LinkNBed: Multi-Graph Representation Learning with Entity Linkage

• Main Authors: Trivedi, Rakshit, Sisman, Bunyamin, Ma, Jun, Faloutsos, Christos, Zha, Hongyuan, Dong, Xin Luna
• Format: Journal Article
• Language: English
• Published: 23-07-2018
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Computation and Language; Computer Science - Learning; Statistics - Machine Learning
• DOI: 10.48550/arxiv.1807.08447

Knowledge graphs have emerged as an important model for studying complex
multi-relational data. This has given rise to the construction of numerous
large scale but incomplete knowledge graphs encoding information extracted from
various resources. An effective and scalable approach to jointly learn over
multiple graphs and eventually construct a unified graph is a crucial next step
for the success of knowledge-based inference for many downstream applications.
To this end, we propose LinkNBed, a deep relational learning framework that
learns entity and relationship representations across multiple graphs. We
identify entity linkage across graphs as a vital component to achieve our goal.
We design a novel objective that leverage entity linkage and build an efficient
multi-task training procedure. Experiments on link prediction and entity
linkage demonstrate substantial improvements over the state-of-the-art
relational learning approaches.