601 citations · 602 across the 2 of their papers we have counts for
6 papers
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…
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…