We have developed an approach for commonsense reasoning using knowledge collected from volunteers over the web. This knowledge is collected in natural language, and includes information such as task instructions, locations of objects in homes, causes and effects, and uses of objects in the home. This knowledge stored in tables in a relational database is filtered using statistical methods and rule-based inference. Missing details within natural language task instructions are reasoned to determine the steps to be executed in the task. These missing details are handled by meta-rules which work across the knowledge categories and interact with the appropriate tables to extract the right information. Our reasoning approach is illustrated for common household tasks.