Extracting New Facts in Knowledge Bases:-A matrix tri factorization approach

Knowledge bases provide with the benefit of organizing knowledge in the relational form but suffer from incompleteness of new entities and relationships. Prior work on relation extraction has been focused on supervised learning techniques which are quite expensive. An alternative based on distant supervision has been of significant interest where one aligns records in the database with sentences of these records. A new line of work on embeddings of symbolic representations (Bordes et al., 2011) has shown promise. We introduce a Matrix tri factorization model which can find missing information in knowledge bases. Experiments show that we are able to query and find missing information from text and shows improvement over existing methods.

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