Entity Linking
Entity linking (EL) is the task of connecting textual mentions of entities (e.g., people, places, organizations) to their corresponding entries in a knowledge base. Current research emphasizes improving EL's accuracy and efficiency across diverse data types (text, images, tables), focusing on model architectures like retriever-reader systems and large language models (LLMs), often incorporating techniques such as ensemble learning and data augmentation to handle challenges like limited training data and ambiguous mentions. The advancements in EL are crucial for building robust knowledge graphs, powering applications in various fields including healthcare, cybersecurity, and scientific literature analysis, ultimately facilitating more effective information extraction and knowledge discovery.
Papers
Towards Better Entity Linking with Multi-View Enhanced Distillation
Yi Liu, Yuan Tian, Jianxun Lian, Xinlong Wang, Yanan Cao, Fang Fang, Wen Zhang, Haizhen Huang, Denvy Deng, Qi Zhang
Benchmarking Diverse-Modal Entity Linking with Generative Models
Sijia Wang, Alexander Hanbo Li, Henry Zhu, Sheng Zhang, Chung-Wei Hang, Pramuditha Perera, Jie Ma, William Wang, Zhiguo Wang, Vittorio Castelli, Bing Xiang, Patrick Ng
Evaluating end-to-end entity linking on domain-specific knowledge bases: Learning about ancient technologies from museum collections
Sebastian Cadavid-Sanchez, Khalil Kacem, Rafael Aparecido Martins Frade, Johannes Boehm, Thomas Chaney, Danial Lashkari, Daniel Simig
WebIE: Faithful and Robust Information Extraction on the Web
Chenxi Whitehouse, Clara Vania, Alham Fikri Aji, Christos Christodoulopoulos, Andrea Pierleoni