A method includes performing contextual association of entities using multi-source data. For each context the method performs co-clustering to identify distinct expert-skill associations; constructing single-entity unipartite graph representations and performing a random walk within each single-entity unipartite graph; for each single-entity unipartite graph, obtaining steady state distributions using the random walks to obtain clusters of experts and skills; performing a weighted two-way random walk across entity graphs (graph edges), giving preference to traversal within members of the same co-cluster; and performing link prediction for each context by dynamically adding edges, and obtaining overall skills predictions, analyses and inferences by merging the contexts and weighting the links of each context. The method can also use the context-specific weights obtained from the co-association information in a matrix completion procedure, and finally merge the context-specific outputs to obtain overall skills predictions, analyses and inferences. A computer program product and a system are also disclosed for performing the method.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-21. (canceled) 22. A method implemented at least partially using a computer, comprising: performing contextual association of entities using multi-source data and for each context performing co-clustering to identify distinct expert-skill associations; and performing a matrix completion procedure where weights for each context are obtained using co-association information. 23. The method of claim 22, where the multi-source data is comprised of at least one of taxonomical classification data, folksonomical data, and data relating to publications and collaborations of employees. 24. The method of claim 22, further comprising incorporating contextual information to co-associate experts and skills.