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Commonsense knowledge acquisition and reasoning have long been a coreartificial intelligence problem. However, in the past, there has been a lack ofscalable methods to collect commonsense knowledge. In this paper, we propose todevelop principles for collecting commonsense knowledge based on selectionalpreference. We generalize the definition of selectional preference from one-hoplinguistic syntactic relations to higher-order relations over linguisticgraphs. Unlike previous commonsense knowledge definition (e.g., ConceptNet),selectional preference (SP) knowledge only relies on statistical distributionover linguistic graphs, which can be efficiently and accurately acquired fromthe unlabeled corpus with modern tools. Following this principle, we develop alarge-scale eventuality (a linguistic term covering activity, state, andevent)-based knowledge graph ASER, where each eventuality is represented as adependency graph, and the relation between them is a discourse relation definedin shallow discourse parsing. The higher-order selectional preference overcollected linguistic graphs reflects various kinds of commonsense knowledge.Moreover, motivated by the observation that humans understand events byabstracting the observed events to a higher level and can thus transfer theirknowledge to new events, we propose a conceptualization module to significantlyboost the coverage of ASER. In total, ASER contains 648 million edges between438 million eventualities. After conceptualization with Probase, a selectionalpreference based concept-instance relational knowledge base, our concept graphcontains 15 million conceptualized eventualities and 224 million edges betweenthem. Detailed analysis is provided to demonstrate its quality. All thecollected data, APIs, and tools are available athttps://github.com/HKUST-KnowComp/ASER.

ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities