Design Principles for Ontological Support of Bayesian Evidence Management

Authors:
Michael N. Huhns;Marco G. Valtorta;Jingsong Wang
Affiliations:
University of South Carolina, Columbia, SC, USA;University of South Carolina, Columbia, SC, USA;University of South Carolina, Columbia, SC, USA
Venue:
Proceedings of the 2010 conference on Ontologies and Semantic Technologies for Intelligence
Year:
2010

Citing 6
Cited 1

Causality: models, reasoning, and inference

Causality: models, reasoning, and inference
A Probabilistic Extension to Ontology Language OWL

HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 4 - Volume 4
OmniSeer: A Cognitive Framework for User Modeling, Reuse of Prior and Tacit Knowledge, and Collaborative Knowledge Services

HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 09
IDENTIFIABILITY IN CAUSAL BAYESIAN NETWORKS: A GENTLE INTRODUCTION

Cybernetics and Systems
P-CLASSIC: a tractable probablistic description logic

AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models

UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence

On the combination of logical and probabilistic models for information analysis

Applied Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This chapter describes work on an integrated system that can assist analysts in exploring hypotheses using Bayesian analysis of evidence from a variety of sources. The hypothesis exploration is aided by an ontology that represents domain knowledge, events, and causality for Bayesian reasoning, as well as models of information sources for evidential reasoning. We are validating the approach via a tool, Magellan, that uses both Bayesian models and logical models for an analyst's prior knowledge about how evidence can be used to evaluate hypotheses. The ontology makes it possible and practical for complex situations of interest to be modeled and then analyzed formally.