  Understanding users' intentions in e-commerce platforms requires commonsense
knowledge. In this paper, we present FolkScope, an intention knowledge graph
construction framework to reveal the structure of humans' minds about
purchasing items. As commonsense knowledge is usually ineffable and not
expressed explicitly, it is challenging to perform information extraction.
Thus, we propose a new approach that leverages the generation power of large
language models~(LLMs) and human-in-the-loop annotation to semi-automatically
construct the knowledge graph. LLMs first generate intention assertions via
e-commerce-specific prompts to explain shopping behaviors, where the intention
can be an open reason or a predicate falling into one of 18 categories aligning
with ConceptNet, e.g., IsA, MadeOf, UsedFor, etc. Then we annotate plausibility
and typicality labels of sampled intentions as training data in order to
populate human judgments to all automatic generations. Last, to structurize the
assertions, we propose pattern mining and conceptualization to form more
condensed and abstract knowledge. Extensive evaluations and studies demonstrate
that our constructed knowledge graph can well model e-commerce knowledge and
have many potential applications.
