Extracting Semantic Networks from Text via Relational Clustering

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SNE, a scalable, unsupervised, and domain-independent system that simultaneously extracts high-level relations and concepts, and learns a semantic network from text.
It first uses TextRunner to extract ground facts as triples from text, and then extract knowledge from the triples.
It does so with a probabilistic model that clusters objects by the objects that they are related to, and that clusters relations by the objects they relate. This allows information to propagate between clusters of relations and clusters of objects as they are created. Each cluster represents a high-level relation or concept. A concept cluster can be viewed as a node in a graph, and a relation cluster can be viewed as links between the concept clusters that it relates. Together the concept clusters and relation clusters define a simple semantic network.