End-to-End NLP Knowledge Graph Construction
This work addresses the need for automated knowledge extraction and organization in the NLP community, though it is incremental as it builds on existing KG construction methods.
The paper tackled the problem of constructing a Knowledge Graph (KG) from NLP scientific papers by extracting four relation types, such as evaluatedOn and coreferent relations, and applied the framework to 30,000 papers to build a large-scale KG for automating leaderboards.
This paper studies the end-to-end construction of an NLP Knowledge Graph (KG) from scientific papers. We focus on extracting four types of relations: evaluatedOn between tasks and datasets, evaluatedBy between tasks and evaluation metrics, as well as coreferent and related relations between the same type of entities. For instance, F1-score is coreferent with F-measure. We introduce novel methods for each of these relation types and apply our final framework (SciNLP-KG) to 30,000 NLP papers from ACL Anthology to build a large-scale KG, which can facilitate automatically constructing scientific leaderboards for the NLP community. The results of our experiments indicate that the resulting KG contains high-quality information.