VHAKG tools

This repository provides a set of tools for searching and extracting videos from VHAKG, a multi-modal knowledge graph (MMKG) of multi-view videos of daily activities.

Contents

• How to use
  • Prerequisites
  • GUI
  • CLI
  • SPARQL
• How to develop
• Experiments

How to use

Prerequisites

• Local machine (RAM: 32GB, HDD: free space 150GB)
  • If there is not enough free memory, loading will be skipped; increase Docker's memory allocation. We have allocated 16 GB of memory to Docker and confirmed that it works. It may work with a little less.
• Install Docker
• Download VHAKG
  

GUI

• Run mkdir RDF.

• Place VHAKG's .ttl files on RDF/ only for the first time

  • Important: Please do not place any files other than .ttl under the RDF/. Please delete .DS_Store if it exists.

• Run chmod +x entrypoint.sh only for the first time

• Run docker compose up --build -d

  • Important: If you are not using Apple Silicon, you must change the GraphDB image in compose.yaml from ontotext/graphdb:10.4.4-arm64 to ontotext/graphdb:10.4.4

• Wait for data to be loaded until the Docker GraphDB container displays the log [main] INFO com.ontotext.graphdb.importrdf.Preload - Finished.

• Open http://localhost:5050

  • Please wait a moment when you open first time, as the back-end system needs to load the activity data.

CLI

• Perform the same steps as in GUI
• Run cd cli
• Run pip install -r requirements.txt only for the first time
• Run python mmkg-search.py -h if you want to know command arguments
• Run python mmkg-search.py args

Example

Extract the video segment of the "grab" part from the camera4’s video of "clean_kitchentable1" in scene1.

python mmkg-search.py clean_kitchentable1 scene1 camera4 . -a grab

SPARQL

• Users familiar with SPARQL can use the GraphDB SPARQL endpoint at localhost:7200/sparql.

How to develop

GUI

• Run mkdir RDF only for the first time
• Place RDF Data on RDF/ only for the first time
• Run chmod +x entrypoint.sh only for the first time
• Run COMPOSE_FILE=compose.yaml:development.yaml docker compose up
• Wait for data to be loaded until the Docker GraphDB container displays the log [main] INFO com.ontotext.graphdb.importrdf.Preload - Finished.
• Open http://localhost:5051

Lint

• Run docker compose exec app-dev sh -c "cd /app && yarn lint"

Format

• Run docker compose exec app-dev sh -c "cd /app && yarn format"

CLI

Environment

• Run pyenv install miniforge3-4.14.0-2
• Run pyenv virtualenv miniforge3-4.14.0-2 vhakg-tools

Experiments

An experimental example of dataset creation and LVLM evaluation using VHAKG

Dataset creation

• Run pip install notebook
• Run jupyter notebook
• Open&Run create_benchmark_dataset.ipynb

Evaluation

GPT-4o and GPT-4V

• Run pip install openai
• Run jupyter notebook
• Open&Run evaluate_lvlm.ipynb with your OpenAI API key