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Enhancing Scholarly Knowledge Graphs via Domain-Specific Entity Detection and Linking

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.