601 citations · 1.3k across the 28 of their papers we have counts for
29 papers
A homotopy-type-theoretic generalization of neurosymbolic inference
Fernando Zhapa-Camacho, Robert Hoehndorf
A wide range of neurosymbolic (NeSy) systems compute one functional: a belief-weighted sum of a logical quantity over a space of -structures, of which weighted model counting, f…
LAFA: A Framework for Reproducible Longitudinal Assessment of Protein Function Annotation Models
An Phan, Yanli Wang, Frimpong Boadu +3
Motivation: Protein function prediction is a challenging task and an open problem in computational biology. The Critical Assessment of protein Function Annotation (CAFA) is a trien…
SIDEKICK: A Semantically Integrated Resource for Drug Effects, Indications, and Contraindications
Mohammad Ashhad, Olga Mashkova, Ricardo Henao +1
Pharmacovigilance and clinical decision support systems utilize structured drug safety data to guide medical practice. However, existing datasets frequently depend on terminologies…
GRAFT: Decoupling Ranking and Calibration for Survival Analysis
Mohammad Ashhad, Robert Hoehndorf, Ricardo Henao
Survival analysis is complicated by censored data, high-dimensional features, and non-linear interactions. Classical models offer interpretability and superior calibration but are…
Causal knowledge graph analysis identifies adverse drug effects
Sumyyah Toonsi, Paul Schofield, Robert Hoehndorf
Knowledge graphs and structural causal models have each proven valuable for organizing biomedical knowledge and estimating causal effects, but remain largely disconnected: knowledg…
DELE: Deductive Embeddings for Knowledge Base Completion
Olga Mashkova, Fernando Zhapa-Camacho, Robert Hoehndorf
Ontology embeddings map classes, roles, and individuals in ontologies into , and within similarity between entities can be computed or new axioms infer…
Towards Automated Functional Equation Proving: A Benchmark Dataset and A Domain-Specific In-Context Agent
Mahdi Buali, Robert Hoehndorf
Automated Theorem Proving (ATP) faces challenges due to its complexity and computational demands. Recent work has explored using Large Language Models (LLMs) for ATP action selecti…
Enhancing Geometric Ontology Embeddings for with Negative Sampling and Deductive Closure Filtering
Olga Mashkova, Fernando Zhapa-Camacho, Robert Hoehndorf
Ontology embeddings map classes, relations, and individuals in ontologies into , and within similarity between entities can be computed or new axioms i…
Ontology Embedding: A Survey of Methods, Applications and Resources
Jiaoyan Chen, Olga Mashkova, Fernando Zhapa-Camacho +3
Ontologies are widely used for representing domain knowledge and meta data, playing an increasingly important role in Information Systems, the Semantic Web, Bioinformatics and many…
Stylized Projected GAN: A Novel Architecture for Fast and Realistic Image Generation
Md Nurul Muttakin, Malik Shahid Sultan, Robert Hoehndorf +1
Generative Adversarial Networks are used for generating the data using a generator and a discriminator, GANs usually produce high-quality images, but training GANs in an adversaria…
An Open-Source Knowledge Graph Ecosystem for the Life Sciences
Tiffany J. Callahan, Ignacio J. Tripodi, Adrianne L. Stefanski +29
Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these…
Lattice-preserving ontology embeddings with saturation
Fernando Zhapa-Camacho, Robert Hoehndorf
Generating vector representations (embeddings) of OWL ontologies is a growing task due to its applications in predicting missing facts and knowledge-enhanced learning in fields suc…
From axioms over graphs to vectors, and back again: evaluating the properties of graph-based ontology embeddings
Fernando Zhapa-Camacho, Robert Hoehndorf
Several approaches have been developed that generate embeddings for Description Logic ontologies and use these embeddings in machine learning. One approach of generating ontologies…
Semantic Units: Organizing knowledge graphs into semantically meaningful units of representation
Lars Vogt, Tobias Kuhn, Robert Hoehndorf
Knowledge graphs and ontologies are becoming increasingly important as technical solutions for Findable, Accessible, Interoperable, and Reusable data and metadata (FAIR Guiding Pri…
FALCON: Scalable Reasoning over Inconsistent ALC Ontologies
Tilman Hinnerichs, Zhenwei Tang, Xi Peng +2
Ontologies are one of the richest sources of knowledge. Real-world ontologies often contain thousands of axioms and are often human-made. Hence, they may contain inconsistency and…
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
Zhenwei Tang, Shichao Pei, Zhao Zhang +4
Most real-world knowledge graphs (KG) are far from complete and comprehensive. This problem has motivated efforts in predicting the most plausible missing facts to complete a given…
TAR: Neural Logical Reasoning across TBox and ABox
Zhenwei Tang, Shichao Pei, Xi Peng +3
Many ontologies, i.e., Description Logic (DL) knowledge bases, have been developed to provide rich knowledge about various domains. An ontology consists of an ABox, i.e., assertion…
Description Logic EL++ Embeddings with Intersectional Closure
Xi Peng, Zhenwei Tang, Maxat Kulmanov +2
Many ontologies, in particular in the biomedical domain, are based on the Description Logic EL++. Several efforts have been made to interpret and exploit EL++ ontologies by distrib…
Efficient long-distance relation extraction with DG-SpanBERT
Jun Chen, Robert Hoehndorf, Mohamed Elhoseiny +1
In natural language processing, relation extraction seeks to rationally understand unstructured text. Here, we propose a novel SpanBERT-based graph convolutional network (DG-SpanBE…
EL Embeddings: Geometric construction of models for the Description Logic EL ++
Maxat Kulmanov, Wang Liu-Wei, Yuan Yan +1
An embedding is a function that maps entities from one algebraic structure into another while preserving certain characteristics. Embeddings are being used successfully for mapping…
OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction
Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf
Motivation: Ontologies are widely used in biology for data annotation, integration, and analysis. In addition to formally structured axioms, ontologies contain meta-data in the for…
Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks
Sohaib Younis, Claus Weiland, Robert Hoehndorf +4
Herbaria worldwide are housing a treasure of 100s of millions of herbarium specimens, which are increasingly being digitized in recent years and thereby made more easily accessible…
Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations
Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf
We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wid…
DeepGO: Predicting protein functions from sequence and interactions using a deep ontology-aware classifier
Maxat Kulmanov, Mohammed Asif Khan, Robert Hoehndorf
A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of the…