In this paper, we showed that iterative graphbased ranking algorithms -originally designed for content-independent Web link analysis or for social networks -turn into a useful source of information for natural language tasks when applied on semantic networks.
we proposed and evaluated a new approach for unsupervised knowledge-based word-sense disambiguation that relies on PageRankstyle algorithms applied on a WordNet-based concepts graph, and showed that the accuracy achieved through our algorithm exceeds the performance obtained by other knowledge-based algorithms.