Extracting knowledge from text has long been a goal of AI. Initial approaches were purely logical and brittle. More recently, the availability of large quantities of text on the Web has led to the development of machine learning approaches. However, to date these have mainly extracted ground facts, as opposed to general knowledge. Other learning approaches can extract logical forms, but require supervision and do not scale. In this paper we present an unsupervised approach to extracting semantic networks from large volumes of text. We use the TextRunner system [1] to extract tuples from text, and then induce general concepts and relations from them by jointly clustering the objects and relational strings in the tuples. Our approach is defined in Markov logic using four simple rules. Experiments on a dataset of two million tuples show that it outperforms three other relational clustering approaches, and extracts meaningful semantic networks.
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