LinkNBed: Multi-Graph Representation Learning with Entity Linkage
| Autor: | Jun Ma, Xin Luna Dong, Rakshit Trivedi, Bunyamin Sisman, Hongyuan Zha, Christos Faloutsos |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Theoretical computer science Computer Science - Computation and Language Computer science Computer Science - Artificial Intelligence Statistical relational learning Inference Machine Learning (stat.ML) 02 engineering and technology Machine Learning (cs.LG) Artificial Intelligence (cs.AI) Knowledge graph Statistics - Machine Learning 020204 information systems 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) Leverage (statistics) 020201 artificial intelligence & image processing Computation and Language (cs.CL) |
| Zdroj: | ACL (1) |
| Popis: | 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. ACL 2018 |
| Databáze: | OpenAIRE |
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