Empirical Analysis of Foundational Distinctions in the Web of Data
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A main difference between pre-Web artificial intelligence and the current one is that the Web and its Semantic extension (i.e. Web of Data) contain open global-scale knowledge and make it available to potentially intelligent machines that may want to benefit from it. Nevertheless, most of the Web of Data lacks ontological distinctions and has a sparse distribution of axiomatisations. For example, foundational 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). There is a gap between these ontologies, that often formalise or are inspired by preexisting philosophical theories and are developed with a top-down approach, and the Web of Data that is mostly derived from existing databases or from crowd-based effort (e.g. DBpedia, Wikidata, Freebase). We investigate whether the Web provides an empirical foundation for characterising entities of the Web of Data according to foundational distinctions. We want to answer questions such as "is the DBpedia entity for dog a class or an instance?" We report on a set of experiments based on machine learning and crowdsourcing that show promising results.
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