CORE: Context-Aware Open Relation Extraction with Factorization Machines

This page provides supplementary material for the paper “CORE: Context-Aware Open Relation Extraction with Factorization Machines” by Fabio Petroni, Luciano del Corro, and Rainer Gemulla published at the 2015 Conference on Empirical Methods on Natural Language Processing (EMNLP).


We propose CORE, a novel matrix factorization model that leverages contextual information for open relation extraction. Our model is based on factorization machines and integrates facts from various sources, such as knowledge bases or open information extractors, as well as the context in which these facts have been observed. We argue that integrating contextual information—such as metadata about extraction sources, lexical context, or type information—significantly improves prediction performance. Open information extractors, for example, may produce extractions that are unspecific or ambiguous when taken out of context. Our experimental study on a large real-world dataset indicates that CORE has significantly better prediction performance than state-of-the-art approaches when contextual information is available.

Supplementary material