Publications

A homotopy-type-theoretic generalization of neurosymbolic inference

Preprint

Fernando Zhapa-Camacho, Robert Hoehndorf

@misc{zhapacamacho2026hottnesy,
      title={A homotopy-type-theoretic generalization of neurosymbolic inference},
      author={Fernando Zhapa-Camacho and Robert Hoehndorf},
      year={2026},
      eprint={2606.17851},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

ArXiv GitHub

INDIGENA: inductive prediction of disease-gene associations using phenotype ontologies

Bioinformatics

Fernando Zhapa-Camacho, Robert Hoehndorf

@article{Zhapa_Camacho_2026_indigena,
  title={INDIGENA: inductive prediction of disease–gene associations using phenotype ontologies},
  volume={42},
  ISSN={1367-4811},
  url={http://dx.doi.org/10.1093/bioinformatics/btag325},
  DOI={10.1093/bioinformatics/btag325},
  number={6},
  journal={Bioinformatics},
  publisher={Oxford University Press (OUP)},
  author={Zhapa-Camacho, Fernando and Hoehndorf, Robert},
  editor={Robinson, Peter},
  year={2026},
  month=May
}

Paper GitHub

Fully Geometric Multi-Hop Reasoning on Knowledge Graphs with Transitive Relations

ESWC 2026

Fernando Zhapa-Camacho, Robert Hoehndorf

@inbook{Zhapa_Camacho_2026_geometre,
  title={Fully Geometric Multi-hop Reasoning on Knowledge Graphs with Transitive Relations},
  ISBN={9783032251565},
  ISSN={1611-3349},
  url={http://dx.doi.org/10.1007/978-3-032-25156-5_14},
  DOI={10.1007/978-3-032-25156-5_14},
  booktitle={The Semantic Web},
  publisher={Springer Nature Switzerland},
  author={Zhapa-Camacho, Fernando and Hoehndorf, Robert},
  year={2026},
  pages={258–277}
}

ArXiv GitHub

LLM Agent Based Protein Function Prediction

PSB 2026

Fernando Zhapa-Camacho, Olga Mashkova, Robert Hoehndorf, Maxat Kulmanov

@inproceedings{Zhapa_Camacho_2025,
title={LLM Agent Based Protein Function Prediction},
url={http://dx.doi.org/10.1142/9789819824755_0036},
DOI={10.1142/9789819824755_0036},
booktitle={Biocomputing 2026},
publisher={WORLD SCIENTIFIC},
author={Zhapa-Camacho,
Fernando and Mashkova, Olga and Hoehndorf, Robert and Kulmanov, Maxat},
year={2025},
month=dec,
pages={508–519} }

Paper GitHub

Lattice-Based ALC Ontology Embeddings With Saturation (Extended Version)

Neurosymbolic Artificial Intelligence

Fernando Zhapa-Camacho, Robert Hoehndorf

@article{Zhapa_Camacho_2025,
title={Lattice-Based ALC Ontology Embeddings With Saturation},
volume={1},
ISSN={2949-8732},
url={http://dx.doi.org/10.1177/29498732251340186},
DOI={10.1177/29498732251340186},
journal={Neurosymbolic Artificial Intelligence},
publisher={SAGE Publications},
author={Zhapa-Camacho, Fernando and Hoehndorf, Robert},
year={2025},
month=jun }

Paper GitHub

Ontology Embedding: A Survey of Methods, Applications and Resources

IEEE TKDE

Jiaoyan Chen, Olga Mashkova, Fernando Zhapa-Camacho, Robert Hoehndorf, Yuan He, Ian Horrocks

@article{Chen_2025,
  title={Ontology Embedding: A Survey of Methods, Applications and Resources},
  ISSN={2326-3865},
  url={http://dx.doi.org/10.1109/TKDE.2025.3559023},
  DOI={10.1109/tkde.2025.3559023},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  publisher={Institute of Electrical and Electronics Engineers (IEEE)},
  author={Chen, Jiaoyan and Mashkova, Olga and Zhapa-Camacho, Fernando and Hoehndorf, Robert and He, Yuan and Horrocks, Ian},
  year={2025},
  pages={1–20}
}

