2018/03/26 by Luigi Asprino, Valerio Basile, Asprino, Luigi +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Artificial intelligence #Biomedical Text Mining and Ontologies #Class (philosophy) #Computation and Language (cs.CL) #Computer science #Crowdsourcing #Data science #Epistemology #FOS: Computer and information sciences #Information retrieval #Linked data #Natural language processing #Object (grammar) #Ontology #Open data #Programming language #Semantic Web #Semantic Web and Ontologies #Set (abstract data type) #Topic Modeling #World Wide Web #cs.AI #cs.CL
paper · pdf · doi:10.48550/arxiv.1803.09840
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2018/03/26 · arxiv created 2018/05/23 · arxiv updated 2018/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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.