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Knowledge graphs have emerged as an important model for studying complex multirelational 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, adoi:10.18653/v1/p18-1024 dblp:conf/acl/FaloutsosTSDMZ18 fatcat:2jbo23d3d5benelgik2byae3ni