Etna: Harvesting Action Graphs from Websites - Riva, Oriana; Kace, Jason (34th ACM Symposium on User Interface Software and Technology (UIST '21), 2021)
Knowledge bases, such as Google knowledge graph, contain millions of entities (people, places, etc.) and billions of facts about them. While much is known about entities, little is known about the actions these entities relate to. On the other hand, the Web has lots of information about human tasks. A website for restaurant reservations, for example, implicitly knows about various restaurant-related actions (making reservations, delivering food, etc.), the inputs these actions require and their expected output; it can also be automated to execute those actions. To harvest action knowledge from websites, we propose Etna. Users demonstrate how to accomplish various tasks in a website, and Etna constructs an action-state model of the website visualized as an action graph. An action graph includes definitions of tasks and actions, knowledge about their start/end states and execution scripts for their automation. We report on our experience in building action-state models of many commercial websites and use cases that leveraged them.