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.

References are an important feature of scientific literature; however, they are unstructured, heterogeneous, noisy, and often multilingual. We present a modular pipeline that leverages fine-tuned transformer models for reference location, classification, parsing, retrieval, and re-ranking across multiple scholarly knowledge graphs, with a focus on multilingual and non-traditional sources such as patents and policy documents. Our main contributions are: a unified pipeline for reference extraction and linking across diverse document types, openly released annotated datasets, fine-tuned models for each subtask, and evaluations across multiple scholarly knowledge graphs, enabling richer, more inclusive infrastructures for open research information.

The accurate attribution of scientific works to research organizations is hindered by the lack of openly available manually annotated data–in particular when multilingual and complex affiliation strings are considered. The AffilGood framework introduced in this paper addresses this gap. We identify three sub-tasks relevant for institution name disambiguation and make available annotated datasets and tools aimed at each of them, including i) a dataset annotated with affiliation spans in noisy automatically-extracted strings; ii) a dataset annotated with named entities for the identification of organizations and their locations; iii) seven datasets annotated with the Research Organization Registry (ROR) identifiers for the evaluation of entity-linking systems. In addition, we describe, evaluate and make available newly developed tools that use these datasets to provide solutions for each of the identified sub-tasks. Our results confirm the value of the developed resources and methods in addressing key challenges in institution name disambiguation.

In this work we propose to leverage resources available with discourse-level annotations to facilitate the identification of argumentative components and relations in scientific texts, which has been recognized as a particularly challenging task. In particular, we implement and evaluate a transfer learning approach in which contextualized representations learned from discourse parsing tasks are used as input of argument mining models. As a pilot application, we explore the feasibility of using automatically identified argumentative components and relations to predict the acceptance of papers in computer science venues. In order to conduct our experiments, we propose an annotation scheme for argumentative units and relations and use it to enrich an existing corpus with an argumentation layer.