Semantic Model

Semantic modeling aims to represent and reason with the meaning of data, enabling more intelligent and efficient systems. Current research focuses on developing robust semantic models for diverse applications, including improving the interpretability and maintainability of deep neural networks, enhancing knowledge graph embeddings for improved link prediction, and facilitating semantic interoperability across heterogeneous data sources like those found in building energy management and biomedical data. These advancements are driving progress in various fields, from improving search engine relevance and recommender systems to automating tasks in industrial settings and advancing natural language understanding.