From Language towards Formal Spatial Calculi

We consider mapping unrestricted natural language to formal spatial representations. We describe ongoing work on a two-level machine learning approach. The first level is linguistic, and deals with the extraction of spatial information from natural language sentences, and is called spatial role labeling. The second level is ontological in nature, and deals with mapping this linguistic, spatial information to formal spatial calculi. Our main obstacles are the lack of available annotated data for training machine learning algorithms for these tasks, and the difficulty of selecting an appropriate abstraction level for the spatial information. For the linguistic part, we approach the problem in a gradual way. We make use of existing resources such as The Preposition Project (TPP) and the validation data of General Upper Model (GUM) ontology, and we show some computational results. For the ontological part, we describe machine learning challenges and discuss our proposed approach.