To understand semantic connections between words in human language (e.g. what do apples have in common with oranges?), AI researchers have explored a wide range of strategies during the previous decades. Ontologies such as WordNet were constructed to represent knowledge about the meaning of words. In a broader sense these ontologies aim at representing the structure of the world itself. Such databases have supported classic AI interfaces like question ↔ answer systems. ConceptNet is a member of the family of semantic databases but it takes a more ‘fuzzy’ approach. It does not (necessarily) attempt to paint a true and scientifically valid ...