Name
Abstract | ||
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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 promising research directions that benefit from AceKG.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3269206.3269252 | CIKM |
Keywords | DocType | Volume |
Knowledge Graph, Benchmarking, Data Mining | Conference | abs/1807.08484 |
ISBN | Citations | PageRank |
978-1-4503-6014-2 | 3 | 0.41 |
References | Authors | |
12 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruijie Wang | 1 | 4 | 3.12 |
| Yuchen Yan | 2 | 3 | 0.75 |
| Jialu Wang | 3 | 5 | 3.51 |
| Yuting Jia | 4 | 9 | 3.51 |
| Ye Zhang | 5 | 49 | 10.86 |
| Weinan Zhang | 6 | 1228 | 97.24 |
| Xinbing Wang | 7 | 2642 | 214.43 |