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Similarity is nominally the quality or condition of two things bearing likeness to one another. Semantic similarity, then, is a term used to determine how alike they are in what ideas they convey. Using semantic graphs, it is possible to create a method in which one can calculate semantic similarity algorithmically. ConceptNet is a large, human-generated graph that provides semantic and contexual information about lingual items called concepts. ConceptNet is a semantic graph on which the similarity between sentences is calculated. The calculation is performed by first ranking the node components, each representing a concept, to filter them down to what is essentially a sentence's semantic kernel. Then measure the semantic similarity of node pairs by calculating the length of the pathway between them as well as their proximity to other important nodes, summing up each pair's score in order to obtain a semantic rating for the sentences. Both word-to-word and and sentence-to-sentence semantic similarity measurements were accurate enough to warrant further investigation
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Anzuoni, Michael Edward, "Using Graph Traversal to Find Similarity Between Sentences" (2013). Senior Projects Spring 2013. 81.