Construct knowledge graphs from unstructured data sources, use graph algorithms for enhanced GraphRAG with a DSPy-based chat bot locally, and curate semantics for optimizing AI app outcomes within a specific domain.

This repository contains a notebook demonstrating a practical implementation of the so-called Entity Embedding for Encoding Categorical Features for Training a Neural Network.

Discover relevant information about categorical data with entity embeddings using Neural Networks (powered by Keras)

Entity Embedding with LSTM for Time Series

Connected Data London 2025 Masterclass: Combining Data from Structured and Unstructured Sources to create High-Quality Knowledge Graphs

This repo contains various data science strategy and machine learning models to deal with structure as well as unstructured data. It contains module on feature-preprocessing, feature-engineering, machine-learning-models, bayesian-parameter-tuning, etc, built using libraries such as scikit-learn, keras, h2o, xgboost, lightgbm, catboost, etc.

Semantics Driven Embedding Learning for Effective Entity Alignment, ICDE 2022

Deep Learning and Entity Embeddings to predict driving behaviour and cluster accident hotspots

A small library that can encode categorical variables to entity embeddings using a TensorFlow 2.0 neural network. Supports classification and regression problems. Network parameters are adjustable.

Train word2vec and keeps all entities regardless of their frequency

Kaggle Competition