# Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities

arXiv: https://arxiv.org/abs/2304.01311 · HTML: https://mlsanity.com/paper/2304.01311/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 86 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 21 of 21)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [A Review on Language Models as Knowledge Bases](https://mlsanity.com/paper/2204.06031/) (2022)
- [Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs](https://mlsanity.com/paper/2004.11440/) (2020)
- [NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries](https://mlsanity.com/paper/2008.10723/) (2020)
- [Domain-specific Knowledge Graphs: A survey](https://mlsanity.com/paper/2011.00235/) (2020)
- [Natural SQL: Making SQL Easier to Infer from Natural Language Specifications](https://mlsanity.com/paper/2109.05153/) (2021)
- [Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs](https://mlsanity.com/paper/2101.09824/) (2021)
- [EventKG: A Multilingual Event-Centric Temporal Knowledge Graph](https://mlsanity.com/paper/1804.04526/) (2018)
- [KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation](https://mlsanity.com/paper/2107.12548/) (2021)
- [PG-Schema: Schemas for Property Graphs](https://mlsanity.com/paper/2211.10962/) (2022)
- [Facilitating Conversational Interaction in Natural Language Interfaces for Visualization](https://mlsanity.com/paper/2207.00189/) (2022)
- [Goals, Process, and Challenges of Exploratory Data Analysis: An Interview Study](https://mlsanity.com/paper/1911.00568/) (2019)
- [Persistent Homology Guided Force-Directed Graph Layouts](https://mlsanity.com/paper/1712.05548/) (2017)
- [Juniper: A Tree+Table Approach to Multivariate Graph Visualization](https://mlsanity.com/paper/1804.03261/) (2018)
- [CAVA: A Visual Analytics System for Exploratory Columnar Data Augmentation Using Knowledge Graphs](https://mlsanity.com/paper/2009.02865/) (2020)
- [A Multi-scale Visual Analytics Approach for Exploring Biomedical Knowledge](https://mlsanity.com/paper/2109.06828/) (2021)
- [Visually Connecting Historical Figures Through Event Knowledge Graphs](https://mlsanity.com/paper/2109.09380/) (2021)

## Built on by (most cited first, 9 of 9)

- [LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph Question-Answering](https://mlsanity.com/paper/2406.06621/) (2024)
- [HealthGenie: Empowering Users with Healthy Dietary Guidance through Knowledge Graph and Large Language Models](https://mlsanity.com/paper/2504.14594/) (2025)
- [The Discovery Engine: A Framework for AI-Driven Synthesis and Navigation of Scientific Knowledge Landscapes](https://mlsanity.com/paper/2505.17500/) (2025)
- [Mitigating LLM Hallucinations with Knowledge Graphs: A Case Study](https://mlsanity.com/paper/2504.12422/) (2025)
- [A Protocol for KG Construction Tasks Involving Users](https://mlsanity.com/paper/2412.16766/) (2024)
- [The Role of Visualization in LLM-Assisted Knowledge Graph Systems: Effects on User Trust, Exploration, and Workflows](https://mlsanity.com/paper/2505.21512/) (2025)
- [The Initial Exploration Problem in Knowledge Graph Exploration](https://mlsanity.com/paper/2602.21066/) (2026)
- [Context-Mediated Domain Adaptation in Multi-Agent Sensemaking Systems](https://mlsanity.com/paper/2603.24858/) (2026)
- [KGVoyager: Knowledge Graph Agnostic Question Answering via Agentic Navigation](https://mlsanity.com/paper/2609.01780/) (2026)

## Abstract (arXiv)

This study presents insights from interviews with nineteen Knowledge Graph (KG) practitioners who work in both enterprise and academic settings on a wide variety of use cases. Through this study, we identify critical challenges experienced by KG practitioners when creating, exploring, and analyzing KGs that could be alleviated through visualization design. Our findings reveal three major personas among KG practitioners - KG Builders, Analysts, and Consumers - each of whom have their own distinct expertise and needs. We discover that KG Builders would benefit from schema enforcers, while KG Analysts need customizable query builders that provide interim query results. For KG Consumers, we identify a lack of efficacy for node-link diagrams, and the need for tailored domain-specific visualizations to promote KG adoption and comprehension. Lastly, we find that implementing KGs effectively in practice requires both technical and social solutions that are not addressed with current tools, technologies, and collaborative workflows. From the analysis of our interviews, we distill several visualization research directions to improve KG usability, including knowledge cards that balance digestibility and discoverability, timeline views to track temporal changes, interfaces that support organic discovery, and semantic explanations for AI and machine learning predictions.