  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.

Information Retrieval;Artificial Intelligence;Computation and Language