Yuan He
Yuan He
Wolfson Building, Parks Road, Oxford OX1 3QD
Interests
My primary research interests lie in the area of neural-symbolic AI. In my PhD, I have been working on a range of ontology engineering tasks including ontology alignment, completion, and embeddings. I also have a NLP background mainly studying language models, and a general mathematical background. I am now interested in constructing geometric space for knowledge representation.
Biography
Hi! I am Yuan He (in Chinese: 何源), a PhD candidate in Computer Science supervised by Professor Ian Horrocks and Professor Bernardo Cuenca Grau at the University of Oxford.
Before my PhD, I took my undergraduate study at the University of Edinburgh and attained the best performance prize for the BSc (Hons) AI & Maths degree. My final year project supervised by Dr Shay B. Cohen has led to a publication about machine transliteration.
Please refer to my page at https://www.yuanhe.wiki/ for more information.
Selected Publications
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BERTMap: A BERT−based Ontology Alignment System
Yuan He‚ Jiaoyan Chen‚ Denvar Antonyrajah and Ian Horrocks
In Proceedings of 36th AAAI Conference on Artificial Intelligence 2022 (AAAI−2022). 2022.
Details about BERTMap: A BERT−based Ontology Alignment System | BibTeX data for BERTMap: A BERT−based Ontology Alignment System | Download (pdf) of BERTMap: A BERT−based Ontology Alignment System | DOI (https://doi.org/10.1609/aaai.v36i5.20510)
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Machine Learning−Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching
Yuan He‚ Jiaoyan Chen‚ Hang Dong‚ Ernesto Jiménez−Ruiz‚ Ali Hadian and Ian Horrocks
In The Semantic Web–ISWC 2022: 21st International Semantic Web Conference‚ Virtual Event‚ October 23–27‚ 2022‚ Proceedings. Pages 575–591. 2022.
Best Resource Paper Candidate
Details about Machine Learning−Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching | BibTeX data for Machine Learning−Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching | Download (pdf) of Machine Learning−Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching | DOI (https://doi.org/10.1007/978-3-031-19433-7_33)