Commonsense Knowledge Acquisition and Applications

The goal of this dissertation is to investigate automated methods for acquisition of large-scale, semantically organized commonsense knowledge. Prior state-of-the-art methods to acquire commonsense are either not automated or based on shallow representations. Thus, they cannot produce large-scale, semantically organized commonsense knowledge.
To achieve the goal, we divide the problem space into three research directions, constituting our core contributions:
1. Properties of objects: acquisition of properties like has Size, has Shape, etc. We develop WebChild, a semi-supervised method to compile semantically organized properties.
2. Relationships between objects: acquisition of relations like largerThan, partOf, memberOf, etc. We develop CMPKB, a linear-programming based method to compile comparative relations, and, we develop PWKB, a method based on statistical and logical inference to compile part-whole relations.
3. Interactions between objects: acquisition of activities like drive a car, park a car, etc., with attributes such as temporal or spatial attributes. We develop Knowlywood, a method based on semantic parsing and probabilistic graphical models to compile activity knowledge.
Together, these methods result in the construction of a large, clean and semantically organized Commonsense Knowledge Base that we call WebChild KB.