D’Souza, J. Agriculture Named Entity Recognition—Towards FAIR, Reusable Scholarly Contributions in Agriculture. Knowledge2024, 4, 1-26.
D’Souza, J. Agriculture Named Entity Recognition—Towards FAIR, Reusable Scholarly Contributions in Agriculture. Knowledge 2024, 4, 1-26.
D’Souza, J. Agriculture Named Entity Recognition—Towards FAIR, Reusable Scholarly Contributions in Agriculture. Knowledge2024, 4, 1-26.
D’Souza, J. Agriculture Named Entity Recognition—Towards FAIR, Reusable Scholarly Contributions in Agriculture. Knowledge 2024, 4, 1-26.
Abstract
We introduce the Open Research Knowledge Graph Agriculture Named Entity Recognition (the ORKG Agri-NER) corpus and service for contribution-centric scientific entity extraction and classification. The ORKG Agri-NER corpus is a seminal benchmark for the evaluation of contribution-centric scientific entity extraction and classification in the agricultural domain. It comprises titles of scholarly papers that are available as Open Access articles on a major publishing platform. We describe the creation of this corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism focused on capturing scientific entities in agriculture that reflect the direct contribution of a work; 2) a performance benchmark for named entity recognition of scientific entities in the agricultural domain by empirically evaluating various state-of-the-art sequence labeling neural architectures and transformer models; and 3) a delineated 3-step automatic entity resolution procedure for the resolution of the scientific entities to an authoritative ontology, specifically AGROVOC that is released in the Linked Open Vocabularies cloud. With this work we aim to provide a strong foundation for future work on the automatic discovery of scientific entities in the scholarly literature of the agricultural domain.
Keywords
information extraction; named entity recognition; natural language processing; dataset; sequence labeling; scholarly knowledge graphs; open research knowledge graph
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
