The growing amount of scientific literature has increased the need for automatic methods that can retrieve, process, and exploit scholarly content. In this work, we explore section name normalization and hierarchy prediction for scientific articles using a two-level taxonomy. We compare independent, sequential classification models, and generative large language models on the SASC dataset. Results show that classification approaches, particularly sequential models that employ document-level context, consistently outperform generative methods. Incorporating section content is essential for fine-grained classification, while generative models remain limited in zero-shot settings. Our experiments highlight the importance of structure-aware modelling for large-scale scholarly document processing, and the importance of section normalization for the development of advanced research mapping and research assessment tools.

Navigating scholarly content presents important challenges due to the fragmented and heterogeneous nature of research production and outputs. Scholarly Knowledge Graphs offer an efficient means to integrate diverse data sources and consolidate knowledge across outputs in a structured manner. This representation, combined with the grounding of unstructured textual data to well-defined research-related concepts, has great potential for enhancing knowledge discovery and supporting researchers navigating through vast amounts of scientific information. Knowledge extraction capabilities are commonly limited by the availability of large collections of annotated data supporting named-entity recognition (NER) and linking (EL), and the enormous effort that their elaboration entails for domain experts. Recent advances in natural language processing and generative artificial intelligence provide valuable opportunities to reduce the data annotation toll and produce high-quality NER with minimal expert involvement. Here, we present a pipeline for domain-specific NER and EL, leveraging LLMs and knowledge from experts in a human-in-the-loop approach to streamline the annotation process, along with transformer-based models and few-shot techniques. While the application focuses on showcasing four specific domains, the pipeline is designed to be flexible and domain agnostic for scientific fields.