Author Name Disambiguation for Scientific publications using Machine Learning, and Community Detection.

src contains the source code for the tasks.

src/preprocess.py contains the preprocessing methods for data extraction such as extracting all article titles, article title per paper, extracting authors of articles, and all authors in the references. It also contains the implementation of author disambiguation by grouping.

src/models.py has machine learning models implementation for classification.

src/evaluate.py has evaluation methods.

src/train.py has training methods for classification and can be run as

python train.py

src/batch_score.py has classifier prediction methods.

Before creating the environment, please unzip the data files.

Creating environment

python -m venv disambiguation
source disambiguation/bin/activate
pip install -r requirements.txt

Data Preprocessing

Extracting all article titles, article title per paper, extracting authors of articles, and all authors in the references from lxml files by parsing. It also contains the implementation of creating author dictionary which maps to article id, publication, affiliation. I have also implemented a method to count the citations of authors in references as ref author:citation dictionary. It can be found in Notebooks/Data_Processing_disambiguation_by_grouping.ipynb

Data Cleaning

on Article Title, Journal Title, Journal Subject, Affiliations: Stopwords (only English), numeric, punctuations, etc. were removed. (Stemming can also be done)

For author names: Dr., Prof., Prof.Dr., Jr., Sr., PhD., MD. were removed for standardizing author names and were converted into lower case.

Author Disambiguation Methods:

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1. Grouping by author features

Author features can be used such as affiliation, contact details, publication, research areas, co-authors to disambiguate authors with the same name. There can be multiple authors with the same name but in different institutions, different research areas, etc.

I have implemented this approach and disambiguated the authors with the same name by article id, article title, affiliation, journal title, journal subject. Then this disambiguated data was used for classification approach. The implementation of author disambiguation by grouping can be found here src/preprocess.py,

If two authors with the same name have same affiliation, email id, and overlapping co-authors then pairwise similarity matching on strings can be considered or algorithm such as Edit-distance on authors name can also be used to group same identical author with different name representation and initials such as M. Niyogi, Niyogi, M., Niyogi Mitodru, etc.

2.Classification Approach:

After preparing the disambiguated dataset after scraping from lxml files to a csv file. Given the author names, affiliations, publication, journal, research area, venue, the problem can be treated as multi-class classification to predict the author given publication. In this case, same author names with different affiliations are already being treated as disambiguated. As the dataset is very small for training, validation, and testing, the classifier is unable to learn from the limited training data as there are more than 100 authors without considering referenced authors but just 50 publications.

Authors	Article ID	Article Title	Journal Title	Journal Subject	Affiliation

TF-IDF vectorization was created to build publication features matrix and then these feature was fed into machine learning algorithms such as Decision Tree, Random Forest, Multinomial Naive Bayes, etc.

Repeated Stratified Cross validation and evaluation metrics such as precision, recall, F1-score have been considered. The implementation and experiments are available in Notebooks/Author-disam-Classification.ipynb

In the future, I would like to use Word2Vec, Glove, BERT embeddings by training on scientific articles to create contextual embeddings of scientific jargon.

3. Clustering Approach

Clustering algorithms like KMeans, Hierarchical, Agglomerative Clustering, density based clustering methods can be used for this problem. The author features can be clustered and similar author features are expected to be grouped together in the clusters.

I have also implemented clustering approach. Features like authors, article title, and references are considered and they were clustered using KMeans and Agglomerative Clustering algorithms. The clusters have been visualized by TSNE. The experiments can be found here Notebooks/Author-disam-clustering.ipynb

4. Network community detection

Construct a co-authorship network:

Create a graph where each node represents an author and an edge exists between two nodes if the corresponding authors have co-authored a paper together. The weight of the edge can be set to the number of papers co-authored by the two authors.

Building a network graph between author and co-authors Community detection algorithm such as Louvain to partition the graph into clusters where authors in the same cluster are more likely to be the same person.can be used to detect unique communities. Further information such as author affiliations, publication venues, and keywords can be used to resolve any remaining ambiguities. The experiments using Louvain algorithm can be found here Notebooks/Author-disam-clustering.ipynb

5. Deep learning and graph neural network approaches

In the future, I would like to try out SOTA architecture for author name disambiguation task.