78 citations · 162 across the 9 of their papers we have counts for
9 papers
Neural sentence embedding models for semantic similarity estimation in the biomedical domain
Kathrin Blagec, Hong Xu, Asan Agibetov +1
BACKGROUND: In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical…
Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphs
Simon Ott, Laura Graf, Asan Agibetov +2
Neural embedding-based machine learning models have shown promise for predicting novel links in biomedical knowledge graphs. Unfortunately, their practical utility is diminished by…
Neural graph embeddings as explicit low-rank matrix factorization for link prediction
Asan Agibetov
Learning good quality neural graph embeddings has long been achieved by minimizing the point-wise mutual information (PMI) for co-occurring nodes in simulated random walks. This de…
Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes
Asan Agibetov, Matthias Samwald
Recently, link prediction algorithms based on neural embeddings have gained tremendous popularity in the Semantic Web community, and are extensively used for knowledge graph comple…
Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules
Ernesto Jiménez-Ruiz, Asan Agibetov, Jiaoyan Chen +2
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In this paper we present an approach that combines a neural embedding model and logic…
OpenBioLink: A benchmarking framework for large-scale biomedical link prediction
Anna Breit, Simon Ott, Asan Agibetov +1
SUMMARY: Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks. However, dedicated benchmarks for meas…
Global and local evaluation of link prediction tasks with neural embeddings
Asan Agibetov, Matthias Samwald
We focus our attention on the link prediction problem for knowledge graphs, which is treated herein as a binary classification task on neural embeddings of the entities. By compari…
Breaking-down the Ontology Alignment Task with a Lexical Index and Neural Embeddings
Ernesto Jimenez-Ruiz, Asan Agibetov, Matthias Samwald +1
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In the paper we present an approach that combines a lexical index, a neural embedding…
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge
Asan Agibetov, Matthias Samwald
In this work we address the problem of fast and scalable learning of neuro-symbolic representations for general biological knowledge. Based on a recently published comprehensive bi…