Vectorland: Brief Notes from Using Text Embeddings for Search > the elegance is in the learning model, but the magic is in the structure of the information we model
> The source-taret training pairs dictate **what notion of "relatedness"** will be modeled in the embedding space
> is eminem more similar to rihanna or rap?
Modeling Uncertainty in Semantic Web Taxonomies Information retrieval systems have to deal with uncertain knowledge and query results should reflect this uncertainty in some manner. We present a new probabilistic method to approach the problem. In our method, degrees of subsumption, i.e., overlap between concepts can be modeled and computed efficiently using Bayesian networks based on RDF(S) ontologies.