The key point of our method is a reliable semantic relatedness measure between concepts (including WordNet concepts, NEs and Wikipedia concepts), called Structural Semantic Relatedness, which can capture both the explicit semantic relations between concepts and the implicit semantic knowledge embedded in graphs and networks.
we first extract the semantic relations between two concepts from a variety of knowledge sources and Machine learning represent them using a graph-based model, semantic-graph.