DeepOnto: A Python Package for Ontology Engineering with Deep Learning
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866909133000671232 |
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| author | He, Yuan Chen, Jiaoyan Dong, Hang Horrocks, Ian Allocca, Carlo Kim, Taehun Sapkota, Brahmananda |
| author_facet | He, Yuan Chen, Jiaoyan Dong, Hang Horrocks, Ian Allocca, Carlo Kim, Taehun Sapkota, Brahmananda |
| contents | Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need of a platform that supports both paradigms. Although packages such as OWL API and Jena offer robust support for basic ontology processing features, they lack the capability to transform various types of information within ontologies into formats suitable for downstream deep learning-based applications. Moreover, widely-used ontology APIs are primarily Java-based while deep learning frameworks like PyTorch and Tensorflow are mainly for Python programming. To address the needs, we present DeepOnto, a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more "Pythonic" manner and extending its capabilities to incorporate other essential components including reasoning, verbalisation, normalisation, taxonomy, projection, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of DeepOnto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_03067 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DeepOnto: A Python Package for Ontology Engineering with Deep Learning He, Yuan Chen, Jiaoyan Dong, Hang Horrocks, Ian Allocca, Carlo Kim, Taehun Sapkota, Brahmananda Artificial Intelligence Computation and Language Machine Learning Logic in Computer Science Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need of a platform that supports both paradigms. Although packages such as OWL API and Jena offer robust support for basic ontology processing features, they lack the capability to transform various types of information within ontologies into formats suitable for downstream deep learning-based applications. Moreover, widely-used ontology APIs are primarily Java-based while deep learning frameworks like PyTorch and Tensorflow are mainly for Python programming. To address the needs, we present DeepOnto, a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more "Pythonic" manner and extending its capabilities to incorporate other essential components including reasoning, verbalisation, normalisation, taxonomy, projection, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of DeepOnto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI). |
| title | DeepOnto: A Python Package for Ontology Engineering with Deep Learning |
| topic | Artificial Intelligence Computation and Language Machine Learning Logic in Computer Science |
| url | https://arxiv.org/abs/2307.03067 |