  Acquiring commonsense knowledge and reasoning is recognized as an important
frontier in achieving general Artificial Intelligence (AI). Recent research in
the Natural Language Processing (NLP) community has demonstrated significant
progress in this problem setting. Despite this progress, which is mainly on
multiple-choice question answering tasks in limited settings, there is still a
lack of understanding (especially at scale) of the nature of commonsense
knowledge itself. In this paper, we propose and conduct a systematic study to
enable a deeper understanding of commonsense knowledge by doing an empirical
and structural analysis of the ConceptNet knowledge base. ConceptNet is a
freely available knowledge base containing millions of commonsense assertions
presented in natural language. Detailed experimental results on three carefully
designed research questions, using state-of-the-art unsupervised graph
representation learning ('embedding') and clustering techniques, reveal deep
substructures in ConceptNet relations, allowing us to make data-driven and
computational claims about the meaning of phenomena such as 'context' that are
traditionally discussed only in qualitative terms. Furthermore, our methodology
provides a case study in how to use data-science and computational
methodologies for understanding the nature of an everyday (yet complex)
psychological phenomenon that is an essential feature of human intelligence.
