Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs

Zhanke Zhou, Yongqi Zhang, Jiangchao Yao, Quanming Yao, Bo Han*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

To deduce new facts on a knowledge graph (KG), a link predictor learns from the graph structure and collects local evidence to find the answer to a given query. However, existing methods suffer from a severe scalability problem due to the utilization of the whole KG for prediction, which hinders their promise on large-scale KGs and cannot be directly addressed by vanilla sampling methods. In this work, we propose the one-shot-subgraph link prediction to achieve efficient and adaptive prediction. The design principle is that, instead of directly acting on the whole KG, the prediction procedure is decoupled into two steps, i.e., (i) extracting only one subgraph according to the query and (ii) predicting on this single, query-dependent subgraph. We reveal that the non-parametric and computation-efficient heuristics Personalized PageRank (PPR) can effectively identify the potential answers and supporting evidence. With efficient subgraph-based prediction, we further introduce the automated searching of the optimal configurations in both data and model spaces. Empirically, we achieve promoted efficiency and leading performances on five large-scale benchmarks.
Original languageEnglish
Title of host publicationProceedings of 12th International Conference on Learning Representations (ICLR'24)
PublisherInternational Conference on Learning Representations
Number of pages32
Publication statusPublished - 10 May 2024
EventThe Twelfth International Conference on Learning Representations, ICLR 2024 - Messe Wien Exhibition and Congress Center, Vienna, Austria
Duration: 7 May 202411 May 2024
https://iclr.cc/ (conference website)
https://iclr.cc/virtual/2024/calendar (conference schedule )

Conference

ConferenceThe Twelfth International Conference on Learning Representations, ICLR 2024
Country/TerritoryAustria
CityVienna
Period7/05/2411/05/24
Internet address

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