Our goal is to find the best candidate for every entity given a candidate's coherence and its initial similar-ity to the entity.
to combine the coherence score coh(e) with iSim(e), we weight the latter with an average value of P P R weights used in coherence computation (3) across all nodes in the document graph G(V, E): the final score for node e is a linear combination score(e) = coh(e) + P P R avg • iSim(e) (5) If the document graph has no edges then P P R avg is zero and for any node e its coherence coh(e) is zero as well.