- The paper presents SciIE, a multi-task framework that concurrently identifies entities, relations, and coreference links to build scientific knowledge graphs.
- Using shared span representations and a novel dataset of 500 abstracts, the approach reduces cascading errors and improves cross-sentence relation detection compared to traditional pipelines.
- The findings establish a robust foundation for automated scientific knowledge graph construction and suggest future enhancements with semi-supervised learning techniques.
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
The paper presents a comprehensive approach to the extraction of structured information from scientific literature by leveraging a multi-task learning framework. This framework is designed to identify and classify entities, relations, and coreference clusters in scientific documents, with the ultimate goal of constructing a scientific knowledge graph.
Framework and Methodology
The authors introduce the Scientific Information Extractor (SciIE), a unified system that integrates multiple information extraction tasks. Unlike traditional pipeline approaches, SciIE employs a multi-task setup that shares parameters across tasks, reducing cascading errors and enhancing the extraction of cross-sentence relations through coreference links. This unified architecture is a departure from previous models, which typically handle these tasks in isolation.
The core component of the framework is a shared span representation that enables effective learning and prediction across tasks. The system generates all possible spans during the decoding phase, facilitating the detection of overlapping entities and connections between them. This capability is crucial for dealing with the intrinsic complexities of scientific texts.
Dataset Creation and Evaluation
To support this research, a novel dataset comprising annotations for entities, relations, and coreference links within scientific abstracts was developed. This dataset includes 500 abstracts spanning various AI disciplines, allowing for a broad evaluation across domains. The annotations are designed to enhance cross-sentence relation identification, an area where existing datasets are notably weaker.
SciIE outperforms state-of-the-art systems in entity and relation extraction, as demonstrated through rigorous experiments. Notably, this improvement is achieved without relying on domain-specific features or preprocessing steps, indicating the robustness and generalizability of the model.
Implications and Future Directions
The successful application of this multi-task learning framework has significant implications for the automated construction of scientific knowledge graphs. By integrating entities and relations extracted from individual articles, the framework supports the creation of a coherent and comprehensive knowledge base that can assist researchers in identifying new associations and trends within scientific literature.
The paper's results suggest several avenues for future research. Enhancing the performance of SciIE through semi-supervised learning techniques or incorporating additional domain-specific knowledge may further boost its efficacy. Moreover, extending this framework to other specialized domains could expand its applicability and utility.
In conclusion, this work provides a robust foundation for automatic scientific information extraction and knowledge graph construction. Its multi-task approach and the newly developed dataset represent significant contributions to the field, offering promising directions for continued advancements in AI-driven information management.