Advances in deep learning methods have had a major impact on the artificial intelligence community. Some great innovations and developments have made many tasks easier. Deep learning techniques are widely used in almost every industry, including healthcare, social media, engineering, finance, and education. Deep One of his greatest inventions in learning is the Large Language Model (LLM). It’s been popular lately and mostly made headlines for its amazing use cases. These models mimic humans and harness the power of natural language processing or computer vision to demonstrate some amazing solutions.
Since then, the application of large-scale language models in the field of ontology engineering has been a topic of discussion. Ontology engineering is a branch of knowledge engineering concerned with the creation, construction, curation, evaluation and maintenance of ontologies. An ontology is basically a formalization of knowledge within a particular domain that provides a systematic vocabulary of concepts and attributes and the relationships between them to enable a common understanding of semantics between humans and machines. is an accurate specification.
While most well-known ontology APIs such as the OWL API and Jena are Java-based, deep learning frameworks such as PyTorch and Tensorflow are generally developed for Python programming. A team of researchers introduced his DeepOnto as a challenge to address this issue. DeepOnto is a Python package developed specifically for ontology engineering that enables seamless integration of frameworks and APIs.
The DeepOnto package provides comprehensive, general, and Python-friendly support for deep learning-based ontology engineering. Also, advanced features such as loading, saving, querying entities, modifying entities and axioms, and reasoning and verbalizing. It also includes tools and resources for ontology refinement, completion, and ontology-based language model exploration.
The team chose OWL API as the backend dependency for DeepOnto. This is due to the API’s characteristics such as stability, reliability, and wide adoption by prominent projects and tools such as ROBOT and HermiT. PyTorch underpins DeepOnto’s deep learning dependencies with its dynamic computing graph, enabling runtime tuning of the model’s architecture, providing flexibility and ease of use. Huggingface’s Transformers library is used for language model applications, and the OpenPrompt library is used to support the prompted learning paradigm, which is a key foundation for large language models like ChatGPT.
DeepOnto’s basic ontology processing module consists of several parts, each of which performs a specific task. The first part is Ontology, the base class of DeepOnto that provides basic methods for viewing and modifying ontologies. The second is ontology inference, which is used to perform inference activities. Then follows ontology pruning, where the ontology is retrieved and a scalable subset is extracted according to certain criteria, such as semantic type. Finally, there is the Ontology Verbalization feature, which verbalizes ontology elements into natural language text to improve ontology accessibility and assist in various ontology engineering activities.
The team leveraged two use cases to demonstrate the practicality of DeepOnto. In the first use case, DeepOnto is used to assist with ontology engineering tasks within the framework of Samsung Research UK’s Digital Health Coaching. The Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI) is his second use case, where DeepOnto is used to align and complete biomedical ontologies using deep learning techniques.
In conclusion, DeepOnto is a powerful package for ontology engineering and a great addition to developments in the field of artificial intelligence. For future implementations and projects such as embedding logic or discovering and introducing new concepts, DeepOnto provides a flexible and extensible interface.
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Tanya Malhotra is a final year student at the University of Petroleum and Energy Research, Dehradun, graduating with a Bachelor of Science in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
A data science enthusiast with good analytical and critical thinking, she has a keen interest in learning new skills, leading groups, and managing work in an organized manner.
