Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Amine Ferdjaoui

dblp:339/4791 · DBLP profile ↗
← Back
2ranked-venue papers
2as first author
2since 2021 · last 2025
0009-0008-3284-030XORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-author · 2 since 2021Databases, data management, data science and information retrieval · 2 · 2 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
2 papers
Information retrieval · 58% Data mining · 42%
Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval
text analysis
1.622025
Cluster Insight: A Weighted Clustering Tool for Large Textual Data Exploration · WSDM 2025
WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data · WSDM 2024
Data mining
clustering
0.912025
Cluster Insight: A Weighted Clustering Tool for Large Textual Data Exploration · WSDM 2025
Data mining › clustering
weighted clustering
0.912025
Cluster Insight: A Weighted Clustering Tool for Large Textual Data Exploration · WSDM 2025
Knowledge, reasoning and agents › Knowledge representation and reasoning › causal reasoning
causal graphical model
0.812024
WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data · WSDM 2024
Information retrieval › interactive information retrieval › exploratory search
topic exploration
0.812024
WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data · WSDM 2024
Visualization and visual analytics
interactive visualization
0.212024
WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data · WSDM 2024

Methods — techniques the papers use, named apart from their topics

causal graphical model · 1.5weighted clustering · 0.9causal graphical models · 0.8
YearPublicationVenuePosition
2025 Cluster Insight: A Weighted Clustering Tool for Large Textual Data Exploration
abstract
International audience
Amine Ferdjaoui, Séverine Affeldt, Mohamed Nadif
WSDM1
2024 WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data
abstract
We present WordGraph, a Python package for exploring the topics of documents corpora. WordGraph provides causal graphical models from text data vocabulary and proposes interactive visualizations of terms networks. Our ease-to-use package is provided with a pre-built pipeline to access the main modules through jupyter widgets. It results in the encapsulation of a whole vocabulary exploration process within a single jupyter notebook cell, with straightforward parameters settings and interactive plots. WordGraph pipeline is fully customizable by adding/removing widgets or changing default parameters. To assist users with no background in Python nor jupyter notebook, but willing to explore large corpora topics, we also propose an automatic dashboard generation from the customizable jupyter notebook pipeline in a web application style. WordGraph is available through a GitHub repository at https://github.com/MLDS-software/WordGraph.
Amine Ferdjaoui, Séverine Affeldt, Mohamed Nadif
WSDM1