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Found 16 papers, 10 papers with code
Enhancing Geometric Ontology Embeddings for $\mathcal{EL}^{++}$ with Negative Sampling and Deductive Closure Filtering
Ontology embeddings map classes, relations, and individuals in ontologies into $\mathbb{R}^n$, and within $\mathbb{R}^n$ similarity between entities can be computed or new axioms inferred.
Stylized Projected GAN: A Novel Architecture for Fast and Realistic Image Generation
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 adversarial setting is a difficult task.
An Open-Source Knowledge Graph Ecosystem for the Life Sciences
Translational research requires data at multiple scales of biological organization.
Lattice-preserving $\mathcal{ALC}$ ontology embeddings
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 such as bioinformatics.
From axioms over graphs to vectors, and back again: evaluating the properties of graph-based ontology embeddings
Several approaches have been developed that generate embeddings for Description Logic ontologies and use these embeddings in machine learning.
LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactions
The LEP-AD model scales favorably in performance with the size of training data.
Ranked #1 on
Protein Language Model
on DAVIS-DTA
FALCON: Faithful Neural Semantic Entailment over ALC Ontologies
Many ontologies, i. e., Description Logic (DL) knowledge bases, have been developed to provide rich knowledge about various domains, and a lot of them are based on ALC, i. e., a prototypical and expressive DL, or its extensions.
TAR: Neural Logical Reasoning across TBox and ABox
Neural logical reasoning (NLR) is a fundamental task to explore such knowledge bases, which aims at answering multi-hop queries with logical operations based on distributed representations of queries and answers.
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
Most real-world knowledge graphs (KG) are far from complete and comprehensive.
Description Logic EL++ Embeddings with Intersectional Closure
Since the intersection of boxes remains as a box, the intersectional closure is satisfied.
Efficient long-distance relation extraction with DG-SpanBERT
In natural language processing, relation extraction seeks to rationally understand unstructured text.
Ranked #17 on
Relation Extraction
on TACRED
EL Embeddings: Geometric construction of models for the Description Logic EL ++
We address the problem of finding vector space embeddings for theories in the Description Logic $\mathcal{EL}^{++}$ that are also models of the TBox.
OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction
Second, we evaluate our method on predicting gene-disease associations based on phenotype similarity by generating vector representations of genes and diseases using a phenotype ontology, and applying the obtained vectors to predict gene-disease associations.
Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations
We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies.
DeepGO: Predicting protein functions from sequence and interactions using a deep ontology-aware classifier
The functions of proteins are classified using the Gene Ontology (GO), which contains over 40, 000 classes.
Neuro-symbolic representation learning on biological knowledge graphs
Motivation: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries.