Paper

Neuro-Symbolic AI in Life Sciences

Frontiers in Artificial Intelligence and Applications

Robert Hoehndorf, Catia Pesquita, Fernando Zhapa-Camacho

@inbook{Hoehndorf_2025,
title={Neuro-Symbolic AI in Life Sciences},
ISBN={9781643685793},
ISSN={1879-8314},
url={http://dx.doi.org/10.3233/FAIA250239},
DOI={10.3233/faia250239},
booktitle={Handbook on Neurosymbolic AI and Knowledge Graphs},
publisher={IOS Press},
author={Hoehndorf,
Robert and Pesquita, Catia and Zhapa-Camacho, Fernando},
year={2025}, month=mar }

Paper

Lattice-preserving ALC ontology embeddings

NeSy 2024

Fernando Zhapa-Camacho, Robert Hoehndorf

@InProceedings{10.1007/978-3-031-71167-1_19,
author="Zhapa-Camacho, Fernando
and Hoehndorf, Robert",
editor="Besold, Tarek R.
and d'Avila Garcez, Artur
and Jimenez-Ruiz, Ernesto
and Confalonieri, Roberto
and Madhyastha, Pranava
and Wagner, Benedikt",
title="Lattice-Preserving {\$}{\$}{\backslash}mathcal {\{}ALC{\}}{\$}{\$}Ontology Embeddings",
booktitle="Neural-Symbolic Learning and Reasoning",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="355--369",
isbn="978-3-031-71167-1"
}

Paper GitHub

Predicting protein functions using positive-unlabeled ranking with ontology-based priors

ISMB 2024

Fernando Zhapa-Camacho, Zhenwei Tang, Maxat Kulmanov, Robert Hoehndorf

@article {Zhapa-Camacho2024.01.28.577662,
	  author = {Fernando Zhapa-Camacho and Zhenwei Tang and Maxat Kulmanov and Robert Hoehndorf},
          title = {Predicting protein functions using positive-unlabeled ranking with ontology-based priors},
          elocation-id = {2024.01.28.577662},
          year = {2024},
          doi = {10.1101/2024.01.28.577662},
          publisher = {Cold Spring Harbor Laboratory},
          abstract = {Automated protein function prediction is a crucial and widely studied problem in bioinformatics. Computationally, protein function is a multilabel classification problem where only positive samples are defined and there is a large number of unlabeled annotations. Most existing methods rely on the assumption that the unlabeled set of protein function annotations are negatives, inducing the false negative issue, where potential positive samples are trained as negatives. We introduce a novel approach named PU-GO, wherein we address function prediction as a positive-unlabeled ranking problem. We apply empirical risk minimization, i.e., we minimize the classification risk of a classifier where class priors are obtained from the Gene Ontology hierarchical structure. We show that our approach is more robust than other state-of-the-art methods on similarity-based and time-based benchmark datasets. Data and code are available at https://github.com/bio-ontology-research-group/PU-GO.Competing Interest StatementThe authors have declared no competing interest.},
        URL = {https://www.biorxiv.org/content/early/2024/01/31/2024.01.28.577662},
        eprint = {https://www.biorxiv.org/content/early/2024/01/31/2024.01.28.577662.full.pdf},
        journal = {bioRxiv}
}

Paper GitHub

From axioms over graphs to vectors, and back again: evaluating the properties of graph-based ontology embeddings

