arXiv cs.AI· Madhulatha Mandarapu, Sandeep Kunkunuru·· 14 小时前AI 评分31
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"提议"的原因
Rebuilding a Field's Open-Problem Catalog from the Literature: A Two-Layer Knowledge Graph, and Why Status Must Stay Proposed
AI 导读
研究者构建了一个双层知识图谱系统，将手工整理的开放问题层与从文献索引重建的证据层通过带来源标注的类型化链接连接，并在数据库研究（1,053 个问题）和 AI 研究（1,000 个问题）两个目录上验证。
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