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.

A proposed standard for specifying and exchanging ontologies, drawing together ideas from Web languages (e.g., XML, RDF), Description Logics, and frame-based systems. OKBC - The Open Knowledge Base Connectivity Protocol (formerly called GFP, the Generic Frame Protocol). OK Station (in French) - The Ontological Knowledge Station, a commercial modeling tool dedicated to the acquisition, definition and manipulation of knowledge bases and ontologies, based on the OK ontological model (Univ Savoie, France). OMCSNet - A semantic network derived from the OpenMind commonsense database, now superceded by the more recent OpenMind derivative knowledge base ConceptNet. ON9 - The ontology library at the Laboratory for Applied Ontology (Italy), developed using the ONIONS methodology and implemented in Loom.