Knowledge Graph Embedding with Iterative Guidance from Soft Rules

Abstract

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Combining such an embedding model with logic rules has recently attracted increasing attention. Most previous attempts made a one-time injection of logic rules, ignoring the interactive nature between embedding learning and logical inference. And they focused only on hard rules, which always hold with no exception and usually require extensive manual effort to create or validate. In this paper, we propose Rule-Guided Embedding (RUGE), a novel paradigm of KG embedding with iterative guidance from soft rules. RUGE enables an embedding model to learn simultaneously from 1) labeled triples that have been directly observed in a given KG, 2) unlabeled triples whose labels are going to be predicted iteratively, and 3) soft rules with various confidence levels extracted automatically from the KG. In the learning process, RUGE iteratively queries rules to obtain soft labels for unlabeled triples, and integrates such newly labeled triples to update the embedding model. Through this iterative procedure, knowledge embodied in logic rules may be better transferred into the learned embeddings. We evaluate RUGE in link prediction on Freebase and YAGO. Experimental results show that: 1) with rule knowledge injected iteratively, RUGE achieves significant and consistent improvements over state-of-the-art baselines; and 2) despite their uncertainties, automatically extracted soft rules are highly beneficial to KG embedding, even those with moderate confidence levels. The code and data used for this paper can be obtained from https://github.com/iieir-km/RUGE.

To appear in AAAI 2018

References in corpus (8)

• Complex Embeddings for Simple Link Prediction
• Harnessing Deep Neural Networks with Logic Rules
• SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions
• Traversing Knowledge Graphs in Vector Space
• Compositional Vector Space Models for Knowledge Base Completion
• Order-Embeddings of Images and Language
• Modeling Relation Paths for Representation Learning of Knowledge Bases
• Probabilistic Reasoning via Deep Learning: Neural Association Models

Cited by in corpus (3)

• Joint Embedding Learning of Educational Knowledge Graphs
• Marrying up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding
• EM-RBR: a reinforced framework for knowledge graph completion from reasoning perspective