arXiv subjects

Tung-Wei Lin

Publications and source records attributed to Tung-Wei Lin.

3 recordsLinked to original sources

arXiv2026

Selective Retrieval Routing for Multi-Violation Knowledge Graph Repair

Knowledge graphs often contain missing or inconsistent facts that undermine the applications built on them. Schema validation detects such violations but does not identify which of several valid repairs recovers the intended graph. External corpora can supply the evidence needed to identify the intended repair, but retrieving from them is costly and not always successful. We propose a framework for selective retrieval in iterative graph repair. An LLM repair agent explores and edits the graph, while a router controls its access to a researcher agent that retrieves external evidence. We extend a violation-injection framework to generate cases with multiple compatible violations and known reference repairs. The agent's repair outcomes on these cases provide supervision for training the deployment-specific router. On two building-automation datasets, researcher access improves repairs for some violations and degrades them for others, with effects differing across deployments. The router uses fewer repair-time tokens than always enabling access in all six evaluation suites. It achieves higher repair $F_1$ than always disabling access in five of six suites.

cs.DB↗

arXiv2026

A Formal Diagnostic Framework for Graph Repair Systems

Graph-based data representations excel at capturing relations among entities, e.g., for semantic web, artificial intelligence, or database applications, and their utility depends critically on their quality, usually enforced via graph schemas. These schemas are often violated, e.g., during data ingestion, calling for repair actions to achieve compliance. This paper presents a formal diagnostic framework for graph repair methods. Current methods often lack rigor and generality, since they rely on ad hoc test datasets. Our method starts, instead, from a compliant graph and uses an abstract rewriting system to systematically introduce schema violations while ensuring provable coverage of the constraints in the schema. We apply the framework to several repair methods, including those based on large language models, and assess their performance across multiple metrics. Results show that our framework effectively differentiates the performance of different methods across various constraints, highlighting the importance of fine-grained, systematic tools for graph repair.

cs.DB↗

arXiv2022

Class-wise Thresholding for Robust Out-of-Distribution Detection

We consider the problem of detecting OoD(Out-of-Distribution) input data when using deep neural networks, and we propose a simple yet effective way to improve the robustness of several popular OoD detection methods against label shift. Our work is motivated by the observation that most existing OoD detection algorithms consider all training/test data as a whole, regardless of which class entry each input activates (inter-class differences). Through extensive experimentation, we have found that such practice leads to a detector whose performance is sensitive and vulnerable to label shift. To address this issue, we propose a class-wise thresholding scheme that can apply to most existing OoD detection algorithms and can maintain similar OoD detection performance even in the presence of label shift in the test distribution.

cs.LG↗