# Knowledge Affordances for Hybrid Human-AI Information Seeking

arXiv: https://arxiv.org/abs/2604.27539 · HTML: https://mlsanity.com/paper/2604.27539/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases](https://mlsanity.com/paper/2102.11965/) (2021)
- [A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems](https://mlsanity.com/paper/1905.12389/) (2019)
- [Mutual Theory of Mind for Human-AI Communication](https://mlsanity.com/paper/2210.03842/) (2022)
- [A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligence](https://mlsanity.com/paper/2401.03188/) (2024)
- [Large Language Models, Knowledge Graphs and Search Engines: A Crossroads for Answering Users' Questions](https://mlsanity.com/paper/2501.06699/) (2025)
- [Multi-Turn Human-LLM Interaction Through the Lens of a Two-Way Intelligibility Protocol](https://mlsanity.com/paper/2410.20600/) (2024)
- [Mutual Understanding between People and Systems via Neurosymbolic AI and Knowledge Graphs](https://mlsanity.com/paper/2504.11200/) (2025)

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

- [Discoverable Agent Knowledge -- A Formal Framework for Agentic KG Affordances (Extended Version)](https://mlsanity.com/paper/2605.19186/) (2026)
- [Knowledge Graph Re-engineering Along the Ontological Continuum (extended version)](https://mlsanity.com/paper/2605.22093/) (2026)

## Abstract (arXiv)

As information ecosystems grow more heterogeneous, both humans and artificial agents increasingly face a simple yet unresolved question: when seeking knowledge, whom should we ask, and why? Inspired by how people intuitively "read a room", this paper introduces the concept of knowledge affordance (KA) to systematize how agents identify meaningful opportunities for information seeking in hybrid human-AI environments. Rather than introducing a fully formed framework, we propose KAs as declarative, semantically grounded descriptions of what a knowledge source can offer, for which kinds of questions, and with which contextual properties. Additionally, we suggest that KAs are relational, possibly emerging from the interplay between the agent's task, preferences and situational factors. Our contribution is thus a conceptual proposal that connects different research streams, including affordances, semantic web services, knowledge engineering and querying, and mutual intelligibility. We sketch possible research directions to build KA-aware systems that navigate information spaces with greater transparency, adaptability and shared understanding.