'surfing for knowledge' finding semantically similar Web clusters

In this paper we present our technique for finding semantically similar clusters within web documents obtained from a set of queries retrieved from the Google search engine. This technique utilizes a clustering algorithm based on previous Latent Semantic Analysis (LSA) work pioneered by Deerwester. In this paper we demonstrate how by using our clustering algorithm we can resolve ambiguities prevalent in natural language such as polysemy and synonymy. Following from a detailed description of the algorithm we present our initial findings using real world Internet queries. We conclude by evaluating the merits of our clustering algorithm through comparison with results observed by human categorization. KEYWORDS Information Retrieval, Semantic Web, Latent Semantic Analysis.

• Type: Conference
• Year: 2004
• Where: IADIS
• Authors: David Cleary, Diarmuid O'Donoghue
• Keywords: Clustering Algorithm, Google Search Engine, IADIS 2004, IADIS 2008, Latent Semantic Analysis