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The "basic level", according to experiments in cognitive psychology, is thelevel of abstraction in a hierarchy of concepts at which humans perform tasksquicker and with greater accuracy than at other levels. We argue thatapplications that use concept hierarchies - such as knowledge graphs,ontologies or taxonomies - could significantly improve their user interfaces ifthey `knew' which concepts are the basic level concepts. This paper examines towhat extent the basic level can be learned from data. We test the utility ofthree types of concept features, that were inspired by the basic level theory:lexical features, structural features and frequency features. We evaluate ourapproach on WordNet, and create a training set of manually labelled examplesthat includes concepts from different domains. Our findings include that thebasic level concepts can be accurately identified within one domain. Conceptsthat are difficult to label for humans are also harder to classifyautomatically. Our experiments provide insight into how classificationperformance across domains could be improved, which is necessary foridentification of basic level concepts on a larger scale.

Is it a Fruit, an Apple or a Granny Smith? Predicting the Basic Level in a Concept Hierarchy