From raw affiliations to organization identifiers
원문 초록
Accurate affiliation matching, which links affiliation strings to standardized organization identifiers, is critical for improving research metadata quality, facilitating comprehensive bibliometric analyses, and supporting data interoperability across scholarly knowledge bases. Existing approaches fail to handle the complexity of affiliation strings that often include mentions of multiple organizations or extraneous information. In this paper, we present AffRo, a novel approach designed to address these challenges, leveraging advanced parsing and disambiguation techniques. We also introduce AffRoDB, an expert-curated dataset to systematically evaluate affiliation matching algorithms, ensuring robust benchmarking. Results demonstrate the effectiveness of AffRp in accurately identifying organizations from complex affiliation strings.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations
DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition
DCR: Quantifying Data Contamination in LLMs Evaluation
FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil Engineering
토론
이 논문과 연결된 포럼 글이 아직 없습니다.