Building Reliable Activity Models Using Hierarchical Shrinkage and Mined Ontology

Abstract. Activity inference based on object use has received considerable recent attention. Such inference requires statistical models that map activities to the objects used in performing them. Proposed techniques for constructing these models (hand definition, learning from data, and web extraction) all share the problem of model incompleteness: it is difficult to either manually or automatically identify all the possible objects that may be used to perform an activity, or to accurately calculate the probability with which they will be used. In this paper, we show how to use auxiliary information, called an ontology, about the functional similarities between objects to mitigate the problem of model incompleteness. We show how to extract a large, relevant ontology automatically from WordNet, an online lexical reference system for the English language. We adapt a statistical smoothing technique, called shrinkage, to apply this similarity information to counter the incompleteness of ou...

• Type: Journal
• Year: 2006
• Where: PERVASIVE
• Authors: Emmanuel Munguia Tapia, Tanzeem Choudhury, Matthai Philipose
• Keywords: Applied Computing, Model Incompleteness, PERVASIVE 2006, Statistical Models, Such Inference