AceMap: Knowledge Discovery through Academic Graph
Xinbing Wang, Luoyi Fu, Xiaoying Gan, Ying Wen, Guanjie Zheng, Jiaxin Ding, Liyao Xiang, Nanyang Ye, Meng Jin, Shiyu Liang, Bin Lu, Haiwen Wang, Yi Xu, Cheng Deng, Shao Zhang, Huquan Kang, Xingli Wang, Qi Li, Zhixin Guo, Jiexing Qi, Pan Liu, Yuyang Ren, Lyuwen Wu, Jungang Yang, Jianping Zhou, Chenghu Zhou
TL;DR
AceMap tackles the challenge of knowledge discovery in exponentially growing scientific literature by building AceKG, a large-scale multi-model academic graph, and by providing scalable visualization (VSAN), entropy-based knowledge quantification (KQI), and AI-assisted analysis tools for tracing idea evolution. It introduces an end-to-end pipeline (from ontology and extraction to alignment and multi-model data extraction) that enables comprehensive representation of papers, authors, venues, and fields, along with human–AI collaboration through DeepShovel, IdeaReader, and DeepReport. The work demonstrates how structural entropy can quantify knowledge content and how idea-flow analyses can reveal the origins and influence of ideas across disciplines, with practical impact for researchers navigating vast corpora. Together, these contributions support more informed discovery, cross-disciplinary synthesis, and scalable knowledge management in modern science, while highlighting future opportunities in large knowledge models and responsible AI for science.
Abstract
The exponential growth of scientific literature requires effective management and extraction of valuable insights. While existing scientific search engines excel at delivering search results based on relational databases, they often neglect the analysis of collaborations between scientific entities and the evolution of ideas, as well as the in-depth analysis of content within scientific publications. The representation of heterogeneous graphs and the effective measurement, analysis, and mining of such graphs pose significant challenges. To address these challenges, we present AceMap, an academic system designed for knowledge discovery through academic graph. We present advanced database construction techniques to build the comprehensive AceMap database with large-scale academic entities that contain rich visual, textual, and numerical information. AceMap also employs innovative visualization, quantification, and analysis methods to explore associations and logical relationships among academic entities. AceMap introduces large-scale academic network visualization techniques centered on nebular graphs, providing a comprehensive view of academic networks from multiple perspectives. In addition, AceMap proposes a unified metric based on structural entropy to quantitatively measure the knowledge content of different academic entities. Moreover, AceMap provides advanced analysis capabilities, including tracing the evolution of academic ideas through citation relationships and concept co-occurrence, and generating concise summaries informed by this evolutionary process. In addition, AceMap uses machine reading methods to generate potential new ideas at the intersection of different fields. Exploring the integration of large language models and knowledge graphs is a promising direction for future research in idea evolution. Please visit \url{https://www.acemap.info} for further exploration.