从文献重建领域开放问题目录：双层知识图谱与“状态必须保持 Proposed”的设计结论
Rebuilding a Field's Open-Problem Catalog from the Literature: A Two-Layer Knowledge Graph, and Why Status Must Stay Proposed
AI 导读
一项 arXiv 研究提出用双层知识图谱把研究领域的开放问题变成可查询数据：人工策展的问题层保持不变，证据层由大语言模型从文献索引重建，模型负责抽取、目录对齐、状态判定和想法生成，且被约束只能回答候选集内答案。
正文 · 原文
Abstract:A research field records its open problems in surveys, keynotes and community reports. Those are prose, written every few years, and nobody can ask them who works on a problem now or whether it was settled last year. We argue the problems should be data, and we build the system that makes them so: a hand-curated problem layer carried in unchanged, under an evidence layer rebuilt from a bibliographic index, joined by typed, provenance-carrying links in one graph database and queryable from one language. A language model does four jobs -- per-paper extraction, catalog grounding, per-paper status verdict, and idea generation -- each constrained to a candidate set so out-of-set answers are refused rather than stored. We report a study on two curated catalogs, database research (1,053 problems) and AI research (1,000 problems), each with a prior catalog-only graph. The rebuilt graph answers every question the catalog-only graph answered -- 47 of 47 on databases, 34 of 35 on AI -- plus questions the catalog-only graph refused: recent workers, untouched problems, methods tried, and outside papers bearing on a problem (336 problems, 770 works on databases). The capability we most wanted does not survive its own audit. Of 41 solves and refutes verdicts hand-checked against the papers, 9 are correct, 30 partly correct and 2 wrong; no refutes verdict (0 of 34) disproves an open problem, and only 1 of 7 solves closes the central question. We report that failure in full and draw the design conclusion: a status change must be proposed with its evidence and never applied, making the human reviewer part of the architecture. We close with the data-engineering problems this raises: entity resolution across curated and harvested layers, verifying model status claims, freshness under source quotas, and evaluation without ground truth.
| Comments: | 9 pages, 1 figure, 7 tables. Code and data: this https URL |
| Subjects: | Databases (cs.DB); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL) |
| ACM classes: | H.2.8; H.3.7; I.2.4 |
| Cite as: | arXiv:2610.39641 [cs.DB] |
| (or arXiv:2610.39641v1 [cs.DB] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39641 arXiv-issued DOI via DataCite |
Submission history
From: Sandeep Kunkunuru [view email]
[v1]
Thu, 29 Oct 2026 09:01:47 UTC (85 KB)
来源：arXiv cs.AI · arxiv.org