Logic-Guided Data Extraction with Answer Set Programming and Large Language Models
This paper proposes a logic-guided data extraction framework combining LLM-based extraction with Answer Set Programming (ASP). The LLM produces candidate facts, whereas ASP performs validation, inference, consistency checking, and control. Unlike existing pipelines that query the LLM independently for all target predicates, the proposed approach uses ASP reasoning to identify which predicates are logically admissible at each stage and to guide extraction queries.
- ▪This paper proposes a logic-guided data extraction framework combining LLM-based extraction with Answer Set Programming (ASP).
- ▪The LLM produces candidate facts, whereas ASP performs validation, inference, consistency checking, and control.
- ▪Unlike existing pipelines that query the LLM independently for all target predicates, the proposed approach uses ASP reasoning to identify which predicates are logically admissible at each stage and to guide extraction queries.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2607.19365 |
| Publication time | Thu, 23 Jul 2026 00:00:00 -0400 |
| Retrieval time | 2026-07-23T04:57:27.447Z |
| Last seen | 2026-07-23T04:57:27.447Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | p4g1b3GQYYJZ |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2607.19365 (cs) [Submitted on 8 Jun 2026] Title:Logic-Guided Data Extraction with Answer Set Programming and Large Language Models Authors:Mario Alviano, Lorenzo Grillo, Nicola Leone, Fabrizio Lo Scudo View a PDF of the paper titled Logic-Guided Data Extraction with Answer Set Programming and Large Language Models, by Mario Alviano and 2 other authors View PDF HTML (experimental) Abstract:When Large Language Models (LLMs) are used for semantic data extraction from unstructured text, producing candidate relational facts from natural language, they may remain unreliable for tasks requiring complex combinatorial reasoning and global consistency.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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