Relation Extraction with Matrix Factorization and Universal Schemas

Traditional relation extraction predicts relations within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, existing structured sources of the same schema. The need for existing datasets can be avoided by using a universal schema: the union of all involved schemas (surface form predicates as in OpenIE, and relations in the schemas of pre-existing databases). This schema has an almost unlimited set of relations (due to surface forms), and supports integration with existing structured data (through the relation types of existing databases). To populate a database of such schema we present matrix factorization…

• NS
  National Science Foundation

  Award: 0803847
• UO
  University of Pennsylvania
• DA
  Defense Advanced Research Projects Agency

  Awards: FA8750-13-2-0020, FA8750
• AF
  Air Force Research Laboratory

  Award: FA8750-09-C-0181