Abstract:Many tools for knowledge management and the Semantic Web presuppose the existence of an arrangement of instances into classes, i. e. an ontology. Creating such an ontology, however, is a labor-intensive task. We present an unsupervised method to learn an ontology from text. We rely on pre-trained language models to generate lexical substitutes of given entities and then use matrix factorization to induce new classes and their entities. Our method differs from previous approaches in that (1) it captures the pol… Show more
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