![]() ![]() ![]() Comparison experimentation with existing system and evaluation experimentation with expert knowledge show that this method is specifically helpful for spatiotemporal data discovery. ![]() 2) Based on the search history of a user, the Bayesian network model can judge which class has the biggest probability to be recommended. This model can address the following two objectives: (1) Given one class in the ontology, the model can judge which class has the biggest likelihood to be selected for recommendation. We created a Bayesian network model for inference based on ontologies. Using the search history, the posterior probability between each subclass and their super class in the ontologies was calculated, indicating a recommendation likelihood. From the historical search log, major keywords were extracted and organized according to ontologies in a hierarchical structure. The source data of this research was from the MUDROD (Mining and Utilizing Dataset Relevancy from Oceanographic Datasets) search platform. This paper presented a content-based recommendation method, and applied Bayesian networks and ontologies into the vocabulary recommendation process for spatiotemporal data discovery. In the research field of spatiotemporal data discovery, how to utilize the semantic characteristics of spatiotemporal datasets is an important topic. Combining the strenght of the two allows to improve both the reasoning under uncertainty and the expert knowledge. ![]() We developped three algorithms throught three distinct approaches, whose main differences lie in their automatisation and the integration (or not) of human expert supervision.The originality of our work is the combination of two broadly opposed philosophies: while the Bayesian approach favors the statistical analysis of the given data in order to reason with it, the ontological approach is based on the modelization of expert knowledge to represent a domain. Our aim is to complement the statistical learning with expert knowledge in order to learn a model as close as possible to the reality and analyze it quantitatively (with probabilistic relations) and qualitatively (with causal discovery). Our goal is to guide the probabilistic relations’ learning with expert knowledge for domains described by ontologies.To do so we propose to couple knowledge bases (KBs) and an oriented-object extension of Bayesian networks, the probabilistic relational models (PRMs). This thesis focuses on integrating expert knowledge to enhance reasoning under uncertainty. ![]()
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