# A Community-driven vision for a new Knowledge Resource for AI

arXiv: https://arxiv.org/abs/2506.16596 · HTML: https://mlsanity.com/paper/2506.16596/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 89 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Builds on (most cited first, 15 of 15)

- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Constraint Answer Set Programming without Grounding](https://mlsanity.com/paper/1804.11162/) (2018)
- [GraphEQA: Using 3D Semantic Scene Graphs for Real-time Embodied Question Answering](https://mlsanity.com/paper/2412.14480/) (2024)
- [The Defeat of the Winograd Schema Challenge](https://mlsanity.com/paper/2201.02387/) (2022)
- [Dialectical language model evaluation: An initial appraisal of the commonsense spatial reasoning abilities of LLMs](https://mlsanity.com/paper/2304.11164/) (2023)
- [Reframing Spatial Reasoning Evaluation in Language Models: A Real-World Simulation Benchmark for Qualitative Reasoning](https://mlsanity.com/paper/2405.15064/) (2024)
- [FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability](https://mlsanity.com/paper/2208.07912/) (2022)
- [Biolink Model: A Universal Schema for Knowledge Graphs in Clinical, Biomedical, and Translational Science](https://mlsanity.com/paper/2203.13906/) (2022)
- [HepLean: Digitalising high energy physics](https://mlsanity.com/paper/2405.08863/) (2024)
- [LLM+AL: Bridging Large Language Models and Action Languages for Complex Reasoning about Actions](https://mlsanity.com/paper/2501.00830/) (2025)
- [Formalizing Chemical Physics using the Lean Theorem Prover](https://mlsanity.com/paper/2210.12150/) (2022)
- [Modelling Chemical Reasoning to Predict Reactions](https://mlsanity.com/paper/1608.07117/) (2016)
- [Toward a New Science of Common Sense](https://mlsanity.com/paper/2112.12754/) (2021)
- [ASPMT(QS): Non-Monotonic Spatial Reasoning with Answer Set Programming Modulo Theories](https://mlsanity.com/paper/1506.04929/) (2015)
- [Deeply Semantic Inductive Spatio-Temporal Learning](https://mlsanity.com/paper/1608.02693/) (2016)

## Abstract (arXiv)

The long-standing goal of creating a comprehensive, multi-purpose knowledge resource, reminiscent of the 1984 Cyc project, still persists in AI. Despite the success of knowledge resources like WordNet, ConceptNet, Wolfram|Alpha and other commercial knowledge graphs, verifiable, general-purpose widely available sources of knowledge remain a critical deficiency in AI infrastructure. Large language models struggle due to knowledge gaps; robotic planning lacks necessary world knowledge; and the detection of factually false information relies heavily on human expertise. What kind of knowledge resource is most needed in AI today? How can modern technology shape its development and evaluation? A recent AAAI workshop gathered over 50 researchers to explore these questions. This paper synthesizes our findings and outlines a community-driven vision for a new knowledge infrastructure. In addition to leveraging contemporary advances in knowledge representation and reasoning, one promising idea is to build an open engineering framework to exploit knowledge modules effectively within the context of practical applications. Such a framework should include sets of conventions and social structures that are adopted by contributors.