Knowledge Graphs Can be Learned with Just Intersection Features

Duy Le, Shaochen Zhong, Zirui Liu, Shuai Xu, Vipin Chaudhary, Kaixiong Zhou, Zhaozhuo Xu
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:26199-26214, 2024.

Abstract

Knowledge Graphs (KGs) are potent frameworks for knowledge representation and reasoning. Nevertheless, KGs are inherently incomplete, leaving numerous uncharted relationships and facts awaiting discovery. Deep learning methodologies have proven effective in enhancing KG completion by framing it as a link prediction task, where the goal is to discern the validity of a triple comprising a head, relation, and tail. The significance of structural information in assessing the validity of a triple within a KG is well-established. However, quantifying this structural information poses a challenge. We need to pinpoint the metric that encapsulates the structural information of a triple and smoothly incorporate this metric into the link prediction learning process. In this study, we recognize the critical importance of the intersection among the $k$-hop neighborhoods of the head, relation, and tail when determining the validity of a triple. To address this, we introduce a novel randomized algorithm designed to efficiently generate intersection features for candidate triples. Our experimental results demonstrate that a straightforward fully-connected network leveraging these intersection features can surpass the performance of established KG embedding models and even outperform graph neural network baselines. Additionally, we highlight the substantial training time efficiency gains achieved by our network trained on intersection features.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-le24c, title = {Knowledge Graphs Can be Learned with Just Intersection Features}, author = {Le, Duy and Zhong, Shaochen and Liu, Zirui and Xu, Shuai and Chaudhary, Vipin and Zhou, Kaixiong and Xu, Zhaozhuo}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {26199--26214}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/le24c/le24c.pdf}, url = {https://proceedings.mlr.press/v235/le24c.html}, abstract = {Knowledge Graphs (KGs) are potent frameworks for knowledge representation and reasoning. Nevertheless, KGs are inherently incomplete, leaving numerous uncharted relationships and facts awaiting discovery. Deep learning methodologies have proven effective in enhancing KG completion by framing it as a link prediction task, where the goal is to discern the validity of a triple comprising a head, relation, and tail. The significance of structural information in assessing the validity of a triple within a KG is well-established. However, quantifying this structural information poses a challenge. We need to pinpoint the metric that encapsulates the structural information of a triple and smoothly incorporate this metric into the link prediction learning process. In this study, we recognize the critical importance of the intersection among the $k$-hop neighborhoods of the head, relation, and tail when determining the validity of a triple. To address this, we introduce a novel randomized algorithm designed to efficiently generate intersection features for candidate triples. Our experimental results demonstrate that a straightforward fully-connected network leveraging these intersection features can surpass the performance of established KG embedding models and even outperform graph neural network baselines. Additionally, we highlight the substantial training time efficiency gains achieved by our network trained on intersection features.} }
Endnote
%0 Conference Paper %T Knowledge Graphs Can be Learned with Just Intersection Features %A Duy Le %A Shaochen Zhong %A Zirui Liu %A Shuai Xu %A Vipin Chaudhary %A Kaixiong Zhou %A Zhaozhuo Xu %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-le24c %I PMLR %P 26199--26214 %U https://proceedings.mlr.press/v235/le24c.html %V 235 %X Knowledge Graphs (KGs) are potent frameworks for knowledge representation and reasoning. Nevertheless, KGs are inherently incomplete, leaving numerous uncharted relationships and facts awaiting discovery. Deep learning methodologies have proven effective in enhancing KG completion by framing it as a link prediction task, where the goal is to discern the validity of a triple comprising a head, relation, and tail. The significance of structural information in assessing the validity of a triple within a KG is well-established. However, quantifying this structural information poses a challenge. We need to pinpoint the metric that encapsulates the structural information of a triple and smoothly incorporate this metric into the link prediction learning process. In this study, we recognize the critical importance of the intersection among the $k$-hop neighborhoods of the head, relation, and tail when determining the validity of a triple. To address this, we introduce a novel randomized algorithm designed to efficiently generate intersection features for candidate triples. Our experimental results demonstrate that a straightforward fully-connected network leveraging these intersection features can surpass the performance of established KG embedding models and even outperform graph neural network baselines. Additionally, we highlight the substantial training time efficiency gains achieved by our network trained on intersection features.
APA
Le, D., Zhong, S., Liu, Z., Xu, S., Chaudhary, V., Zhou, K. & Xu, Z.. (2024). Knowledge Graphs Can be Learned with Just Intersection Features. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:26199-26214 Available from https://proceedings.mlr.press/v235/le24c.html.

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