Using Ontological Reasoning for an Adaptive E-Commerce Experience

Using Ontological Reasoning for an Adaptive E-Commerce Experience

Manoj A. Thomas (Virginia Commonwealth University, USA), Richard T. Redmond (Virginia Commonwealth University, USA) and Victoria Y. Yoon (University of Maryland-Baltimore County, USA)
Copyright: © 2009 |Pages: 12
DOI: 10.4018/jiit.2009080703
OnDemand PDF Download:
No Current Special Offers


As e-commerce applications proliferates the Web, the authors are often overwhelmed by the task of sifting through the copious volumes of information. Since the nature of foraging for information in such digital spaces can be characterized as the interaction between internal task representation and the external problem domain, the authors look at how expert systems can be used to reduce complexity of the task. They describe a conceptual framework to analyze user interactions based on mental representations. They also detail an expert system implementation using the ontology language OWL to express the semantics of the representations and the rule language SWRL to define the rule base for contextual reasoning. The chapter illustrates how an expert system can be used to guide users in an e-commerce setting by orchestrating a cognitive fit between the task environment and the task solution.
Article Preview


Digital domains exist on the Internet in different forms serving different purposes ranging from e-commerce storefronts to digital libraries. As store houses of information, the content on these sites provide a space that is information rich on features, service and goods (Ranganathan et al. 2002). Visitors to these information spaces rely on navigational aids (hyperlinks, menu structures, keyword search, etc.) to find the information content of interest and to evaluate (feedback forms, reviews, ranking, testimonials, etc.) their relevance. Users almost often have uniquely different information needs. While one might search Amazon ( to determine the specifications and best price of a certain brand of LCD high definition TV. Ironically, it is the navigational aids that provision effective information search and enhance the consumer experience. But, they are almost always passive facilitators.

There is no shortage of information on the World Wide Web today. In our efforts to find what we need on the web, we often end up playing the guessing game of making the right choice from a finite set of juxtaposed options (links, search results, etc.). The passive navigational aids are often not adept in leading the user in the right direction nor are they designed to reflect the cognitive process of the individual user who ultimately uses them. Since browsing information spaces involve cognitive actions of the user, it is only natural to assume that navigational aids should be in agreement with, and adjust in an anticipatory manner based on the perceived user intentions.

We encounter different types of situations during our interaction with information spaces. We can refer to these as task environments. The activities of individuals in these task environments are situated in the social and physical setting in which they occur (Klahr et al. 1989, p:288; Newell et al. 1972). This applies to both physical and digital worlds, where consciously or subconsciously we engage, interact and converse with the environment before directing an action. Information processing models have failed to adequately address this situated character of activity (Suchman 1987). For effective interaction with the environment, an individual’s knowledge structure must incorporate detailed understanding of the structural features of the environment (Klahr et al. 1989). Unfortunately, this becomes a far-fetched expectation, especially in instances where representations fail to provide any form of guidance on the decision to act.

Complete Article List

Search this Journal:
Open Access Articles
Volume 17: 4 Issues (2021): 2 Released, 2 Forthcoming
Volume 16: 4 Issues (2020)
Volume 15: 4 Issues (2019)
Volume 14: 4 Issues (2018)
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing