# SKG: A Versatile Information Retrieval and Analysis Framework for Academic Papers with Semantic Knowledge Graphs

arXiv: https://arxiv.org/abs/2306.04758 · HTML: https://mlsanity.com/paper/2306.04758/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 63 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 12 of 12)

- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering](https://mlsanity.com/paper/1908.08167/) (2019)
- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation](https://mlsanity.com/paper/1812.09912/) (2018)
- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles](https://mlsanity.com/paper/2104.00948/) (2021)
- [COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature](https://mlsanity.com/paper/2007.12731/) (2020)
- [Generating Knowledge Graphs by Employing Natural Language Processing and Machine Learning Techniques within the Scholarly Domain](https://mlsanity.com/paper/2011.01103/) (2020)
- [VitaLITy: Promoting Serendipitous Discovery of Academic Literature with Transformers & Visual Analytics](https://mlsanity.com/paper/2108.03366/) (2021)
- [Toward a deeper understanding of Visualization through keyword analysis](https://mlsanity.com/paper/1408.3297/) (2014)
- [Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation](https://mlsanity.com/paper/2110.06354/) (2021)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Tree-structured Knowledge Graph For Academic Insight Survey](https://mlsanity.com/paper/2402.04854/) (2024)

## Abstract (arXiv)

The number of published research papers has experienced exponential growth in recent years, which makes it crucial to develop new methods for efficient and versatile information extraction and knowledge discovery. To address this need, we propose a Semantic Knowledge Graph (SKG) that integrates semantic concepts from abstracts and other meta-information to represent the corpus. The SKG can support various semantic queries in academic literature thanks to the high diversity and rich information content stored within. To extract knowledge from unstructured text, we develop a Knowledge Extraction Module that includes a semi-supervised pipeline for entity extraction and entity normalization. We also create an ontology to integrate the concepts with other meta information, enabling us to build the SKG. Furthermore, we design and develop a dataflow system that demonstrates how to conduct various semantic queries flexibly and interactively over the SKG. To demonstrate the effectiveness of our approach, we conduct the research based on the visualization literature and provide real-world use cases to show the usefulness of the SKG. The dataset and codes for this work are available at https://osf.io/aqv8p/?view_only=2c26b36e3e3941ce999df47e4616207f.