LifeNet: A Propositional Model of Ordinary Human Activity

LifeNet, a new common sense knowledge base that captures a first-person model of human experience in terms of a propositional representation, and a knowl- edge acquisition system that lets people interact with LifeNet to extend it further.

Abstract

LifeNet, a new common sense knowledge base that captures a first-person model of human experience in terms of a propositional representation. LifeNet represents knowledge as an undirected graphical model relating 80,000 egocentric propositions with 415,000 temporal and atemporal links between these propositions. We explain how we built LifeNet by extracting its propositions and links from the Open Mind Common Sense corpus of com- mon sense assertions, present a method for reasoning with the resulting knowledge base, evaluate the knowledge in LifeNet and the quality of inference, and describe a knowl- edge acquisition system that lets people interact with LifeNet to extend it further. INTRODUCTION interested in building 'common sense' models of the structure and flow of human life. Today's computer systems lack such models—they know almost nothing about the kinds of activities people engage in, the actions we are capable of and their likely effects, the kinds of places we spend our time and the things that are found there, the types of events we enjoy and types we loathe, and so forth. By finding ways to give computers the ability to represent and reason about ordinary life, we believe they can be made more helpful participants in the human world. An adequate common sense model should include knowl- edge about a wide range of objects, states, events, and situations. For example, a common sense model of human life should enable the following kinds of predictions: