Towards a Categorical Matching Method to Process High-Dimensional Emergency Knowledge Structures.

To keep the original semantic information, Textual data in emergency knowledge acquisitions can be actually represented in categorical semantic structures based on typed category theory. These netted topological structures preserve high-dimensions to achieve higher reliability of knowledge processing that relates to the various scenarios of emergency responses. This paper presents a categorical matching method for effective processing on such high-dimensional structures of textual data. The quantification of the matching is achieved through the Greatest Common Subcategory between two categorical structures. Simulated experimental results show a reasonable matching rate for the semantic oriented high-dimensional knowledge processing. [ABSTRACT FROM AUTHOR]