ConceptNet: A Practical Commonsense Reasoning Toolkit

ConceptNet is a freely available commonsense knowledgebase and natural-language-processing toolkit which supports many practical textual-reasoning tasks over real-world documents including topic-jisting (e.g. a news article containing the concepts, “gun,” “convenience store,” “demand money” and “make getaway” might suggest the topics “robbery” and “crime”), affect-sensing (e.g. this email is sad and angry), analogy-making (e.g. “scissors,” “razor,” “nail clipper,” and “sword” are perhaps like a “knife” because they are all “sharp,” and can be used to “cut something”), and other contextoriented inferences. The knowledgebase is a semantic network presently consisting of over 1.6 million assertions of commonsense knowledge encompassing the spatial, physical, social, temporal, and psychological aspects of everyday life. Whereas similar large-scale semantic knowledgebases like Cyc and WordNet are carefully handcrafted, ConceptNet is generated automatically from the 700,000 sentences of the Open Mind Common Sense Project – a World Wide Web based collaboration with over 14,000 authors. ConceptNet is a unique resource in that it captures a wide range of commonsense concepts and relations, such as those found in the Cyc knowledgebase, yet this knowledge is structured not as a complex and intricate logical framework, but rather as a simple, easy-to-use semantic network, like WordNet. While ConceptNet still supports many of the same applications as WordNet, such as query expansion and determining semantic similarity, its focus on concepts-rather-than-words, its more diverse relational ontology, and its emphasis on informal conceptual-connectedness over formal linguistic-rigor allow it to go beyond WordNet to make practical, context-oriented, commonsense inferences over real-world texts. In this paper, we first give an overview of the role that commonsense knowledge plays in making sense of text, and we situate our commonsense toolkit, ConceptNet, in the literature of large-scale semantic knowledgebases; we then discuss how ConceptNet was built and how it is structured; third, we present the ConceptNet natural-language-processing engine and describe the various practical reasoning tasks that it supports; fourth, we delve into a more detailed quantitative and qualitative analysis of ConceptNet; fifth, we review the gamut of real-world applications which researchers have built using the ConceptNet toolkit; we conclude by reflecting back on the Big Picture. INTRODUCTION In today’s digital age, text is the primary medium of representing and transmitting information, as evidenced by the pervasiveness of emails, instant messages, documents, weblogs, news articles, homepages, and printed materials. Our lives are now saturated with textual information, and there is an increasing urgency to develop technology to help us manage and make sense of the resulting information overload. While keyword-based and statistical approaches have enjoyed some success in assisting information retrieval, data mining, and natural language processing (NLP) systems, there is a growing recognition that such approaches deliver too shallow an understanding. To continue to make progress in textual-information management, vast amounts of semantic knowledge are needed to give our software the capacity for deeper and more meaningful understanding of text. What is Commonsense Knowledge? Of the different sorts of semantic knowledge that are researched, arguably the most general and widely applicable kind is knowledge about the everyday world that is possessed by all people – what is widely called ‘commonsense knowledge’. While to the average person the term “common sense” is regarded as synonymous with “good judgment,” to the AI community it is used in a technical sense to refer to the millions of basic facts and understandings possessed by most people. A lemon is sour. To open a door, you must usually first turn the doorknob. If you forget someone’s birthday, they may be unhappy with you. Commonsense knowledge, thusly defined, spans a huge portion of human experience, encompassing knowledge about the spatial, physical, social, temporal, and psychological aspects of typical everyday life. Because it is assumed that every person possesses common sense, such knowledge is typically omitted from social communications, such as text. A full understanding of any text then, requires a surprising amount of common sense, which currently only people possess. It is our purpose to find ways to provide such common sense to machines.