Tools for Formal Concept Analysis

Retrospective Extraction of Visual and Logical Insights for Ontology-based interpretation of Neural Networks

System for Medical Concept Extraction and Linking

Concept Explorer is an educational web app designed to make exploring concepts as exciting as exploring the physical world. It uses AI-powered data generated by the Concept Generator project to provide engaging and interactive concept exploration.

Concept Generator utilizes ChatGPT 3.5 to generate multifaceted data about concepts, which are fundamental building blocks in thinking. The project yields three types of data: Relational, Comparative, and Descriptive. It's designed for learners, educators, researchers, and anyone curious about the intricacies of human thought.

Explainability of Deep Learning Models

REST-API for LearningMiner.

CoCo-Ex extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph.

CoCo-Ex extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph.

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

The replication package of STRICT: Search Term Identification for Concept Location using Graph-Based Term Weighting

A toolkit to do concept expansion via search engine snippet

Web Of Things Ontology Inspection

MEME: Generating RNN Model Explanations via Model Extraction

CME: Concept-based Model Extraction

Clinical text-mining/machine learning project I did as part of my masters thesis at LIG.

The proposed system provides high-quality augmentations for educational texts like concept definitions, applications, equations, and examples for improved user understanding.

Python code for construction and analysis of semantic networks from text.

Severity-focused biomedical concept normalization in Python