Enriching Cultural Heritage Knowledge Graph Metadata from Finnish Texts with Large Language Models

This paper introduces the Finnish Named Entity Linker (FINEL), a tool that leverages Deep Learning models, including Large Language Models (LLMs), to recognize, disambiguate, and link Named Entities in Cultural Heritage texts. FINEL is designed to enhance the metadata of textual documents by connecting them to Knowledge Graphs (KG). We propose a zero-shot classification method that resembles Retrieval-Augmented Generation (RAG) and discuss a prototype web service with a user interface that enables human intervention for final disambiguation decisions. This editing capability is crucial, particularly when automatic linking may be hindered by errors and hallucinations inherent in LLM-based tools. The paper also reflects on lessons learned from using FINEL in applications targeting Digital Humanities (DH) research. Since the focus is on Finnish texts, our methods accommodate the specific challenges posed by this highly inflectional language and the available processing resources. Preliminary evaluation results underscore the potential of FINEL: our named entity lemmatizer achieved an accuracy of 96.5% on the test dataset, while an LLM from the Llama family reached 97% accuracy for entities with only one candidate. However, accuracy decreased with each additional candidate.