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Pairwise Tensor Factorization for learning new facts in Knowledge BasesAuthors: Tanmoy Mukherjee,Vinay Pande,Vasudeva Varma Conference: The 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) Location Chicago Date: 2013-08-11 Report no: IIIT/TR/2013/75 AbstractKnowledge 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 approach based on distant supervision has been of signicant interest where one aligns database records with sentences of these records. A new line of work on embeddings of symbolic representations [2] has shown promise. We introduce a Matrix trifactorization 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. Full paper: pdf Centre for Search and Information Extraction Lab |
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