Very glad to announce that we got 2 best paper awards at #ESWC2024 for our works about PyGraft (resource track) and semantically enhanced loss functions to learn graph #embedding (research track)! Congratulations Nicolas Hubert!

#knowledgeGraph #syntheticData #neuroSymbolicAI #ArtificialIntelligence #semanticWeb #linkPrediction #graphEmbedding

Proud to announce our new paper, "Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning" with Lucas Jarnac and Miguel Couceiro, accepted in #cikm2023

#knowledgeGraph #machineLearning #graphEmbedding #transferLearning #zeroShot #openScience

Accepted extended abstracts in #WikiWorkshop2023: https://wikiworkshop.org/223/#papers

Looking forward to presenting our work addressing the cold start problem by bootstrapping an Enterprise #KnowledgeGraph from #Wikidata with thematic subgraph selection and pruning!
#graphEmbedding #machineLearning #semanticWeb #coldStart

https://www.sciencedirect.com/science/article/pii/S0933365723000374

"By transforming complex and multidimensional phenotypes from the Human Phenotype Ontology (HPO) format into vectors, our proposed method enables efficient representation of these phenotypes for downstream tasks that require deep phenotyping. This is demonstrated in a patient similarity analysis and can further be applied to disease trajectory and risk prediction."

#ontology #semantic #knowledgeBase #phenotype #graphEmbedding #representations #dataAnalysis