ASKG: An Approach to Enrich Scholarly Knowledge Graphs through Paper Decomposition with Deep Learning

Knowledge Graphs (KGs) play a pivotal role in the field of artificial intelligence, yet the construction of such graphs often requires significant human resources. Automated KG construction technologies are key to achieving large-scale KGs construction. To address this, we have developed an automated Knowledge Graph Construction Pipeline (KGCP) and applied it to the enhancement of the Australian National University (ANU) Scholarly Knowledge Graph (ASKG), which comprehensively represents not only the metadata but also the scholarly knowledge encapsulated in the academic papers. This study introduces an innovative, automatic approach to KGs construction using an array of Natural Language Processing (NLP) techniques. Leveraging Named Entity Recognition (NER) models, key academic entities related to computer science are efficiently identified, such as Research Problems, Methods, Solution, Tool, Resource, Dataset, and Language. The ASKG is further enriched through Named Entity Linking (NEL) with Wikidata, keyword extraction, automatic summarisation, and the integration of entities from the Metadata Extractor & Loader and The NLP-NER Toolkit (MEL & TNNT).