Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction

TL;DR

This paper introduces a joint embedding model for relation extraction that leverages both text and knowledge base information, demonstrating improved performance on real-world data.

Contribution

It presents a novel embedding-based approach that combines text and knowledge base data for relation extraction, outperforming text-only methods.

Findings

Effective use of Freebase data improves relation extraction accuracy.

Model outperforms existing text-only relation extraction methods.

Demonstrates scalability with large knowledge bases.

Abstract

This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on two scoring functions that operate by learning low-dimensional embeddings of words and of entities and relationships from a knowledge base. We empirically show on New York Times articles aligned with Freebase relations that our approach is able to efficiently use the extra information provided by a large subset of Freebase data (4M entities, 23k relationships) to improve over existing methods that rely on text features alone.