Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction
https://2024.eswc-conferences.org/wp-content/uploads/2024/04/146640020.pdf
#knowledgeGraph #syntheticData #neuroSymbolicAI #ArtificialIntelligence #semanticWeb #linkPrediction #graphEmbedding
PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips https://2024.eswc-conferences.org/wp-content/uploads/2024/04/146640336-1.pdf
#knowledgeGraph #syntheticData #neuroSymbolicAI #ArtificialIntelligence #semanticWeb #linkPrediction #graphEmbedding
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
Thrilled to share two articles I co-authored for the 1st issue of Transactions on Graph Data and Knowledge (@tgdkjournal):
- #MachineLearning and #KnowledgeGraphs: Existing Gaps and Future Research Challenges (https://doi.org/10.4230/TGDK.1.1.8)
- #KnowledgeGraphs for the #LifeSciences: Recent Developments, Challenges and Opportunities (https://doi.org/10.4230/TGDK.1.1.5)
It was a fantastic experience to collaborate on these two vision papers!
#semanticWeb #linkedOpenData #explainableAI #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
