@inproceedings{xu-etal-2026-structure,
title = "Structure-Aware Zero-Shot Relational Learning for Knowledge Graphs without External Knowledge",
author = "Xu, Kuan and
Zhang, Baoxin and
Fan, Shuyue and
Chen, Ming and
Ke, Zhipeng and
Yu, Jian and
Zhou, Xuezhong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.941/",
pages = "18855--18869",
ISBN = "979-8-89176-395-1",
abstract = "Zero-shot Relational Learning (ZRL) aims to perform knowledge graph completion when dealing with newly emerging relations without instances of them. However, existing ZRL methods typically depend on external knowledge beyond Knowledge Graphs (KGs), resulting in increased annotation costs and limited practical applicability. To address this issue, we propose a new **S**tructure-**A**ware paradigm for **ZRL**, termed **SAZRL**, that performs ZRL without relying on external knowledge. SAZRL leverages intrinsic structural patterns in KGs to bridge semantic correlations for new relations with existing ones. It constructs structure-aware conditional query graphs based on shared entities and adaptive relation updating module to generate representations for new relations based on the query graphs. We conduct extensive experiments on three real-world benchmarks, **NELL-ZS**, **Wiki-ZS** and **FB15K-ZS**, demonstrating that SAZRL consistently surpasses state-of-the-art ZRL methods, achieving up to **10.66{\%}** improvement in **MRR** while reducing annotation costs and enhancing practical applicability. **The code and data are provided in supplementary materials.**"
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="xu-etal-2026-structure">
<titleInfo>
<title>Structure-Aware Zero-Shot Relational Learning for Knowledge Graphs without External Knowledge</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kuan</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Baoxin</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shuyue</namePart>
<namePart type="family">Fan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ming</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhipeng</namePart>
<namePart type="family">Ke</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jian</namePart>
<namePart type="family">Yu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xuezhong</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-395-1</identifier>
</relatedItem>
<abstract>Zero-shot Relational Learning (ZRL) aims to perform knowledge graph completion when dealing with newly emerging relations without instances of them. However, existing ZRL methods typically depend on external knowledge beyond Knowledge Graphs (KGs), resulting in increased annotation costs and limited practical applicability. To address this issue, we propose a new **S**tructure-**A**ware paradigm for **ZRL**, termed **SAZRL**, that performs ZRL without relying on external knowledge. SAZRL leverages intrinsic structural patterns in KGs to bridge semantic correlations for new relations with existing ones. It constructs structure-aware conditional query graphs based on shared entities and adaptive relation updating module to generate representations for new relations based on the query graphs. We conduct extensive experiments on three real-world benchmarks, **NELL-ZS**, **Wiki-ZS** and **FB15K-ZS**, demonstrating that SAZRL consistently surpasses state-of-the-art ZRL methods, achieving up to **10.66%** improvement in **MRR** while reducing annotation costs and enhancing practical applicability. **The code and data are provided in supplementary materials.**</abstract>
<identifier type="citekey">xu-etal-2026-structure</identifier>
<location>
<url>https://aclanthology.org/2026.findings-acl.941/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>18855</start>
<end>18869</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Structure-Aware Zero-Shot Relational Learning for Knowledge Graphs without External Knowledge
%A Xu, Kuan
%A Zhang, Baoxin
%A Fan, Shuyue
%A Chen, Ming
%A Ke, Zhipeng
%A Yu, Jian
%A Zhou, Xuezhong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F xu-etal-2026-structure
%X Zero-shot Relational Learning (ZRL) aims to perform knowledge graph completion when dealing with newly emerging relations without instances of them. However, existing ZRL methods typically depend on external knowledge beyond Knowledge Graphs (KGs), resulting in increased annotation costs and limited practical applicability. To address this issue, we propose a new **S**tructure-**A**ware paradigm for **ZRL**, termed **SAZRL**, that performs ZRL without relying on external knowledge. SAZRL leverages intrinsic structural patterns in KGs to bridge semantic correlations for new relations with existing ones. It constructs structure-aware conditional query graphs based on shared entities and adaptive relation updating module to generate representations for new relations based on the query graphs. We conduct extensive experiments on three real-world benchmarks, **NELL-ZS**, **Wiki-ZS** and **FB15K-ZS**, demonstrating that SAZRL consistently surpasses state-of-the-art ZRL methods, achieving up to **10.66%** improvement in **MRR** while reducing annotation costs and enhancing practical applicability. **The code and data are provided in supplementary materials.**
%U https://aclanthology.org/2026.findings-acl.941/
%P 18855-18869
Markdown (Informal)
[Structure-Aware Zero-Shot Relational Learning for Knowledge Graphs without External Knowledge](https://aclanthology.org/2026.findings-acl.941/) (Xu et al., Findings 2026)
ACL
- Kuan Xu, Baoxin Zhang, Shuyue Fan, Ming Chen, Zhipeng Ke, Jian Yu, and Xuezhong Zhou. 2026. Structure-Aware Zero-Shot Relational Learning for Knowledge Graphs without External Knowledge. In Findings of the Association for Computational Linguistics: ACL 2026, pages 18855–18869, San Diego, California, United States. Association for Computational Linguistics.