|Title||Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases|
|Publication Type||Conference Paper|
|Year of Publication||2013|
|Authors||Pramod Anantharam, Krishnaprasad Thirunarayan, Amit Sheth|
|Conference Name||2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM 2013)|
|Conference Location||Austin, Texas|
|Keywords||cyber physical systems, Declarative Knowledge, Graphical models, probabilistic modeling, traffic analytics|
Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore pre-existing 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.