Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Explaining inferences in Bayesian networks
Applied Intelligence
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Explaining answers from the Semantic Web: the Inference Web approach
Web Semantics: Science, Services and Agents on the World Wide Web
Explaining conclusions from diverse knowledge sources
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Characterizing Communities of Practice in Emerging Science and Technology Fields
SOCIETY '13 Proceedings of the 2013 International Conference on Social Intelligence and Technology
Modeling Debate within a Scientific Community
SOCIETY '13 Proceedings of the 2013 International Conference on Social Intelligence and Technology
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Analysts who are interested in quickly identifying new and emerging scientific advancements have numerous challenges as the breadth, depth, and volume of scientific literature increases. Network analysis and mining is key to the success in this task. The ARBITER system seeks to identify indicators of emergence and provide a system that is capable of analyzing corpora of full text and metadata to identify emerging science topics and explain its reasoning and conclusions. In this paper, we describe a network-modeling framework that is used in the ARBITER system, and describe our novel hybrid approach using probabilistic foundations in combination with semantic technology and introduce our explanation infrastructure. We include a discussion of some challenges and opportunities related to explaining hybrid approaches to indicator-based analysis and emergence detection.