Generating Reliable Adverse event Profiles for Health through Automated Integrated Data (GRAPH-AID): A Semi-Automated Ontology Building Approach

2025-06-25

AI Summary

The rapid growth of adverse drug reaction (ADR) data exacerbates challenges in integrating Neo4j knowledge graphs with OWL ontologies, particularly due to the high barrier to entry for ontology engineering and the syntactic complexity of description logic. Method: This paper proposes a user-friendly, semi-automatic conversion framework that maps Neo4j schema and instance data directly to OWL classes, object/data properties, and axioms—without requiring expertise in description logic. Implemented in Python using rdflib, the method is empirically validated on FDA Adverse Event Reporting System (FAERS) data, enabling end-to-end integration from schema-level modeling to instance-level extraction. Contribution/Results: Experimental evaluation demonstrates a substantial reduction in ontology engineering complexity and accelerated knowledge graph construction. The resulting ADR ontology effectively supports pharmacovigilance applications and public health decision-making.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

As data and knowledge expand rapidly, adopting systematic methodologies for ontology generation has become crucial. With the daily increases in data volumes and frequent content changes, the demand for databases to store and retrieve information for the creation of knowledge graphs has become increasingly urgent. The previously established Knowledge Acquisition and Representation Methodology (KNARM) outlines a systematic approach to address these challenges and create knowledge graphs. However, following this methodology highlights the existing challenge of seamlessly integrating Neo4j databases with the Web Ontology Language (OWL). Previous attempts to integrate data from Neo4j into an ontology have been discussed, but these approaches often require an understanding of description logics (DL) syntax, which may not be familiar to many users. Thus, a more accessible method is necessary to bridge this gap. This paper presents a user-friendly approach that utilizes Python and its rdflib library to support ontology development. We showcase our novel approach through a Neo4j database we created by integrating data from the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. Using this dataset, we developed a Python script that automatically generates the required classes and their axioms, facilitating a smoother integration process. This approach offers a practical solution to the challenges of ontology generation in the context of rapidly growing adverse drug event datasets, supporting improved drug safety monitoring and public health decision-making.

Problem

• Seamlessly integrating Neo4j databases with OWL ontology language
• Reducing dependency on description logics syntax for ontology creation
• Automating adverse drug event data integration for improved drug safety

Innovation

• Semi-automated ontology building using Python
• Neo4j and OWL integration via rdflib
• Automated class and axiom generation