Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Agibetov, Asan"'
Publikováno v:
BMC Bioinformatics (2019)
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 literature. We trained different neural embedding models on 1.7 milli
Externí odkaz:
http://arxiv.org/abs/2110.15708
Neural embedding-based machine learning models have shown promise for predicting novel links in biomedical knowledge graphs. Unfortunately, their practical utility is diminished by their lack of interpretability. Recently, the fully interpretable, ru
Externí odkaz:
http://arxiv.org/abs/2012.05750
Autor:
Agibetov, Asan
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 design choice has been mostly popularized by the direct application of t
Externí odkaz:
http://arxiv.org/abs/2011.09907
Autor:
Agibetov, Asan, Samwald, Matthias
Recently, link prediction algorithms based on neural embeddings have gained tremendous popularity in the Semantic Web community, and are extensively used for knowledge graph completion. While algorithmic advances have strongly focused on efficient wa
Externí odkaz:
http://arxiv.org/abs/2005.07654
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-based modules to accurately divide an input ontology matching task in
Externí odkaz:
http://arxiv.org/abs/2003.05370
Publikováno v:
Bioinformatics, Volume 36, Issue 13, July 2020
SUMMARY: Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks. However, dedicated benchmarks for measuring algorithmic progress have not yet emerged. With OpenBioLink, we
Externí odkaz:
http://arxiv.org/abs/1912.04616
Autor:
Agibetov, Asan, Samwald, Matthias
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 comparing, combining and extending different methodologies for link predictio
Externí odkaz:
http://arxiv.org/abs/1807.10511
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 model and locality modules to effectively divide an input ontology ma
Externí odkaz:
http://arxiv.org/abs/1805.12402
Autor:
Agibetov, Asan, Samwald, Matthias
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 biological knowledge graph (Alshahrani, 2017) that was used for demonstr
Externí odkaz:
http://arxiv.org/abs/1804.11105