NeSy 2023

Fernando Zhapa-Camacho, Robert Hoehndorf

@inproceedings{zhapa2023axioms,
  author       = {Fernando Zhapa{-}Camacho and
                  Robert Hoehndorf},
  editor       = {Artur S. d'Avila Garcez and
                  Tarek R. Besold and
                  Marco Gori and
                  Ernesto Jim{\'{e}}nez{-}Ruiz},
  title        = {From Axioms over Graphs to Vectors, and Back Again: Evaluating the
                  Properties of Graph-based Ontology Embeddings},
  booktitle    = {Proceedings of the 17th International Workshop on Neural-Symbolic
                  Learning and Reasoning, La Certosa di Pontignano, Siena, Italy, July
                  3-5, 2023},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3432},
  pages        = {85--102},
  publisher    = {CEUR-WS.org},
  year         = {2023},
  url          = {https://ceur-ws.org/Vol-3432/paper7.pdf},
  timestamp    = {Tue, 11 Jul 2023 17:14:10 +0200},
  biburl       = {https://dblp.org/rec/conf/nesy/Zhapa-CamachoH23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Paper GitHub

mOWL: Python library for machine learning with biomedical ontologies

Bioinformatics, Volume 39, Issue 1

Fernando Zhapa-Camacho, Maxat Kulmanov, Robert Hoehndorf

@article{10.1093/bioinformatics/btac811,
	author = {Zhapa-Camacho, Fernando and Kulmanov, Maxat and Hoehndorf, Robert},
    	title = "{mOWL: Python library for machine learning with biomedical ontologies}",
    	journal = {Bioinformatics},
    	year = {2022},
    	month = {12},
    	issn = {1367-4803},
    	doi = {10.1093/bioinformatics/btac811},
    	url = {https://doi.org/10.1093/bioinformatics/btac811},
    	note = {btac811},
   	}

Paper GitHub

DELE: Deductive EL++ Embeddings for Knowledge Base Completion

Preprint

Olga Mashkova , Fernando Zhapa-Camacho, Robert Hoehndorf

@misc{mashkova2024deledeductivemathcalelthinspace,
      title={DELE: Deductive $\mathcal{EL}^{++} \thinspace $ Embeddings for Knowledge Base Completion}, 
      author={Olga Mashkova and Fernando Zhapa-Camacho and Robert Hoehndorf},
      year={2024},
      eprint={2411.01574},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2411.01574}, 
}

ArXiv GitHub

Enhancing Geometric Ontology Embeddings for EL++ with Negative Sampling and Deductive Closure Filtering

NeSy 2024

Olga Mashkova, Fernando Zhapa-Camacho, Robert Hoehndorf

@inbook{Mashkova_2024,
  title={Enhancing Geometric Ontology Embeddings for $$\mathcal{E}\mathcal{L}^{++}$$ with Negative Sampling and Deductive Closure Filtering},
  ISBN={9783031711671},
  ISSN={1611-3349},
  url={http://dx.doi.org/10.1007/978-3-031-71167-1_18},
  DOI={10.1007/978-3-031-71167-1_18},
  booktitle={Neural-Symbolic Learning and Reasoning},
  publisher={Springer Nature Switzerland},
  author={Mashkova, Olga and Zhapa-Camacho, Fernando and Hoehndorf, Robert},
  year={2024},
  pages={331–354}
}

Paper GitHub

Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning

Bioinformatics, Volume 40, Issue 5

Azza Althagafi, Fernando Zhapa-Camacho, Robert Hoehndorf

@article {Althagafi2023.11.08.566179,
        author = {Azza Althagafi and Fernando Zhapa-Camacho and Robert Hoehndorf},
        title = {Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning},
        elocation-id = {2023.11.08.566179},
        year = {2023},
        doi = {10.1101/2023.11.08.566179},
        publisher = {Cold Spring Harbor Laboratory},
        URL = {https://www.biorxiv.org/content/early/2023/11/13/2023.11.08.566179},
        eprint = {https://www.biorxiv.org/content/early/2023/11/13/2023.11.08.566179.full.pdf},
        journal = {bioRxiv}
}

