Elenchus: Generating Knowledge Bases from Prover-Skeptic Dialogues
arXiv:2603.06974v1 Announce Type: new Abstract: We present Elenchus, a dialogue system for knowledge base construction grounded in inferentialist semantics, where knowledge engineering is re-conceived as explicitation rather than extraction from expert testimony or textual content. A human expert develops a bilateral position (commitments and denials) about a topic through prover-skeptic dialogue with a large language model (LLM) opponent. The LLM proposes tensions (claims that parts of the position are jointly incoherent) which the expert resolves by retraction, refinement, or contestation. The LLM thus serves as a defeasible derivability oracle whose unreliability is structurally contained by the expert's authority. Our main technical contribution is a mapping from Elenchus dialectical states to material bases in Hlobil and Brandom's NonMonotonic MultiSuccedent (NMMS) logic, satisfying Containment and enabling the elaboration of logical vocabulary that makes explicit the inferential
arXiv:2603.06974v1 Announce Type: new Abstract: We present Elenchus, a dialogue system for knowledge base construction grounded in inferentialist semantics, where knowledge engineering is re-conceived as explicitation rather than extraction from expert testimony or textual content. A human expert develops a bilateral position (commitments and denials) about a topic through prover-skeptic dialogue with a large language model (LLM) opponent. The LLM proposes tensions (claims that parts of the position are jointly incoherent) which the expert resolves by retraction, refinement, or contestation. The LLM thus serves as a defeasible derivability oracle whose unreliability is structurally contained by the expert's authority. Our main technical contribution is a mapping from Elenchus dialectical states to material bases in Hlobil and Brandom's NonMonotonic MultiSuccedent (NMMS) logic, satisfying Containment and enabling the elaboration of logical vocabulary that makes explicit the inferential relationships negotiated in the dialectic. We demonstrate the approach on the W3C PROV-O provenance ontology, where a single dialogue session elicits and structures design tensions that a domain expert can articulate, corresponding to decisions documented in a retrospective analysis of the ontology's design. Using pyNMMS, an automated NMMS reasoner, we verify that the structural properties of the resulting material base (nontransitivity, nonmonotonicity, and independence) correspond to specific PROV design rationales, demonstrating end-to-end integration from dialogue through formal reasoning.
Executive Summary
This article presents Elenchus, a novel dialogue system for constructing knowledge bases grounded in inferentialist semantics. A human expert engages in prover-skeptic dialogue with a large language model (LLM) opponent to develop a bilateral position on a topic, resolving tensions and contradictions through a process of retraction, refinement, or contestation. The LLM serves as a defeasible derivability oracle, and the expert's authority contains the unreliability of the LLM. The article demonstrates the approach using the W3C PROV-O provenance ontology, verifying the structural properties of the resulting material base through formal reasoning. This work offers a new paradigm for knowledge engineering, re-conceiving it as explicitation rather than extraction from expert testimony or textual content.
Key Points
- ▸ Elenchus is a dialogue system for constructing knowledge bases grounded in inferentialist semantics.
- ▸ The system uses prover-skeptic dialogue between a human expert and a large language model (LLM) opponent.
- ▸ The LLM serves as a defeasible derivability oracle, and the expert's authority contains its unreliability.
- ▸ The approach is demonstrated using the W3C PROV-O provenance ontology and verified through formal reasoning.
Merits
Strength in Inferentialist Semantics
The article's use of inferentialist semantics to ground knowledge base construction offers a novel and rigorous approach to knowledge engineering.
Effective Use of Large Language Models
The LLM serves as a defeasible derivability oracle, effectively leveraging the power of AI to support expert reasoning.
Formal Reasoning and Verification
The article's use of formal reasoning and verification through pyNMMS demonstrates the robustness and reliability of the approach.
Demerits
Limited Scope and Domains
The article's demonstration using the W3C PROV-O provenance ontology may limit the generalizability of the approach to other domains and topics.
Dependence on Human Expertise
The effectiveness of the approach relies heavily on the expertise and authority of the human expert, which may introduce variability and limitations.
Complexity and Scalability
The dialogue system and formal reasoning may become complex and difficult to scale for large or complex knowledge bases.
Expert Commentary
This article presents a novel and rigorous approach to knowledge engineering, leveraging inferentialist semantics and large language models. While the approach shows promise, its limitations and potential challenges should be carefully considered. The article's reliance on human expertise and authority raises questions about the nature of expertise and its role in knowledge construction. Furthermore, the complexity and scalability of the approach may become significant challenges for large or complex knowledge bases. Nonetheless, the article offers a valuable contribution to the field of knowledge engineering and formal methods, and its implications extend to various domains and applications.
Recommendations
- ✓ Further research should investigate the scalability and complexity of the approach for large or complex knowledge bases.
- ✓ The article's reliance on human expertise and authority should be explored in more detail, including the nature of expertise and its role in knowledge construction.
- ✓ The approach should be applied in various domains and contexts to demonstrate its generalizability and effectiveness.
