Document Type
Conference Proceeding
Publication Date
2013
Abstract
Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays.
Repository Citation
Anantharam, P.,
Thirunarayan, K.,
& Sheth, A. P.
(2013). Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases. Proceedings of the 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013), 13-20.
https://corescholar.libraries.wright.edu/knoesis/143
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Comments
This paper was presented at the 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.
This is the authors' version of the paper.