Ontogenia and Text Analysis

Ontogenia is aimed at streamlining and enhancing the ontology engineering and knowledge graph development process by leveraging advanced artificial intelligence techniques, such as large language models (LLMs) and deep learning.

FOSSR IOPP

Objectives

The first objective of Ontogenia is to automate tasks like competency question generation, ontology creation, and knowledge graph validation;

The system aims to ensure interoperability, adherence to FAIR principles, and compliance with the Linked
Data paradigm, thereby facilitating the integration, reuse, and effective management of knowledge across diverse domains.

Ontogenia aims to streamline and enhance the ontology engineering and
knowledge graph development process by leveraging advanced artificial intelligence techniques

Description of the service

The key features of Ontogenia are:

Knowledge Graph creation from heterogeneous data sources

Automatically generation of competency questions (CQs) from user stories and structured or semi-structured datasets

Reuse of Ontology Design Patterns (ODPs) and integration with existing ontology networks like FOSSR

Production of linked data compliant with RDF standards and Linked Data principles

Use of RML to define mapping rules for data transformation

Automatic knowledge graph validation

Leveraging of Large Language Models (LLMs) and deep learning techniques for generating CQs, ontologies, and mapping rules

To whom it is addressed

The service is aimed at:

Researchers in the social, economic and political sciences;
Private, public and non-profit organisations.

Information

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*pictures credits: Yogesh Pedamkar on Unsplash