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新论文 · v1arXiv:2611.11426

Vietnamese Legal Ontology: Every legal answer, traced to the article that grounds it

Khac-Hoai Nam Bui, Quan Tran Minh

原文摘要

A legal answer is only as good as the article it rests on. Vietnam's largest open legal corpus pairs 109,543 published questions and answers with 573,851 legal documents, yet its citations are free text: "Khoan 3 Dieu 101 cua Bo luat nay" ("Clause 3, Article 101 of this Code") names neither the code nor its version. Following the five stages of NVIDIA Auto Ontology, Discover, Ground, Validate, Expose and Learn, we turn four open datasets into a knowledge graph of 11.5 M nodes and 16.0 M edges in which every node and edge carries its source record and method, with no duplicate keys, dangling edges or schema violations. Of 67,868 cited provisions, 61,487 resolve to a dated law version, 51,694 to a parsed article, clause or point, and 19,012 are confirmed word for word. The ontology then shows what flat records hide. 36% of the 78,361 answers whose citations resolve now cite a law that is no longer in force; 2,525 verified citations point to an article number that says something else in the current law; 2,520 citations were re-versioned because their words match a different version than their date suggests; and 5,575 document numbers are shared by several local authorities, one of them by 72. Every number in this paper is computed from the graph.

cs.CL · cs.AI

新论文 · v1arXiv:2611.05013

One Strategy Does Not Fit All: Rethinking Data Labeling for Just-in-Time Vulnerability Prediction

Quan Tran, Duong Nguyen, Thanh Le-Cong, Triet Huynh Minh Le, M. Ali Babar, Hoang Dieu Vu, Quyet-Thang Huynh

原文摘要

Just-In-Time Vulnerability Prediction (JIT-VP) identifies security risks in code changes at commit time, enabling early feedback before vulnerabilities enter software systems. However, its practical reliability is challenged by the difficulty of constructing accurate labels. Multiple strategies with different trade-offs have been proposed to identify vulnerability-fixing commits (VFCs) and vulnerability-introducing commits (VICs) for dataset construction, leading to substantially different datasets. This makes it difficult to separate model performance from dataset construction. Moreover, real-world quality assurance requires not only classification but also prioritizing high-risk commits and localizing vulnerable causes. This paper presents an extensive empirical study of how labeling strategies affect these downstream tasks. We constructed 28 datasets using four VFC and four VIC labeling strategies across OpenSSL and Linux kernel, then evaluated six representative JIT-VP models for classification, prioritization, and localization. Our results show that labeling effectiveness is strongly task-dependent. Manual VFC labeling combined with vulnerability-specific VIC labeling provides the most reliable performance for classification and prioritization, significantly outperforming 11 of 15 alternatives. Meanwhile, precision-first VFC labeling combined with LLM-assisted VIC labeling performs best for localization, significantly outperforming 7 of 15 alternatives. These findings demonstrate that no single labeling strategy is universally optimal and highlight the importance of task-oriented dataset construction.

cs.SE