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Extracting interest profiles of users based on their personal documents is one of the key topics of IR research. However, when these extracted profiles are used in expert finding applications, only naive text-matching techniques are used to rank experts for a given requirement. This paper addresses this gap and describes multiple techniques to match user profiles for better ranking of experts. The paper proposes new metrics for computing semantic similarity of user profiles using spreading activation networks derived from ontologies. The pilot evaluation shows that matching algorithms based on bipartite graphs over semantic user profiles provide the best results. The paper shows that using these techniques, one can find an expert more accurately than other approaches, in particular within the top ranked results.
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