Knowledge Graphs

By Aidan Hogan, E. Blomqvist, Michael Cochez, C. d'Amato, Gerard de Melo, C. Gutierrez, J. E. L. Gayo, S. Kirrane, S. Neumaier, Axel Polleres and 8 more. ACM Computing Surveys, 2020

2,597 citations recorded by the source corpus.

In this article, 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, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4f00435f66a3dee7d5965fe8696c56f67021f1cc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Philip S. Yu
2. Shirui Pan
3. Feng Xia
4. Erik Cambria
5. Shaoxiong Ji
6. Hao Peng
7. Ciyuan Peng
8. Xindong Wu

In Management Science and Operations Research

1. Philip S. Yu
2. Xindong Wu
3. Gerhard Weikum
4. Hao Peng
5. Lingfeng Zhong
6. Qian Li
7. Jia Wu
8. Pekka Marttinen

Citations in the corpus

• Knowledge Graph Completion: A Review (2020, background): “The current reviews [1], [8], [10], [25]–[30] related to knowledge graphs mainly involve research on knowledge representation, knowledge graph construction, knowledge reasoning, etc., lacking of systematic review articles of knowledge graph completion.”
• A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, background): “Recently, knowledge graphs as a form of structured human knowledge have drawn great research attention from both the academia and the industry [3]–[6].”
• Knowledge Graphs: Opportunities and Challenges (2023, background): “Specifically, the limitations of existing knowledge graph technologies are the key challenges for promoting the development of knowledge graphs (Hogan et al. 2021).”
• A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, background): “[5] 2021 Overview of KG • • - - • • - • • • - • • Cai et al.”
• Knowledge Graphs 2021: A Data Odyssey (2021, background): “Comprehensive surveys on KB construction and curation are [11, 25, 38].”
• PYTHIA: Universal Question Answering over Knowledge Graphs (2026, unknown): “Knowledge graphs (KGs) have established themselves as a core technology in various fields [33], due to their rich semantic representation of entities and their relationships, as well as their ability to easily integrate diverse and heterogeneous data sources [12].”
• Enhancing patient care with AI agents: integrating advanced chatbot technologies for improved healthcare delivery (2026, unknown): “In addition to RAG, knowledge graphs are being used to enhance the functionality of AI agents by mapping patient symptoms to potential diseases Hogan et al. [13].”
• Higher-Order Knowledge Representations for Agentic Scientific Reasoning (2026, unknown): “Each edge reflects the direct connection or relationship between two specific concepts, allowing the network to represent how individual ideas are related to one another [42, 43].”
• mKG-RAG: Leveraging Multimodal Knowledge Graphs in Retrieval-Augmented Generation for Knowledge-intensive VQA (2025, unknown): “To address these limitations, a natural direction is to retrieve and leverage structured knowledge sources, such as knowledge graphs (KGs) [12, 23], which offer compact and well-organized representations to augment generation [13, 19].”
• SemRAG: Semantic Knowledge-Augmented RAG for Improved Question-Answering (2025, unknown): “It improves relevance through disambiguation, comprehensive summaries, and deeper topic connections, offering superior contextual understanding and intuitive navigation [7].”
• RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation (2025, unknown): “…integrating knowledge graphs (KGs) with LLMs (Sun et al., 2019; Yu et al., 2022; He et al., 2024; Edge et al., 2024), as KGs offer compact, structured representations of entities and relationships that enable more accurate and interpretable reasoning (Hogan et al., 2021; Jiang et al., 2024).”