  Commonsense knowledge acquisition and reasoning have long been a core
artificial intelligence problem. However, in the past, there has been a lack of
scalable methods to collect commonsense knowledge. In this paper, we propose to
develop principles for collecting commonsense knowledge based on selectional
preference. We generalize the definition of selectional preference from one-hop
linguistic syntactic relations to higher-order relations over linguistic
graphs. Unlike previous commonsense knowledge definition (e.g., ConceptNet),
the selectional preference (SP) knowledge only relies on statistical
distribution over linguistic graphs, which can be efficiently and accurately
acquired from the unlabeled corpus with modern tools. Following this principle,
we develop a large-scale eventuality (a linguistic term covering activity,
state, and event)-based knowledge graph ASER, where each eventuality is
represented as a dependency graph, and the relation between them is a discourse
relation defined in shallow discourse parsing. The higher-order selectional
preference over collected linguistic graphs reflects various kinds of
commonsense knowledge. Moreover, motivated by the observation that humans
understand events by abstracting the observed events to a higher level and can
thus transferring their knowledge to new events, we propose a conceptualization
module to significantly boost the coverage of ASER. In total, ASER contains 438
million eventualities and 648 million edges between eventualities. After
conceptualization with Probase, a selectional preference based concept-instance
relational knowledge base, our concept graph contains 15 million conceptualized
eventualities and 224 million edges between them. Detailed analysis is provided
to demonstrate its quality. All the collected data, APIs, and tools are
available at https://github.com/HKUST-KnowComp/ASER.
