AceKG: A Large-scale Knowledge Graph for Academic Data Mining
This addresses the need for high-quality, machine-processable academic data for researchers, though it is incremental as it builds on existing KG concepts with domain-specific enhancements.
The authors tackled the problem of insufficient and ambiguous academic knowledge graphs by introducing AceKG, a large-scale knowledge graph with 3.13 billion triples that provides clean data and benchmarks for tasks like link prediction and scholar classification.
Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing. In this paper, we present AceKG, a new large-scale KG in academic domain. AceKG not only provides clean academic information, but also offers a large-scale benchmark dataset for researchers to conduct challenging data mining projects including link prediction, community detection and scholar classification. Specifically, AceKG describes 3.13 billion triples of academic facts based on a consistent ontology, including necessary properties of papers, authors, fields of study, venues and institutes, as well as the relations among them. To enrich the proposed knowledge graph, we also perform entity alignment with existing databases and rule-based inference. Based on AceKG, we conduct experiments of three typical academic data mining tasks and evaluate several state-of- the-art knowledge embedding and network representation learning approaches on the benchmark datasets built from AceKG. Finally, we discuss several promising research directions that benefit from AceKG.