@inproceedings{trivedi-etal-2018-linknbed,
title = "{L}ink{NB}ed: Multi-Graph Representation Learning with Entity Linkage",
author = "Trivedi, Rakshit and
Sisman, Bunyamin and
Dong, Xin Luna and
Faloutsos, Christos and
Ma, Jun and
Zha, Hongyuan",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1024",
doi = "10.18653/v1/P18-1024",
pages = "252--262",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T LinkNBed: Multi-Graph Representation Learning with Entity Linkage
%A Trivedi, Rakshit
%A Sisman, Bunyamin
%A Dong, Xin Luna
%A Faloutsos, Christos
%A Ma, Jun
%A Zha, Hongyuan
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F trivedi-etal-2018-linknbed
%X 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.
%R 10.18653/v1/P18-1024
%U https://aclanthology.org/P18-1024
%U https://doi.org/10.18653/v1/P18-1024
%P 252-262
Markdown (Informal)
[LinkNBed: Multi-Graph Representation Learning with Entity Linkage](https://aclanthology.org/P18-1024) (Trivedi et al., ACL 2018)
ACL
- Rakshit Trivedi, Bunyamin Sisman, Xin Luna Dong, Christos Faloutsos, Jun Ma, and Hongyuan Zha. 2018. LinkNBed: Multi-Graph Representation Learning with Entity Linkage. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 252–262, Melbourne, Australia. Association for Computational Linguistics.