Research On Ontology Knowledge Uncertainty Reasoning Considering Temporal Information

Domain knowledge management is a current research hotspot. As an importantmodeling tool for knowledge model, ontology has made the organization of domainknowledge conform to a certain standard and has provided a good platform for domainknowledge management. Because the knowledge and information people obtained maycontain uncertainty, so ontology knowledge uncertainty reasoning is based on theuncertain knowledge and evidence. In many practical applications, temporal informationis a must for ontology knowledge model and temporal information may contain timerestriction relationship and uncertainty, so how to reason this kind of ontology knowledgeis a problem to be solved.The research uses the reasoning ability of Bayesian Network on ontology knowledgeuncertainty reasoning. But current ontology reasoning theories based on BayesianNetwork mainly focus on the state uncertainty of ontology concepts, which is widely usedin pathological diagnosis and fault diagnosis. But the researches are useless in theapplications in which time restriction relationship and time uncertainty are must, such asintention inference and situation assessment. The research uses this as a starting point anddoes some research on time ontology modeling, temporal dilation of Bayesian Networkand temporal ontology translation algorithm for Temporal Bayesian Network. Theachievements of the research on key theory analysis and implementation technologies areas follows:First, in order to express causal relationship and temporal information of ontology atthe same time, interval algebra is introduced into ontology modeling and a OWL CausalTemoral ontology is proposed which contains time restriction relationship and timeuncertainty. Second, interval algebra is also introduced into the time dilation of BayesianNetwork and a Temporal Bayesian Network(Ontology Temporal Bayesian Network, OTBN) is proposed which can be used for OWL Causal Temoral ontology uncertaintyreasoning. Third, the research proposed a temporal ontology translation algorithm forOTBN which can translate domain knowledge ontology model into OTBN. Last, theresearch designs and develops a prototype system for OWL Causal Temporal ontologyreasoning. The system architecture is given and the main functional modules areintroduced in detail. A military case is used to do the experiments.