Empirical Analysis of Foundational Distinctions in Linked Open Data

TL;DR

This paper investigates whether machines can learn foundational ontological distinctions in Linked Open Data, such as class versus instance, using machine learning and crowdsourcing, addressing a gap between formal ontologies and real-world data.

Contribution

It presents empirical experiments demonstrating that machine learning and crowdsourcing can effectively identify foundational distinctions in Linked Open Data.

Findings

Promising results in classifying entities as classes or instances

Effective use of crowdsourcing to validate ontological distinctions

Machine learning models can learn common sense distinctions from data

Abstract

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.