arXiv AI

The zbMATH Open Knowledge Graph: Tracing Centuries of Mathematical Research

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.

arXiv AI
Aug 10

SCALE: Scientific Concept Aggregation via LLMs and Embeddings for Fine-Grained Taxonomy Extension

arXiv:2608. 07254v1 Announce Type: cross Abstract: The increasing specialization of scientific research challenges existing classification systems, which provide effective representations of broad disciplines and research topics but often fail to capture the fine-grained conceptual structure of contemporary science.

By Daniele Raimondi, Feichi Lu, Oliver Grun, Mariia Eremina, Andrea Perlato
arXiv AI
Jul 24

From Static Bibliometrics to Dynamic Knowledge Graphs: An LLM-Powered Framework for Modernizing Science, Technology, and Innovation (STI) Analytics

arXiv:2607. 21327v1 Announce Type: cross Abstract: Bibliometric indicators - citation counts, h-indexes, co-authorship networks - have long anchored science, technology, and innovation (STI) analytics, yet suffer from temporal lag, semantic shallowness, and an inability to capture the non-linear dynamics of contemporary knowledge ecosystems.

By Muhsen Hammoud
arXiv AI
Jun 12

Agents-K1: Towards Agent-native Knowledge Orchestration

arXiv:2606. 13669v1 Announce Type: new Abstract: Current LLM-based research agents have advanced through agent orchestration, yet largely overlook scientific knowledge orchestration.

By Zongsheng Cao, Bihao Zhan, Jinxin Shi, Jiong Wang, Fangchen Yu, Zhijie Zhong, Zijie Guo, Tianshuo Peng, Zhuo Liu, Yi Xie, Xiang Zhuang, Yue Fan, Runmin Ma, Shiyang Feng, Xiangchao Yan, Anran Liu, Peng Ye, Wenlong Zhang, Shufei Zhang, Chunfeng Song, Fenghua Ling, Jie Zhou, Liang He, Bo Zhang, Lei Bai
arXiv AI
Jun 19

Charting the Future of Scholarly Knowledge with AI: A Community Perspective

arXiv:2509. 02581v2 Announce Type: replace-cross Abstract: Despite the growing availability of tools designed to support scholarly knowledge extraction and organization, many researchers still rely on manual methods, sometimes due to unfamiliarity with existing technologies or limited access to domain-adapted solutions.

By Azanzi Jiomekong, Hande K\"u\c{c}\"uk McGinty, Keith G. Mills, Allard Oelen, Enayat Rajabi, Harry McElroy, Antrea Christou, Anmol Saini, Janice Anta Zebaze, Hannah Kim, Anna M. Jacyszyn, Gollam Rabby, Dirk Betz, Claudia Biniossek, Sanju Tiwari, S\"oren Auer
arXiv AI
Sep 16

AquiLLM: Evaluating Faithfulness in Open-Weight RAG-LLM Systems for Scientific Research

arXiv:2609.16519v1 Announce Type: new Abstract: Scientific research increasingly relies on large, heterogeneous data sources, motivating interest in retrieval-augmented generation (RAG) systems that...

By Bernie Boscoe, Srinath Saikrishnan, Vikram Seenivasan, Jack Stark, Andrew Lizarraga, Morgan Himes, Jonathan Soriano, PJ Allen, Tuan Do