Paper GitHub

Evaluating Different Methods for Semantic Reasoning Over Ontologies

SemREC 2023

Fernando Zhapa-Camacho, Robert Hoehndorf

@inproceedings{DBLP:conf/semweb/Zhapa-CamachoH23,
  author       = {Fernando Zhapa{-}Camacho and
                  Robert Hoehndorf},
  editor       = {Debayan Banerjee and
                  Ricardo Usbeck and
                  Nandana Mihindukulasooriya and
                  Gunjan Singh and
                  Raghava Mutharaju and
                  Pavan Kapanipathi},
  title        = {Evaluating Different Methods for Semantic Reasoning Over Ontologies},
  booktitle    = {Joint Proceedings of Scholarly {QALD} 2023 and SemREC 2023 co-located
                  with 22nd International Semantic Web Conference {ISWC} 2023, Athens,
                  Greece, November 6-10, 2023},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3592},
  publisher    = {CEUR-WS.org},
  year         = {2023},
  url          = {https://ceur-ws.org/Vol-3592/paper9.pdf},
  timestamp    = {Tue, 02 Jan 2024 17:44:44 +0100},
  biburl       = {https://dblp.org/rec/conf/semweb/Zhapa-CamachoH23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Paper GitHub

A-LIOn - Alignment Learning through Inconsistency negatives of the aligned Ontologies

The 17th International Workshop on Ontology Matching

Sarah M. Alghamdi, Fernando Zhapa-Camacho, Robert Hoehndorf

@inproceedings{DBLP:conf/semweb/AlghamdiZH22,
  author       = {Sarah M. Alghamdi and
                  Fernando Zhapa{-}Camacho and
                  Robert Hoehndorf},
  editor       = {Pavel Shvaiko and
                  J{\'{e}}r{\^{o}}me Euzenat and
                  Ernesto Jim{\'{e}}nez{-}Ruiz and
                  Oktie Hassanzadeh and
                  C{\'{a}}ssia Trojahn},
  title        = {A-LIOn - alignment learning through inconsistency negatives of the
                  aligned ontologies},
  booktitle    = {Proceedings of the 17th International Workshop on Ontology Matching
                  {(OM} 2022) co-located with the 21th International Semantic Web Conference
                  {(ISWC} 2022), Hangzhou, China, held as a virtual conference, October
                  23, 2022},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3324},
  pages        = {137--144},
  publisher    = {CEUR-WS.org},
  year         = {2022},
  url          = {https://ceur-ws.org/Vol-3324/oaei22\_paper2.pdf},
  timestamp    = {Fri, 10 Mar 2023 16:23:09 +0100},
  biburl       = {https://dblp.org/rec/conf/semweb/AlghamdiZH22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Paper GitHub

DeepGOWeb: fast and accurate protein function prediction on the (Semantic) Web

Nucleic Acids Research

Maxat Kulmanov, Fernando Zhapa-Camacho, Robert Hoehndorf

@article{10.1093/nar/gkab373,
    author = {Kulmanov, Maxat and Zhapa-Camacho, Fernando and Hoehndorf, Robert},
    title = "{DeepGOWeb: fast and accurate protein function prediction on the (Semantic) Web}",
    journal = {Nucleic Acids Research},
    volume = {49},
    number = {W1},
    pages = {W140-W146},
    year = {2021},
    month = {05},
    issn = {0305-1048},
    doi = {10.1093/nar/gkab373},
    url = {https://doi.org/10.1093/nar/gkab373},
    eprint = {https://academic.oup.com/nar/article-pdf/49/W1/W140/38841723/gkab373.pdf},
}

Paper

Successive Adaptive Linear Neural Modeling for Equidistant Real Roots Finding

ETCM 2018

Joseph R. González, Fernando Zhapa-Camacho, Oscar V. Guarnizo, Francisco Ortega-Zamorano

@INPROCEEDINGS{8580280,
  author={González, Joseph R. and Zhapa, Fernando P. and Guarnizo, Oscar V. and Ortega-Zamorano, Francisco},
  booktitle={2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)}, 
  title={Successive Adaptive Linear Neural Modeling for Equidistant Real Roots Finding}, 
  year={2018},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/ETCM.2018.8580280}}

Paper

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