SynEL: A synthetic benchmark for entity linking.

ABSTRACT: Large language models (LLMs) offer significant potential for constructing commonsense knowledge graphs from text, demonstrating adaptability across diverse domains. However, their effectiveness varies significantly with domain-specific language, highlighting a critical need for specialized benchmarks to assess and optimize knowledge graph construction sub-tasks like named entity recognition, relation extraction, and entity linking. Currently, domain-specific benchmarks are scarce. To address this gap, we introduce SynEL, a novel benchmark developed for evaluating text-based knowledge extraction methods, validated using customer support dialogues. We present a comprehensive methodology for benchmark construction, propose two distinct approaches for generating synthetic datasets, and evaluat

SUBMITTER: Karpov I 

PROVIDER: S-EPMC12782364 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature