Clustering Hyperlinks for Topic Extraction: An Exploratory Analysis

In a Web of increasing size and complexity, a key issue is automatic document organization, which includes topic extraction in collections. Since we consider topics as document clusters with semantic properties, we are concerned with exploring suitable clustering techniques for their identification on hyperlinked environments (where we only regard structural information). For this purpose, three algorithms (PDDP, k-means, and graph local clustering) were executed over a document subset of an increasingly popular corpus: Wikipedia. Results were evaluated with unsupervised metrics (cosine similarity, semantic relatedness, Jaccard index) and suggest that promising results can be produced for this particular domain.