122 citations · 218 across the 11 of their papers we have counts for
6 papers · 1 filter
Explaining Black-box Models for Biomedical Text Classification
Milad Moradi, Matthias Samwald
In this paper, we propose a novel method named Biomedical Confident Itemsets Explanation (BioCIE), aiming at post-hoc explanation of black-box machine learning models for biomedica…
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…
Explaining black-box text classifiers for disease-treatment information extraction
Milad Moradi, Matthias Samwald
Deep neural networks and other intricate Artificial Intelligence (AI) models have reached high levels of accuracy on many biomedical natural language processing tasks. However, the…
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…