Ontology alignment using relative entropy for semantic uncertainty analysis

Abstract

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.

Applying spatial mutual information to AIS data

“…For example, entropy has been applied to attribute matching between AIS messages and authoritative databases for the purpose of correcting attribute values in high-volume data streams (Horn et al, 2015). Information theory has also been used to assess the data quality of AIS messages (Iphar et al, 2015) and applied to the semantics in MDA ontologies, for the matching of vessel labels (Blasch et al, 2010).…”

An Information-Theoretic Analysis of Flexible Efficient Cognition for Persistent Sustainable Production

“…Hence, with regard to flexibility, labelling of X − needs to be related to those Y to which it is relevant. In doing so, mental models in ecosystems A (source of X − ) and B (source of Y ) need to be taken in account [ 59 ]. With regard to efficiency, side information provided by X − , needs to reconcile probabilistic prediction of task execution outcome ( p ) with what should happen in task execution ( q ) and so reduce relative entropy D ( p || q ) towards zero.…”

Ontologies for nextgen avionics systems

“…Some developments and applications of ontologies for information fusion include geospatial data alignment [68], semantic analysis [69], motion imagery [70,71], and knowledge management [72]. Using discussions from civil aviation [73] and airport security operations [74], we seek ways to integrate the information from mandates, regulations, and real time operations. We will demonstrate the use of the ontology for ATM combined with visualization extending our 2013 paper [75].…”