# Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs

arXiv: https://arxiv.org/abs/1708.03310 · HTML: https://mlsanity.com/paper/1708.03310/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2017.

## Built on by (most cited first, 10 of 10)

- [Planning Like Human: A Dual-process Framework for Dialogue Planning](https://mlsanity.com/paper/2406.05374/) (2024)
- [Generating Fake Cyber Threat Intelligence Using Transformer-Based Models](https://mlsanity.com/paper/2102.04351/) (2021)
- [Thinking Fast and Slow in AI: the Role of Metacognition](https://mlsanity.com/paper/2110.01834/) (2021)
- [A Neural-Symbolic Approach to Natural Language Understanding](https://mlsanity.com/paper/2203.10557/) (2022)
- [Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments](https://mlsanity.com/paper/2201.07050/) (2022)
- [Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis](https://mlsanity.com/paper/2406.18114/) (2024)
- [A Neural Divide-and-Conquer Reasoning Framework for Image Retrieval from Linguistically Complex Text](https://mlsanity.com/paper/2305.02265/) (2023)
- [MathLearner: A Large Language Model Agent Framework for Learning to Solve Mathematical Problems](https://mlsanity.com/paper/2408.01779/) (2024)
- [Bridging Social Psychology and LLM Reasoning: Conflict-Aware Meta-Review Generation via Cognitive Alignment](https://mlsanity.com/paper/2503.13879/) (2025)
- [A Planning Ontology to Represent and Exploit Planning Knowledge for Performance Efficiency](https://mlsanity.com/paper/2307.13549/) (2023)

## Abstract (arXiv)

Knowledge graphs and vector space models are robust knowledge representation techniques with individual strengths and weaknesses. Vector space models excel at determining similarity between concepts, but are severely constrained when evaluating complex dependency relations and other logic-based operations that are a strength of knowledge graphs. We describe the VKG structure that helps unify knowledge graphs and vector representation of entities, and enables powerful inference methods and search capabilities that combine their complementary strengths. We analogize this to thinking `fast' in vector space along with thinking 'slow' and `deeply' by reasoning over the knowledge graph. We have created a query processing engine that takes complex queries and decomposes them into subqueries optimized to run on the respective knowledge graph or vector view of a VKG. We show that the VKG structure can process specific queries that are not efficiently handled by vector spaces or knowledge graphs alone. We also demonstrate and evaluate the VKG structure and the query processing engine by developing a system called Cyber-All-Intel for knowledge extraction, representation and querying in an end-to-end pipeline grounded in the cybersecurity informatics domain.