wikontic 0.0.4

Extract a knowledge graph with LLM from texts and perform QA over the resulted KG

Wikontic

Build ontology-aware, Wikidata-aligned knowledge graphs from raw text using LLMs

---

🚀 Overview

Knowledge Graphs (KGs) provide structured, verifiable representations of knowledge, enabling fact grounding and empowering large language models (LLMs) with up-to-date, real-world information. However, creating high-quality KGs from open-domain text is challenging due to issues like redundancy, inconsistency, and lack of alignment with formal ontologies.

Wikontic is a multi-stage pipeline for constructing ontology-aligned KGs from unstructured text using LLMs and Wikidata. It extracts candidate triples from raw text, then refines them through ontology-based typing, schema validation, and entity deduplication—resulting in compact, semantically coherent graphs.

---

📁 Repository Structure

• preprocessing/constraint-preprocessing.ipynb
  Jupyter notebook for collecting constraint rules from Wikidata.

• utils/
  Utilities for LLM-based triple extraction and alignment with Wikidata ontology rules.

• utils/openai_utils.py
LLMTripletExtractor class for LLM-based triple extraction.

To use ontology:

• utils/ontology_mappings/
  JSON files containing ontology mappings from Wikidata.

• utils/structured_inference_with_db.py

  • StructuredInferenceWithDB class: triple extraction and qa functions

• utils/structured_aligner.py

  • Aligner class: ontology alignment and entity name refinement

Not to use ontology:

• utils/inference_with_db.py

  • InferenceWithDB class: triple extraction and qa functions

• utils/dynamic_aligner.py

  • Aligner class: entity and relation name refinement

Evaluation:

• inference_and_eval/
  • Scripts for building KGs for MuSiQue and HotPot datasets and evaluation of QA performance
• analysis/
  • Notebooks with downstream analysis of the resulted KG

Use Wikontic as a service:

• pages/ and Wikontic.py
  Code for the web service for knowledge graph extraction and visualization.

• Dockerfile
  For building a containerized web service.

---

🏁 Getting Started

1. Set up the ontology and KG databases:

   ./setup_db.sh
   

2. Launch the web service:

   streamlit run Wikontic.py
   

---

Enjoy building knowledge graphs with Wikontic!