AceMap：基于学术图的知识发现
AceMap: Knowledge Discovery through Academic Graph
AI总结：
针对现有科学搜索引擎忽视学术实体协作、思想演化及内容深度分析的问题，提出学术系统AceMap，通过构建大规模学术图数据库，结合可视化、结构熵度量、思想演化追踪等技术，实现学术知识发现。
摘要：
科学文献的指数级增长要求对宝贵洞见进行有效管理与提取。现有科学搜索引擎虽擅长基于关系数据库交付搜索结果，但往往忽视对科学实体间协作关系、思想演化的分析，以及对科学出版物内容的深度剖析。异构图的表示，以及对此类图的有效度量、分析与挖掘，构成了重大挑战。为应对这些挑战，我们提出AceMap——一个旨在通过学术图实现知识发现的学术系统。我们采用先进的数据库构建技术，搭建了涵盖大规模学术实体的综合AceMap数据库，这些实体包含丰富的视觉、文本与数值信息。AceMap还运用创新的可视化、量化与分析方法，以探索学术实体间的关联与逻辑关系。AceMap引入了以星云图为核心的大规模学术网络可视化技术，可从多维度提供学术网络的全局视图。此外，AceMap提出了一种基于结构熵的统一度量指标，用于量化衡量不同学术实体的知识含量。不仅如此，AceMap还具备高级分析能力，包括通过引用关系与概念共现追踪学术思想的演化，并基于这一演化过程生成简洁摘要。另外，AceMap利用机器阅读方法，在不同领域的交叉点生成潜在的新思想。探索大语言模型与知识图谱的融合，是未来思想演化研究的一个有前景的方向。更多探索请访问https://www.acemap.info。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
