# BiblioDAP: The 1st Workshop on Bibliographic Data Analysis and Processing

arXiv: https://arxiv.org/abs/2106.12320 · HTML: https://mlsanity.com/paper/2106.12320/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks](https://mlsanity.com/paper/1903.06464/) (2019)
- [Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic](https://mlsanity.com/paper/2005.10732/) (2020)
- [Author name disambiguation of bibliometric data: A comparison of several unsupervised approaches](https://mlsanity.com/paper/1904.12746/) (2019)
- [MexPub: Deep Transfer Learning for Metadata Extraction from German Publications](https://mlsanity.com/paper/2106.07359/) (2021)
- [Bibliometric-enhanced Information Retrieval 10th Anniversary Workshop Edition](https://mlsanity.com/paper/2001.10336/) (2020)

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

- [Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data](https://mlsanity.com/paper/2107.04382/) (2021)
- [Multimodal Approach for Metadata Extraction from German Scientific Publications](https://mlsanity.com/paper/2111.05736/) (2021)

## Abstract (arXiv)

Automatic processing of bibliographic data becomes very important in digital libraries, data science and machine learning due to its importance in keeping pace with the significant increase of published papers every year from one side and to the inherent challenges from the other side. This processing has several aspects including but not limited to I) Automatic extraction of references from PDF documents, II) Building an accurate citation graph, III) Author name disambiguation, etc. Bibliographic data is heterogeneous by nature and occurs in both structured (e.g. citation graph) and unstructured (e.g. publications) formats. Therefore, it requires data science and machine learning techniques to be processed and analysed. Here we introduce BiblioDAP'21: The 1st Workshop on Bibliographic Data Analysis and Processing.