The paper presents a system that transforms open‑problem listings in research fields into a structured, queryable knowledge graph. It combines a hand‑curated problem layer with an evidence layer derived from bibliographic data, linked through typed, provenance‑carrying connections. The authors evaluate the approach on database and AI problem catalogs, showing that the new graph can answer all previously answerable queries and additional ones about recent work, but also reveal limitations in automated status judgments, leading to a design recommendation that status changes must be proposed with evidence and reviewed by humans.
By Madhulatha Mandarapu, Sandeep Kunkunuru
arXiv:2610.02679v1 Announce Type: new
Abstract: Data journalism, the practice of using data analysis to surface newsworthy stories, depends increasingly on the ability of reporters and investigative...
By Raquib Bin Yousuf, Harith Laxman, Vitaliy Shkremetko, Eunice Son, Shambhavi Verma, Brian O'Leary, Venketesh Subramony, Sylvain Nazef, Jacquelyn Elias, Ron Coddington, Chris Contakes, Michael Riley, Naren Ramakrishnan
arXiv:2606. 05724v1 Announce Type: cross Abstract: Long-form narrative QA requires reasoning over evolving story worlds rather than isolated passages: answers may depend on earlier goals, changing character states, social relations, causal triggers, temporal position, and later consequences.
By Qiuyu Tian, Fengyi Chen, Yiding Li, Youyong Kong, Fan Guo, Yuyao Li, Jinjing Shen, Zhijing Xie, Yiyun Luo, Xin Zhang, Yingce Xia, Zequn Liu
ArcRAG is a retrieval-augmented generation system that operates over knowledge graphs without relying on construction‑specific schemas or relation names. It dynamically induces a query‑local graph interface at inference time, plans multi‑hop traversals, and executes them as Cypher queries. Experiments on MuSiQue, HotpotQA, and TriviaQA show that ArcRAG remains effective across both schema‑guided and open‑information‑extraction graph constructions, demonstrating that retrieval can be construction‑agnostic.
By Jyotin Goel, Abhinav Arun, Tejas Prakash Agarwal, Bhaskarjit Sarmah, Stefano Pasquali
arXiv:2606. 09648v1 Announce Type: cross Abstract: Multi-modal data management has emerged as a central research topic in the database community, spanning data integration, semantic query processing, and data quality assessment.
By Luciano Duarte, Olga Ovcharenko, Sebastian Schelter
Build2SPARQL is a large-scale benchmark dataset for translating natural-language questions into SPARQL queries over building knowledge graphs. The dataset is generated by a KG‑grounded pipeline that produces 6,136 executable SPARQL queries and 30,680 corresponding natural-language questions across six query-pattern families and five vocabulary registers, covering 201 building KGs. Human validation shows high semantic fidelity, naturalness, and operational plausibility, and retrieval‑augmented evaluation demonstrates significant accuracy gains for open‑weight language models.
By Wooyoung Jung