LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs.
Olivier CuréHubert NaackeTendry RandriamalalaBernd AmannPublished in: CoRR (2015)
Keyphrases
- encoding scheme
- efficient inference
- rdf graphs
- probabilistic inference
- fully connected
- conditional random fields
- markov random field
- hidden variables
- human pose estimation
- graph structure
- genetic algorithm
- approximate inference
- machine learning
- exact inference
- data sets
- semantic web
- graphical models
- probabilistic model