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ConceptNet is a freely available commonsense knowledge base and natural-language-processing tool-kit which supports many practical textual-reasoning tasks over real-world documents including topic-gisting, analogy-making, and other context oriented inferences. The knowledge base 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. 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.
For example, what is the precise color of a "red apple?" In logic, we might be able to formally represent the range in the color spectrum corresponding to a "red apple," but in natural language, the word "red" is imprecise and has various interpretations. Consider the differing colors which map to "red apple" versus "red wine" versus "red hair." WordNet has tried to address this issue of semantic leakage by imposing boundaries on word called word senses. In many cases, such boundaries are very clear, as in the case of homonyms (e.g. river bank versus financial bank), but in the case of more systematic polysemies (e.g. WordNet has different senses for a short sleep versus a long sleep), it is clear that such boundaries are artificial.
Things fall down, not up. Weddings (sometimes) have a bride and a groom. If someone yells at you, they"re probably angry. One of the reasons that computers seem dumber than humans is that they don"t have common sense--a myriad of simple facts about everyday life and the ability to make use of that knowledge easily when appropriate. A long-standing dream of artificial intelligence has been to put that kind of knowledge into computers, but applications of commonsense knowledge have been slow in coming.
We were interested in the question of whether it was possible to distribute the problem of building a commonsense knowledge base across thousands of people on the Web, and especially, people with little or no special training in computer science or artificial intelligence. We were interested in whether the "average person" could participate in the process of building a commonsense knowledge base. After all, every ordinary person possesses the kind of commonsense we wish to give our machines!
Asociacion Espanola de Inteligencia Artificial "Best AI Paper Award" Recipient. One novice user submitted the query: "I want to find other people who like movies," and obtained many irrelevant and unwanted search results on the topic of movies. In contrast, a more experienced user formed the query: " +"my homepage" +"my interests" +"movies" " and was able to get many relevant results. The experienced user chose not only a keyword ("movies") on the topic of the search, but also two keywords ("my homepage", "my interests") differentiating the context in which the topic keyword should appear. In choosing these keywords, the experienced user used her expertise to guide a series of inferences from the search goal.
We investigated three broad properties of intelligent software agents -- communication through and understanding human language; exercising some commonsense to prevent obvious mistakes; and learning from past user interactions to improve future interactions. The ARIA Photo Agent provided an application platform from which approaches to these properties were tested, and what resulted was an intelligent software agent that can automatically annotate photos by understanding the user's English text; robustly retrieve annotated photos by incorporating commonsense; and learn personal commonsense of the user and use that knowledge to improve future retrieval by knowledge specific to the user.
Makebelieve, an interactive story generation agent that can generate short fictional texts of 5 to 20 lines when the user supplies the first line of the story. Our fail-soft approach to story generation represents a hybrid approach inheriting from both the structuralist and transformationalist traditions. It also incorporates a novel knowledge source, commonsense, which unlike other story knowledge bases, is not specifically purposed for story telling. Using a subset of knowledge in Open Mind, which describes causation, Makebelieve performs fuzzy and creative inference to generate casual chains, which become the basis for a storyline.
In our photo domain, we propose a mechanism for robust retrieval by expanding the concepts depicted in the photos, thus going beyond lexical-based expansion. Because photos often depict places, situations and events in everyday life, concepts depicted in photos such as place, event, and activity can be expanded based on our "common sense" notions of how concepts relate to each other in the real world. For example, given the concept "surfer" and our common sense knowledge that surfers can be found at the beach, we might provide the additional concepts: "beach", "waves", "ocean", and "surfboard".
In user testing, we saw not only that ARIA adapts to the user, but that the user adapts to ARIA. Often a user's typing will bring up some photos relevant to the user's current text, but that also trigger the user's memory, encouraging him or her to explain related pictures in subsequent text, triggering new picture retrieval. This mutual adaptation is an important characteristic of adaptive systems, and our users particularly liked the continual interplay between their story and ARIA's suggestions.