Ontology alignment using relative entropy for semantic uncertainty analysis

The development and use of many diverse ontologies to support the representational needs of different sources and different contexts is common and necessary. However, the increased sharing of databases implementing heterogeneous ontologies pose the problem of ontological alignment. Ontology alignment typically consists of manual operations from users with different experiences and understandings and limited reporting is conducted in the quality of mappings. To assist the International Organization for Standards (ISO) in standards development for information and data quality assessment, we propose an approach using relative entropy for semantic uncertainty analysis. Information theory has widely been adopted and provides uncertainty assessment for quality of service (QOS) analysis. Quality of information (QOI) or Information Quality (IQ) definitions for semantic assessment can be used to bridge the gap between ontology (semantic) uncertainty alignment and information theory (symbolic) analysis. Pragmatically aiding users of the shared ontologies requires assessments of the cognitive mental models, recognition of semantic classifications, and action over timeliness, throughput, confidence, and accuracy of the translations. In this paper, we explore issues of ontology uncertainty alignment utilizing the elements of information theory (KL divergence or relative entropy). A maritime domain situational awareness example with ship semantic labels is shown to demonstrate ontology alignment uncertainty assessment for data quality standards to assist users for pragmatic surveillance.