# Knowledge Graphs: The Future of Data Integration and Insightful Discovery

arXiv: https://arxiv.org/abs/2502.15689 · HTML: https://mlsanity.com/paper/2502.15689/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 51 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Domain-specific Knowledge Graphs: A survey](https://mlsanity.com/paper/2011.00235/) (2020)
- [CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context](https://mlsanity.com/paper/2212.10007/) (2022)
- [Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases](https://mlsanity.com/paper/2009.11564/) (2020)
- [A Toolkit for Generating Code Knowledge Graphs](https://mlsanity.com/paper/2002.09440/) (2020)
- [An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice](https://mlsanity.com/paper/2003.00344/) (2020)
- [Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey](https://mlsanity.com/paper/2205.04712/) (2022)
- [A Survey on Knowledge Graph-based Methods for Automated Driving](https://mlsanity.com/paper/2210.08119/) (2022)
- [Dynamic Relevance Graph Network for Knowledge-Aware Question Answering](https://mlsanity.com/paper/2209.09947/) (2022)
- [Automated Climate Analyses Using Knowledge Graph](https://mlsanity.com/paper/2110.11039/) (2021)
- [Utilizing Background Knowledge for Robust Reasoning over Traffic Situations](https://mlsanity.com/paper/2212.07798/) (2022)

## Built on by (most cited first, 2 of 2)

- [The KG-ER Conceptual Schema Language](https://mlsanity.com/paper/2508.02548/) (2025)
- [Robotic Fire Risk Detection based on Dynamic Knowledge Graph Reasoning: An LLM-Driven Approach with Graph Chain-of-Thought](https://mlsanity.com/paper/2509.00054/) (2025)

## Abstract (arXiv)

Knowledge graphs are an efficient method for representing and connecting information across various concepts, useful in reasoning, question answering, and knowledge base completion tasks. They organize data by linking points, enabling researchers to combine diverse information sources into a single database. This interdisciplinary approach helps uncover new research questions and ideas. Knowledge graphs create a web of data points (nodes) and their connections (edges), which enhances navigation, comprehension, and utilization of data for multiple purposes. They capture complex relationships inherent in unstructured data sources, offering a semantic framework for diverse entities and their attributes. Strategies for developing knowledge graphs include using seed data, named entity recognition, and relationship extraction. These graphs enhance chatbot accuracy and include multimedia data for richer information. Creating high-quality knowledge graphs involves both automated methods and human oversight, essential for accurate and comprehensive data representation.