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Knowledge Graphs

2020/03/04 by Aidan Hogan, Eva Blomqvist, Michael Cochez +17 · 3 voices · 1,657 citations
Computer Science · Decision Sciences · #Advanced Graph Neural Networks #Artificial intelligence #Computer science #Data Quality and Management #Knowledge graph #Machine Learning and Algorithms #cs.AI #cs.DB #cs.LG

paper · pdf · open access · doi:10.1145/3447772

published in ACM Computing Surveys 54(4), 1-37 (Association for Computing Machinery) · Revision from v5: Correcting errata from previous version for entailment/models, and some other minor typos

openalex created_date 2020/03/13 · openalex publication_date 2021/07/02 · arxiv created 2021/09/11 · arxiv updated 2021/09/14 · openalex updated_date 2026/08/05

Abstract

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

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