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Knowledge Graphs

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arxiv 2003.02320 v6 pith:FWVWS2E6 submitted 2020-03-04 cs.AI cs.DBcs.LG

Aidan Hogan , Eva Blomqvist , Michael Cochez , Claudia d'Amato , Gerard de Melo , Claudio Gutierrez , José Emilio Labra Gayo , Sabrina Kirrane
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Sebastian Neumaier Axel Polleres Roberto Navigli Axel-Cyrille Ngonga Ngomo Sabbir M. Rashid Anisa Rula Lukas Schmelzeisen Juan Sequeda Steffen Staab Antoine Zimmermann
This is my paper · ORCID
classification cs.AIcs.DBcs.LG
keywords knowledgegraphsdatatechniquesacademiaaforementionedapplicationsassessment
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Complex System Diagnostics Using a Knowledge Graph-Informed and Large Language Model-Enhanced Framework

    cs.AI 2025-05 conditional novelty 5.0 of 10

    An LLM-and-knowledge-graph pipeline automatically builds Dynamic Master Logic models for system diagnostics, reporting over 90% accuracy on a nuclear feedwater case study.

  2. Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery

    cs.CL 2025-09 reject novelty 2.0 of 10

    A climate knowledge graph built from prior extraction work is presented with example queries, but without evaluation or released artifacts.

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