Titlebook: Knowledge Engineering and Knowledge Management; 21st International C Catherine Faron Zucker,Chiara Ghidini,Yannick Tous Conference proceedi

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Support and Centrality: Learning Weights for Knowledge Graph Embedding Modelsstructural information. The learned embeddings can be used in multiple downstream tasks such as question answering, information extraction, query expansion, semantic similarity, and information retrieval. Over the past years, multiple embedding techniques have been proposed based on different underl

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OmniScience and Extensions – Lessons Learned from Designing a Multi-domain, Multi-use Case Knowledgeto date, comprehensive and to the point data. One of the key aspects of that offer is to have a global Knowledge Organization System (KOS) overarching scientific branches but also going deep enough into each domain to provide rich annotation or classification capacities. Knowing that the endeavor of

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Automating Class/Instance Representational Choices in Knowledge Basese form of corresponding textual corpora such as Wikipedia articles. The approach, based on machine classification of the text, avoids the need for feature engineering and provides valuable guidance when building or refining large knowledge bases. The approach works well over different domains and ou

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Maxime Clement,Ryutaro Ichise019 in honour of Sergio Albeverio. It presents the new area of studies concerning invariance and symmetry properties of finite and infinite dimensional stochastic differential equations.This area constitutes a natural, much needed, extension of the theory of classical ordinary and partial differenti