arXiv：cs.CL· Ahmad Abbas, Hussein Mazeh, Chadi Abou Chakra, Aya Jouni, Mohammad Ballout, Fadi A. Zaraket·· 11 小时前
U4RASD 在 KnowledgeGraphEval-2026：面向阿拉伯语关系抽取的本体约束编码器与 LLM 系统
U4RASD at KnowledgeGraphEval-2026: Ontology-Constrained Encoder and LLM Systems for Arabic Relation Extraction
AI 导读
U4RASD 团队在 KnowledgeGraphEval 2026 子任务 2 的阿拉伯语有向关系抽取中，以 Yehia-RE、Qwen-RE 与 TypedPair 的集成系统取得 94.06 的 micro-F1，排名第一。
正文
Abstract:This paper details our submission to KnowledgeGraphEval 2026 Subtask 2 on directed Arabic relation extraction. We explored encoder-based and large language model approaches for predicting one of 40 relations or no_relation from an Arabic sentence and a subject-object pair. Our encoder systems use typed entity markers and separate relation-existence and relation-classification heads, while our LLM systems fine-tune Yehia and Qwen3.5 with QLoRA and ontology-constrained prediction. Our best system, an ensemble of Yehia-RE, Qwen-RE, and TypedPair, achieved a micro-F1 score of 94.06 and ranked 1st in Subtask 2. Yehia-RE (full-vocabulary) achieved 92.98, while the best encoder and Qwen-RE achieved 92.69 and 91.94 respectively. Our results highlight the importance of constrained prediction and complementary modeling approaches for Arabic relation extraction.
| Comments: | 20 pages. System description paper for the KnowledgeGraphEval 2026 Shared Task at ArabicNLP 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.12742 [cs.CL] |
| (or arXiv:2610.12742v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12742 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ahmad Abbas [view email]
[v1]
Thu, 8 Oct 2026 21:41:29 UTC (429 KB)
来源：arXiv：cs.CL · arxiv.org