TL;DR:
- Deep Learning methodologies have revolutionized the AI community.
- Large Language Models (LLMs) offer incredible solutions through NLP and Computer Vision.
- DeepOnto is a Python package that integrates deep learning with ontology engineering.
- It supports ontology processing, alignment, completion, and language model probing.
- DeepOnto leverages the OWL API for stability and PyTorch for flexibility.
- It enhances ontology accessibility through verbalization and reasoning capabilities.
- DeepOnto has been successfully applied to Digital Health Coaching and Bio-ML ontologies.
- It represents a significant advancement in AI and provides a flexible interface for future implementations.
Main AI News:
The field of Artificial Intelligence (AI) has witnessed remarkable advancements in recent years, thanks to the continuous evolution of Deep Learning methodologies. These groundbreaking innovations have significantly impacted various industries, ranging from healthcare and social media to engineering, finance, and education. Among the most remarkable developments in Deep Learning are Large Language Models (LLMs), which have gained immense popularity for their incredible use cases. These models possess the remarkable ability to emulate human-like behavior and leverage Natural Language Processing (NLP) and Computer Vision to provide astounding solutions.
The application of Large Language Models in Ontology Engineering has been the center of attention and discussion. Ontology engineering, a crucial branch of knowledge engineering, revolves around the creation, development, curation, assessment, and maintenance of ontologies. An ontology serves as a formal and precise specification of knowledge within a specific domain, offering a systematic vocabulary of concepts and attributes, along with their interrelationships. Its primary goal is to establish a shared understanding of semantics between humans and machines.
However, traditional ontology APIs like the OWL API and Jena are predominantly Java-based, while deep learning frameworks such as PyTorch and TensorFlow are primarily developed for the Python programming language. Bridging this gap presented a challenge that needed to be addressed. In response, a team of dedicated researchers introduced DeepOnto—a Python package specifically designed for ontology engineering. DeepOnto seamlessly integrates deep learning frameworks and APIs, offering a comprehensive, general, and Python-friendly support system.
At the core of the DeepOnto package lies its ontology processing module, which serves as the foundation for deep learning-based ontology engineering. This module facilitates fundamental operations, including loading, saving, querying entities, modifying entities and axioms, and advanced functions like reasoning and verbalization. Furthermore, it provides a wide range of tools and resources for ontology alignment, completion, and ontology-based language model probing.
DeepOnto leverages the OWL API as its backend dependency. The choice of the OWL API is driven by its stability, reliability, and widespread adoption in renowned projects and tools such as ROBOT and HermiT. On the other hand, DeepOnto relies on PyTorch as the foundation for its deep learning dependencies. PyTorch’s dynamic computing graph enables runtime adjustments to the model’s architecture, providing unparalleled flexibility and usability. Additionally, the language model applications in DeepOnto utilize Huggingface’s Transformers library, while the OpenPrompt library supports the prompt learning paradigm—an essential foundation for large language models like ChatGPT.
The ontology processing module of DeepOnto comprises several components, each dedicated to specific tasks. First and foremost is the Ontology class, which serves as the fundamental entity for viewing and modifying ontologies. Following that, the ontology reasoning component facilitates reasoning activities, while ontology pruning allows the extraction of a scalable subset from an ontology based on specific criteria, such as semantic categories. Lastly, the Ontology Verbalization component enhances ontology accessibility by transforming ontology elements into natural language text, thereby aiding various ontology engineering activities.
To showcase the practical utility of DeepOnto, the team has successfully applied it to two distinct use cases. The first use case involves leveraging DeepOnto for ontology engineering tasks within the framework of Digital Health Coaching at Samsung Research UK. The second use case revolves around the Ontology Alignment Evaluation Initiative (OAEI)’s Bio-ML track, where DeepOnto plays a vital role in aligning and refining biomedical ontologies using deep learning techniques.
Conclusion:
The introduction of DeepOnto, a Python package for ontology engineering with deep learning, signifies a transformative advancement for the market. By seamlessly integrating deep learning frameworks and ontology APIs, DeepOnto empowers organizations to leverage the power of Large Language Models for ontology-related tasks. This provides a competitive edge, as it streamlines and enhances the efficiency of ontology engineering processes. With its comprehensive support, flexible interface, and successful real-world applications, DeepOnto is poised to drive innovation and enable the development of cutting-edge AI solutions in various industries. Businesses that adopt DeepOnto can expect improved productivity, enhanced semantic understanding, and accelerated advancements in their respective domains.
