  The Web and its Semantic extension (i.e. Linked Open Data) contain open
global-scale knowledge and make it available to potentially intelligent
machines that want to benefit from it. Nevertheless, most of Linked Open Data
lack ontological distinctions and have sparse axiomatisation. For example,
distinctions such as whether an entity is inherently a class or an individual,
or whether it is a physical object or not, are hardly expressed in the data,
although they have been largely studied and formalised by foundational
ontologies (e.g. DOLCE, SUMO). These distinctions belong to common sense too,
which is relevant for many artificial intelligence tasks such as natural
language understanding, scene recognition, and the like. There is a gap between
foundational ontologies, that often formalise or are inspired by pre-existing
philosophical theories and are developed with a top-down approach, and Linked
Open Data that mostly derive from existing databases or crowd-based effort
(e.g. DBpedia, Wikidata). We investigate whether machines can learn
foundational distinctions over Linked Open Data entities, and if they match
common sense. We want to answer questions such as "does the DBpedia entity for
dog refer to a class or to an instance?". We report on a set of experiments
based on machine learning and crowdsourcing that show promising results.
