The zbMATH Open Knowledge Graph is a large-scale RDF knowledge graph that spans more than 250 years of mathematical scholarship. It goes beyond traditional bibliographic metadata by incorporating expert-curated semantic content such as reviews, keywords, subject classifications, software references, and disambiguated authorship. With 34 million entities and 168 million RDF triples, the graph enables fine-grained, historically grounded exploration of mathematical concepts, research fields, and scholarly relationships over time.
By Yuni Susanti, Moritz Schubotz
arXiv:2608. 15193v1 Announce Type: cross Abstract: As large language model (LLM) agents are increasingly adopted in scientific research, external knowledge bases, knowledge graphs, and long-term memory have improved information retrieval and task continuity.
By Yuyang Zheng, Nan Li, Wenxia Deng, Lige Yan, Xiang Li, Si Chen
The paper introduces the Large Knowledge Model (LKM), a scientific knowledge infrastructure that converts research literature into shared, computationally accessible reasoning graphs. LKM aligns questions, claims, and reasoning chains across papers, creating a Scientific Reasoning Landscape with Question, Workflow, and Evidence views. The system enhances scientific search, evidence‑grounded QA, and research planning, achieving notable accuracy gains on ChemBench, PubMedQA, and SciBench.
By Yuan Huang, Sihan Hu, Hongyu Gu, Chao Ma, Jiaxing Zhang, Zhiyong Zou, Caiyu Fan, Yan Xiao, Mingjun Xu, Chenyu Xie, Mingzhen Ju, Zhehao Ma, Qi Zhang, Baozong Wang, Yu Li, Zhiyuan Yao, Ruoxue Liao, Xinyu Li, Linfeng Zhang, Kun Chen, Weinan E
arXiv:2608. 14228v1 Announce Type: new Abstract: Life science knowledge graphs make large collections of structured data available through SPARQL, but each resource uses its own schema, identifiers, and links.
By Yiming Zhang, Koji Tsuda