Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
This addresses the urgent need for automated knowledge graph construction in specific domains like oncology, reducing reliance on manual labeling, though it appears incremental as it builds on existing distant supervision methods.
The paper tackles the problem of constructing domain-specific knowledge graphs without manual annotations by proposing a coarse-to-fine adaptation framework from biomedical to oncology domains using distantly-supervised iterative training, achieving efficient domain adaptation and knowledge graph construction.
Modern supervised learning neural network models require a large amount of manually labeled data, which makes the construction of domain-specific knowledge graphs time-consuming and labor-intensive. In parallel, although there has been much research on named entity recognition and relation extraction based on distantly supervised learning, constructing a domain-specific knowledge graph from large collections of textual data without manual annotations is still an urgent problem to be solved. In response, we propose an integrated framework for adapting and re-learning knowledge graphs from one coarse domain (biomedical) to a finer-define domain (oncology). In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation. Consequently, no manual data annotation is required to train the model. We introduce a novel iterative training strategy to facilitate the discovery of domain-specific named entities and triples. Experimental results indicate that the proposed framework can perform domain adaptation and construction of knowledge graph efficiently.