Enabling contextualized knowledge description with non-symbolic integration

Abstract

Formal knowledge descriptions have been one of the main tools for enabling knowledge representation and reasoning in AI. The W3C Semantic Web initiative proposed a set of standards, including on a common data interchange format that enabled greater interoperability and integration between knowledge descriptions generated by different organizations in the academy and industry. Nonetheless, these standards have been unable to provide contextualized integration of knowledge descriptions effectively, as they do not provide constructs to explicitly define contextualized n-dimensional (e.g. time and space, n-ary and hierarchical facts, etc.) relationships between concepts from different descriptions, leading to inefficiencies in the reasoning and query processing over decontextualized relationships, and inconsistencies in the modeling approaches utilized for dealing with this limitation. The main goal of this demo is to present how a hybrid knowledge representation model and description language, namely Hyperknowledge, allows contextualized integration of knowledge descriptions using constructs that enable the explicit definition of rich contextual relationships. This demonstration will be performed using the Knowledge Explorer System (KES), that supports visualization and management of the effective contextualization of Hyperknowledge expressivity w.r.t. n-dimensional relationships between concepts from multiple descriptions and between those concepts with their corresponding non-symbolic content.