Constructing intelligent and open mobile commerce using a semantic web approach

Abstract

To achieve the full potential of mobile commerce (m-Commerce), many problems need to be resolved, such as how to make m-Commerce seamlessly span wide areas with heterogeneous information sources; how to improve the matchmaking between user requirements and product specifications; and how to make m-Commerce systems more intelligent in taking different actions according to different user context environments. We believe that the semantic web is a promising technology to solve these problems. In this paper, a framework is proposed in which semantic web ontology is used to model the contexts, user profiles, and product/service information. The semantic web service ontology, OWL-S, is extended for matching user requirements with product specifications at the semantic level, with context information taken into account. Semantic Web Rule Language (SWRL) is used for inferencing with context and user profile descriptions. Our system adopts a multi-agent infrastructure to deal with the integration and interaction between information sources and users. This paper demonstrates some of the great potential of semantic web technology for m-Commerce.

Ontology‐based Multi‐agent System to Support Business Users and Management / Daugiaagentine Sistema Grindžiama Ontologija Verslo Vartotojams Ir Vadybininkams Paremti

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A distributed meta-learning system for Chinese entity relation extraction

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Assessment of ontology-based knowledge network formation by Vector-Space Model

“…Considerable ontology researches have been recently conducted to allow computer to provide information with knowledge implication. For example, Liao et al (2005) proposed a framework in which semantic web ontology is used to model the contexts, user profiles, and product/service information (Liao et al 2005), Pirró and Talia (2010) proposed an ontology mapping system with strategy prediction capabilities, Zhang et al (2009) proposed an ontology and peer-to-peer based data and service unified discovery system (Zhang et al 2009). Weng and Chang (2008) utilized ontology network analysis for research document recommendation (Weng and Chang 2008).…”