In this paper, we propose to use semantic knowledge from Wikipedia and large-scale structured knowledge datasets available as Linked Open Data (LOD) for the answer passage reranking task. We represent question and candidate answer passages with pairs of shallow syntactic/semantic trees, whose constituents are connected using LOD. The trees are processed by SVMs and tree kernels, which can automatically exploit tree fragments. The experiments with our SVM rank algorithm on the TREC Question Answering (QA) corpus show that the added relational information highly improves over the state of the art, e.g., about 15.4% of relative improvement in P@1.
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