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 publications 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 https://www.acemap.info for
further exploration.

科学文献的快速增长需要有效管理和提取有价值的见解。AceMap 是为了通过学术图来实现知识发现而设计的学术系统，它提供了高级数据库构建技术、创新的可视化和分析方法以及追踪学术观点演变、生成简洁摘要等先进分析功能。