Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering
The article discusses a new framework called DualGraph designed for semi-structured question answering. It combines semantic retrieval and symbolic querying to improve the effectiveness of retrieval-augmented generation systems. The authors also introduce a benchmark dataset, SpecsQA, to evaluate the performance of their method against existing approaches.
- ▪DualGraph represents documents through a Textual Knowledge Graph for semantic retrieval and a Symbolic Knowledge Graph for symbolic querying.
- ▪The framework aims to address the limitations of current retrieval methods on semi-structured corpora.
- ▪Experiments demonstrate that DualGraph outperforms state-of-the-art methods in various question-answering scenarios.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.27164 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| 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 | Av3gv6w-tV8j |
| 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 |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| 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:2605.27164 (cs) [Submitted on 26 May 2026] Title:Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering Authors:Mateusz Czyżnikiewicz, Ryszard Tuora, Adam Kozakiewicz, Tomasz Ziętkiewicz, Mateusz Galiński, Michał Godziszewski, Michał Karpowicz, Timothy Hospedales, Cristina Cornelio View a PDF of the paper titled Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering, by Mateusz Czy\.znikiewicz and 8 other authors View PDF HTML (experimental) Abstract:Retrieval-Augmented Generation (RAG) systems for question answering typically retrieve evidence by semantic similarity between the query and document chunks.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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