MfWMA: A Novel Web Mining Architecture for Expert Discovery

Identification of expert to domain knowledge in any field of interest is essential for consulting in industry, academia and scientific community. The objective of this study is to address the expert-finding task in contemporary communities. We proposed Multifaceted Web Mining Architecture (MfWMA) and implemented a tool with data extracted from Growbag, dblpXML and web authors home page resource to identify personnel with specific expertise. We mined two thousand and five hundred author's personal web pages with the underlying criteria of a dozen of key parameters; while parsing on each page in pursuit of 8 thousand topics. This study corroborate this quantification in terms of a measure of expertise. The prototype provides its users to distinguish the level of expertise in a particular area; thus resulting in the capability to mark people with broader expertise. Through this extension to the web enabling technique, we have demonstrated that the proposed architecture presents a novel web mining approximation with realistic results.