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Article
Open AccessMulti-domain knowledge graph embeddings for gene-disease association prediction
Predicting gene-disease associations typically requires exploring diverse sources of information as well as sophisticated computational approaches. Knowledge graph embeddings can help tackle these challenges b...
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Chapter and Conference Paper
Biomedical Knowledge Graph Embeddings with Negative Statements
A knowledge graph is a powerful representation of real-world entities and their relations. The vast majority of these relations are defined as positive statements, but the importance of negative statements is ...
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Chapter and Conference Paper
The Supervised Semantic Similarity Toolkit
Knowledge graph-based semantic similarity measures have been used in several applications. Although knowledge graphs typically describe entities according to different semantic aspects modeled in ontologies, s...
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Article
Open AccessEvolving knowledge graph similarity for supervised learning in complex biomedical domains
In recent years, biomedical ontologies have become important for describing existing biological knowledge in the form of knowledge graphs. Data mining approaches that work with knowledge graphs have been propo...
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Chapter and Conference Paper
Evolving Meaning for Supervised Learning in Complex Biomedical Domains Using Knowledge Graphs
Knowledge graphs represent an unparalleled opportunity for machine learning, given their ability to provide meaningful context to data through semantic representations. Knowledge graphs provide multiple perspe...
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Chapter and Conference Paper
A Collection of Benchmark Data Sets for Knowledge Graph-Based Similarity in the Biomedical Domain
The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importa...
