OWL-Based User Preference and Behavior Routine Ontology for Ubiquitous System

Abstract

Abstract. In ubiquitous computing, behavior routine learning is the process of mining the context-aware data to find interesting rules on the user's behavior, while preference learning tries to utilize the user's behavior information to infer user interests, intention and desires. An intelligent environment should be adaptive, i.e. it is should be able to learn the routine and preference of user, then provide user with the suitable service. Developing intelligent ubiquitous environment requires not only good learning algorithms but also appropriate reusable models of user preference and behavior routine, which are not fully covered by current projects. In this paper, we propose a formal and comprehensive ontology-based model of user preference and behavior routine. The implementation of the ontology using OWL[14] enhances the expressiveness, support inference, knowledge reuse and knowledge sharing, which we can not achieve by normal models. The main benefit of this model is the ability to reason over context data to predict what the user wants the system to do. Based on our model, we also present a rule learning mechanism to learn the preference and behavior rules from context data.

A Goal-Directed Human Activity Computing Model

“…Recently, several ontology-oriented methods have been proposed to support context-aware activity computing, such as [13], [14], [15], [16], [17]. However, these works either focused on ontology of standard fundamental concepts (e.g., time, space, event, etc.)…”

“…for ubiquitous applications such as SOUPA [14] and COBRA-ONT [13], and/or aimed at developing specific ontology-based middleware frameworks (architectures) for context-aware computing, such as CA-MUS [16], CoBrA [13], and SOCAM [15]. An ontologybased model of user preference and behavior routine was proposed in [17], but the inference rules for user preference were actually defined as a kind of association rules in terms of data mining techniques.…”

A user meta-model for context-aware recommender systems

“…Ngoc et al [7] defined a preference and behavior routine ontology for ubiquitous systems. This ontology creates a good input for user preference learning mechanism.…”

Emotion Communication Model Based on Life-Log Comparison: Mutual Understanding through Comparable Experiences

“…In addition, we will expand the use case by leveraging a great variety of life logs including Web access logs, purchasing histories, and operation logs of wireless remote controllers. It may be possible to automate subjective log acquisition to some extent by, for example, integrating techniques such as emotion extraction from text [21] and the interest extraction using ontology [22]. Furthermore, we would like to examine the possibility of advanced communication support by a method to exemplify information with appropriate timing and effective visualization that suits the device currently being used.…”