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NeuroQL: A Neuro-Symbolic Language and Dataset for Inter-Subjective Reasoning

arXiv 2303.07146 ↗First posted 13 Mar 2023cs.PL cs.AI cs.CLcited by 1

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Abstract (arXiv)

We present a new AI task and baseline solution for Inter-Subjective Reasoning. We define inter-subjective information, to be a mixture of objective and subjective information possibly shared by different parties. Examples may include commodities and their objective properties as reported by IR (Information Retrieval) systems, that need to be cross-referenced with subjective user reviews from an online forum. For an AI system to successfully reason about both, it needs to be able to combine symbolic reasoning of objective facts with the shared consensus found on subjective user reviews. To this end we introduce the NeuroQL dataset and DSL (Domain-specific Language) as a baseline solution for this problem. NeuroQL is a neuro-symbolic language that extends logical unification with neural primitives for extraction and retrieval. It can function as a target for automatic translation of inter-subjective questions (posed in natural language) into the neuro-symbolic code that can answer them.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 57 references that are in the map. First posted on arXiv 13 Mar 2023.

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23 of 57 references.

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Citing papers per year: 2025: 1.

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