122 citations · 145 across the 3 of their papers we have counts for
5 papers
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…
Post-hoc explanation of black-box classifiers using confident itemsets
Milad Moradi, Matthias Samwald
Black-box Artificial Intelligence (AI) methods, e.g. deep neural networks, have been widely utilized to build predictive models that can extract complex relationships in a dataset…
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…
Clustering of Deep Contextualized Representations for Summarization of Biomedical Texts
Milad Moradi, Matthias Samwald
In recent years, summarizers that incorporate domain knowledge into the process of text summarization have outperformed generic methods, especially for summarization of biomedical…
Applying deep learning techniques on medical corpora from the World Wide Web: a prototypical system and evaluation
Jose Antonio Miñarro-Giménez, Oscar Marín-Alonso, Matthias Samwald
BACKGROUND: The amount of biomedical literature is rapidly growing and it is becoming increasingly difficult to keep manually curated knowledge bases and ontologies up-to-date. In…