# AceMap: Knowledge Discovery through Academic Graph

arXiv: https://arxiv.org/abs/2403.02576 · HTML: https://mlsanity.com/paper/2403.02576/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [TableBank: A Benchmark Dataset for Table Detection and Recognition](https://mlsanity.com/paper/1903.01949/) (2019)
- [Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references](https://mlsanity.com/paper/1402.4578/) (2014)
- [Pretrained Language Models for Sequential Sentence Classification](https://mlsanity.com/paper/1909.04054/) (2019)
- [Exploring and Verbalizing Academic Ideas by Concept Co-occurrence](https://mlsanity.com/paper/2306.02282/) (2023)
- [Latent Tree Models for Hierarchical Topic Detection](https://mlsanity.com/paper/1605.06650/) (2016)
- [CRNN: A Joint Neural Network for Redundancy Detection](https://mlsanity.com/paper/1706.01069/) (2017)
- [DeepShovel: An Online Collaborative Platform for Data Extraction in Geoscience Literature with AI Assistance](https://mlsanity.com/paper/2202.10163/) (2022)
- [Towards Controlled Table-to-Text Generation with Scientific Reasoning](https://mlsanity.com/paper/2312.05402/) (2023)
- [IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications](https://mlsanity.com/paper/2209.13243/) (2022)
- [KnowledgeShovel: An AI-in-the-Loop Document Annotation System for Scientific Knowledge Base Construction](https://mlsanity.com/paper/2210.02830/) (2022)

## Built on by (most cited first, 2 of 2)

- [CHAINSFORMER: Numerical Reasoning on Knowledge Graphs from a Chain Perspective](https://mlsanity.com/paper/2504.14282/) (2025)
- [Compass: Navigating Global Marine Lead Data Integration through Expert-Guided LLM Agent](https://mlsanity.com/paper/2605.29966/) (2026)

## Abstract (arXiv)

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 urlhttps://www.acemap.info for further exploration.