Advances in deep learning methodologies are having a huge impact on the artificial intelligence community. With some great innovations and developments, a number of tasks are becoming easier. Deep learning techniques are widely used in almost every industry, be it healthcare, social media, engineering, finance, or education. One of the best deep learning inventions is Large Language Models (LLMs), which have recently become popular and mostly make headlines for their amazing use cases. These models mimic humans and, using the power of natural language processing or computer vision, demonstrate some amazing solutions.
The application of Big Language Models in the field of Ontological Engineering has been a topic of discussion ever since. Ontology engineering is a branch of knowledge engineering that deals with the creation, construction, preservation, evaluation and maintenance of ontologies. An ontology is basically a formal and precise specification of knowledge within a particular area that offers a systematic vocabulary of concepts and attributes, along with the relationships between them, to enable a shared understanding of human-machine semantics.
Popular ontology APIs, such as the OWL and Jena APIs, are primarily based on Java, while deep learning frameworks, such as PyTorch and Tensorflow, are generally developed for Python programming. This presents a challenge to address which a team of researchers introduced DeepOnto, a Python package developed specifically for ontology engineering that enables seamless integration of frameworks and APIs.
The DeepOnto package provides comprehensive, general, Python-compatible support for deep learning-based ontology engineering, and consists of an ontology processing module as a base that supports basic operations such as load, save, query entities, modify entities, and axioms. , and advanced functions such as reasoning and verbalization. It also includes tools and resources for ontology alignment and completion, and ontology-based language model probing.
The team chose the OWL API as the back-end dependency for DeepOnto. This is due to the features of the API, such as its stability, reliability, and widespread adoption in notable projects and tools like ROBOT and HermiT. PyTorch is the foundation of DeepOnto’s deep learning dependencies due to its dynamic computation graph, which allows the execution time of the model architecture to be tuned, offering flexibility and ease of use. Huggingface’s Transformers library has been used for language model applications, and the OpenPrompt library has been used to support the fast learning paradigm, which is a crucial building block for large language models like ChatGPT.
DeepOnto’s basic ontology processing module is made up of a number of parts, each of which performs a particular task: the first is Ontology, the DeepOnto base class that provides the fundamental methods for viewing and changing an ontology. Second, there is ontology reasoning, which is used to perform reasoning activities, followed by ontology pruning in which an ontology is taken and a scalable subset is extracted based on particular criteria, such as semantic types. Lastly, Ontology Verbalization is there, which improves ontology accessibility and helps in a variety of ontology engineering activities by verbalizing ontology elements in natural language text.
The team has demonstrated the practical utility of DeepOnto with the help of two use cases. In the first use case, DeepOnto was used to support ontology engineering within the framework of Digital Health Coaching at Samsung Research UK. The Ontology Alignment Evaluation Initiative (OAEI) Bio-ML track is the second use case, where DeepOnto has been used to align and finalize biomedical ontologies using deep learning techniques.
In conclusion, DeepOnto is a strong package for ontology engineering and is a great addition to developments in the field of Artificial Intelligence. For future implementations and projects such as logic embeddings and the discovery and introduction of 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 Power Studies, Dehradun, studying BTech in Computer Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
