Drawing inferences between open-domain natural language predicates is a necessity for true language understanding.
We present a multi-level geocoding model (MLG) that learns to associate texts to geographic locations.
Entity extraction and relation extraction are two indispensable building blocks for knowledge graph construction.
The new entailment score outperforms prior state-of-the-art results on a standard entialment dataset and the new link prediction scores show improvements over the raw link prediction scores.
We instead propose a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph.