Towards A Topological Framework for Integrating Semantic Information Sources
Cliff Joslyn, Emilie Hogan, Michael D. Robinson · 2014
Abstract—In this position paper we argue for the role that Topological Data Modeling (TDM) principles can play in pro-viding a framework for sensor integration. While used success-fully in standard (quantitative) sensors, we are developing this methodology in new directions to make it appropriate specifically for semantic information sources, including keyterms, ontology terms, and other general Boolean, categorical, ordinal, and partially-ordered data types. Given pairwise information source integration principles, TDM can measure overall consistency, and most importantly, reveal cyclic dependencies amongst data sources where conflicts might not be able to be identified. We illustrate the basics of the methodology in an extended use case/example, and discuss path forward. I.