Pairwise Tensor Factorization for learning new facts in Knowledge Bases
Tanmoy Mukherjee, Vinay Pande, Vasudeva Varma · 2013
Knowledge bases provide with the benet of organizing knowledge in the relational form but suer 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 nd missing information in knowledge bases. Experiments show that we are able to query and nd missing information from text and shows improvement over existing methods.