifically handle
the ontology upon which the knowledge retrieval system is based. The framework has an additional argumentation
function that allows users to challenge the knowledge retrieval component findings. In the event that the user prevails
in the reasoning, the framework will learn new knowledge.

Submission Number: 35