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AceKG: A Large-scale Knowledge Graph for Academic Data Mining

Published: 17 October 2018 Publication History

Abstract

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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cover image ACM Conferences
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management
October 2018
2362 pages
ISBN:9781450360142
DOI:10.1145/3269206
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 17 October 2018

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Author Tags

  1. benchmarking
  2. data mining
  3. knowledge graph

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  • Short-paper

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  • National Key R&D Program of China
  • NSFC

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CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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  • (2025)Counterfactual Learning on Graphs: A SurveyMachine Intelligence Research10.1007/s11633-024-1519-z22:1(17-59)Online publication date: 24-Jan-2025
  • (2024)SLOGProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694352(55348-55370)Online publication date: 21-Jul-2024
  • (2024)A Business-Model-Driven Approach to Task-Planning Knowledge Graph ConstructionApplied Sciences10.3390/app14231109014:23(11090)Online publication date: 28-Nov-2024
  • (2024)ESDC: 一种用于支持地学文献信息抽取的开放地球科学数据语料库SCIENTIA SINICA Terrae10.1360/N072023-0247Online publication date: 12-Nov-2024
  • (2024)ArieL: Adversarial Graph Contrastive LearningACM Transactions on Knowledge Discovery from Data10.1145/363805418:4(1-22)Online publication date: 12-Feb-2024
  • (2024)OAG-Bench: A Human-Curated Benchmark for Academic Graph MiningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672354(6214-6225)Online publication date: 25-Aug-2024
  • (2024)Graph Data Condensation via Self-expressive Graph Structure ReconstructionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671710(1992-2002)Online publication date: 25-Aug-2024
  • (2024)Topological Anonymous Walk Embedding: A New Structural Node Embedding ApproachProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679565(2796-2806)Online publication date: 21-Oct-2024
  • (2024)TGOnline: Enhancing Temporal Graph Learning with Adaptive Online Meta-LearningProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657791(1659-1669)Online publication date: 10-Jul-2024
  • (2024)MetaHKG: Meta Hyperbolic Learning for Few-shot Temporal ReasoningProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657711(59-69)Online publication date: 10-Jul-2024
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