A Model of Text-Enhanced Knowledge Graph Representation Learning with Collaborative Attention

Yashen Wang, Huanhuan Zhang, Haiyong Xie
; Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:220-235, 2019.

Abstract

This paper proposes a novel collaborative attention mechanism, to fully utilize the mutually reinforcing relationship among the knowledge graph representation learning procedure (i.e., structure representation) and textual relation representation learning procedure (i.e., text representation). Based on this collaborative attention mechanism, a text-enhanced knowledge graph (KG) representation model is proposed, which could utilize textual information to enhance the knowledge representations and make the multi-direction signals to be fully integrated to learn more accurate textual representations for further improving structure representation and vice versa. Experimental results demonstrate the efficiency of the proposed model on both link prediction task and triple classification task.

Cite this Paper


BibTeX
@InProceedings{pmlr-v101-wang19d, title = {A Model of Text-Enhanced Knowledge Graph Representation Learning with Collaborative Attention}, author = {Wang, Yashen and Zhang, Huanhuan and Xie, Haiyong}, pages = {220--235}, year = {2019}, editor = {Wee Sun Lee and Taiji Suzuki}, volume = {101}, series = {Proceedings of Machine Learning Research}, address = {Nagoya, Japan}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v101/wang19d/wang19d.pdf}, url = {http://proceedings.mlr.press/v101/wang19d.html}, abstract = {This paper proposes a novel collaborative attention mechanism, to fully utilize the mutually reinforcing relationship among the knowledge graph representation learning procedure (i.e., structure representation) and textual relation representation learning procedure (i.e., text representation). Based on this collaborative attention mechanism, a text-enhanced knowledge graph (KG) representation model is proposed, which could utilize textual information to enhance the knowledge representations and make the multi-direction signals to be fully integrated to learn more accurate textual representations for further improving structure representation and vice versa. Experimental results demonstrate the efficiency of the proposed model on both link prediction task and triple classification task.} }
Endnote
%0 Conference Paper %T A Model of Text-Enhanced Knowledge Graph Representation Learning with Collaborative Attention %A Yashen Wang %A Huanhuan Zhang %A Haiyong Xie %B Proceedings of The Eleventh Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Wee Sun Lee %E Taiji Suzuki %F pmlr-v101-wang19d %I PMLR %J Proceedings of Machine Learning Research %P 220--235 %U http://proceedings.mlr.press %V 101 %W PMLR %X This paper proposes a novel collaborative attention mechanism, to fully utilize the mutually reinforcing relationship among the knowledge graph representation learning procedure (i.e., structure representation) and textual relation representation learning procedure (i.e., text representation). Based on this collaborative attention mechanism, a text-enhanced knowledge graph (KG) representation model is proposed, which could utilize textual information to enhance the knowledge representations and make the multi-direction signals to be fully integrated to learn more accurate textual representations for further improving structure representation and vice versa. Experimental results demonstrate the efficiency of the proposed model on both link prediction task and triple classification task.
APA
Wang, Y., Zhang, H. & Xie, H.. (2019). A Model of Text-Enhanced Knowledge Graph Representation Learning with Collaborative Attention. Proceedings of The Eleventh Asian Conference on Machine Learning, in PMLR 101:220-235

Related Material