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172
ConceptNet: A Practical Commonsense Reasoning Toolkit
- BT TECHNOLOGY JOURNAL
, 2004
"... 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," &qu ..."
Abstract
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Cited by 343 (7 self)
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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.
Semantic Wikipedia
, 2006
"... Wikipedia is the world’s largest collaboratively edited source of encyclopaedic knowledge. But its contents are barely machineinterpretable. Structural knowledge, e. g. about how concepts are interrelated, can neither be formally stated nor automatically processed. Also the wealth of numerical data ..."
Abstract
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Cited by 263 (19 self)
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Wikipedia is the world’s largest collaboratively edited source of encyclopaedic knowledge. But its contents are barely machineinterpretable. Structural knowledge, e. g. about how concepts are interrelated, can neither be formally stated nor automatically processed. Also the wealth of numerical data is only available as plain text and thus can not be processed by its actual meaning. We provide an extension to be integrated in Wikipedia, that allows the typing of links between articles and the specification of typed data inside the articles in an easy-to-use manner. Enabling even casual users to participate in the creation of an open semantic knowledge base, Wikipedia has the chance to become a resource of semantic statements, hitherto unknown regarding size, scope, openness, and internationalisation. These semantic enhancements bring to Wikipedia benefits of today’s semantic technologies: more specific ways of searching and browsing. Also, the RDF export, that gives direct access to the formalised knowledge, opens Wikipedia up to a wide range of external applications, that will be able to use it as a background knowledge base.
ConceptNet 3: a flexible, multilingual semantic network for common sense knowledge
- the 22nd Conference on Artificial Intelligence
, 2007
"... The Open Mind Common Sense project has been collecting common-sense knowledge from volunteers on the Internet since 2000. This knowledge is represented in a machine-interpretable semantic network called ConceptNet. We present ConceptNet 3, which improves the acquisition of new knowledge in ConceptNe ..."
Abstract
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Cited by 98 (19 self)
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The Open Mind Common Sense project has been collecting common-sense knowledge from volunteers on the Internet since 2000. This knowledge is represented in a machine-interpretable semantic network called ConceptNet. We present ConceptNet 3, which improves the acquisition of new knowledge in ConceptNet and facilitates turning edges of the network back into natural language. We show how its modular design helps it adapt to different data sets and languages. Finally, we evaluate the content of ConceptNet 3, showing that the information it contains is comparable with WordNet and the
Probase: A Probabilistic Taxonomy for Text Understanding
"... Knowledge is indispensable to understanding. The ongoing information explosion highlights the need to enable machines to better understand electronic text in human language. Much work has been devoted to creating universal ontologies or taxonomies for this purpose. However, none of the existing onto ..."
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Cited by 76 (21 self)
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Knowledge is indispensable to understanding. The ongoing information explosion highlights the need to enable machines to better understand electronic text in human language. Much work has been devoted to creating universal ontologies or taxonomies for this purpose. However, none of the existing ontologies has the needed depth and breadth for “universal understanding”. In this paper, we present a universal, probabilistic taxonomy that is more comprehensive than any existing ones. It contains 2.7 million concepts harnessed automatically from a corpus of 1.68 billion web pages. Unlike traditional taxonomies that treat knowledge as black and white, it uses probabilities to model inconsistent, ambiguous and uncertain information it contains. We present details of how the taxonomy is constructed, its probabilistic modeling, and its potential applications in text understanding.
Unsupervised activity recognition using automatically mined common sense
- In AAAI
, 2005
"... A fundamental difficulty in recognizing human activities is obtaining the labeled data needed to learn models of those activities. Given emerging sensor technology, however, it is possible to view activity data as a stream of natural language terms. Activity models are then mappings from such terms ..."
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Cited by 71 (5 self)
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A fundamental difficulty in recognizing human activities is obtaining the labeled data needed to learn models of those activities. Given emerging sensor technology, however, it is possible to view activity data as a stream of natural language terms. Activity models are then mappings from such terms to activity names, and may be extracted from text corpora such as the web. We show that models so extracted are sufficient to automatically produce labeled segmentations of activity data with an accuracy of 42 % over 26 activities, well above the 3.8 % baseline. The segmentation so obtained is sufficient to bootstrap learning, with accuracy of learned models increasing to 52%. To our knowledge, this is the first human activity inferencing system shown to learn from sensed activity data with no human intervention per activity learned, even for labeling.
Analogyspace: reducing the dimensionality of common sense knowledge. In:
- AAAI’08: Proceedings of the 23rd national conference on Artificial intelligence.
, 2008
"... Abstract We are interested in the problem of reasoning over very large common sense knowledge bases. When such a knowledge base contains noisy and subjective data, it is important to have a method for making rough conclusions based on similarities and tendencies, rather than absolute truth. We pres ..."
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Cited by 64 (28 self)
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Abstract We are interested in the problem of reasoning over very large common sense knowledge bases. When such a knowledge base contains noisy and subjective data, it is important to have a method for making rough conclusions based on similarities and tendencies, rather than absolute truth. We present AnalogySpace, which accomplishes this by forming the analogical closure of a semantic network through dimensionality reduction. It self-organizes concepts around dimensions that can be seen as making distinctions such as "good vs. bad" or "easy vs. hard", and generalizes its knowledge by judging where concepts lie along these dimensions. An evaluation demonstrates that users often agree with the predicted knowledge, and that its accuracy is an improvement over previous techniques.
A goal-oriented web browser
- In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems (CHI ’06
, 2006
"... p0220 p0225 p0230 Many users are familiar with the interesting but limited functionality of data detector interfaces like Microsoft’s Smart Tags and Google’s AutoLink. In this chapter we significantly expand the breadth and functionality of this type of user interface through the use of large-scale ..."
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Cited by 52 (1 self)
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p0220 p0225 p0230 Many users are familiar with the interesting but limited functionality of data detector interfaces like Microsoft’s Smart Tags and Google’s AutoLink. In this chapter we significantly expand the breadth and functionality of this type of user interface through the use of large-scale knowledge bases of semantic information. The result is a Web browser that is able to generate personalized semantic hypertext, providing a goal-oriented browsing experience. We present (1) Creo, a programming-by-example system for the Web that allows users to create a general purpose procedure with a single example; and (2) Miro, a data detector that matches the content of a page to high-level user goals. An evaluation with 34 subjects found that they were more efficient using our system, and that the subjects would use features like these if they were integrated into their Web browser. s0010 p0235 p0240
Commonsense reasoning in and over natural language
- PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS (KES-2004
, 2004
"... ConceptNet is a very large semantic network of commonsense knowledge suitable for making various kinds of practical inferences over text. ConceptNet captures a wide range of commonsense concepts and relations like those in Cyc, while its simple semantic network structure lends it an ease-of-use co ..."
Abstract
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Cited by 50 (3 self)
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ConceptNet is a very large semantic network of commonsense knowledge suitable for making various kinds of practical inferences over text. ConceptNet captures a wide range of commonsense concepts and relations like those in Cyc, while its simple semantic network structure lends it an ease-of-use comparable to WordNet. To meet the dual challenge of having to encode complex higher-order concepts, and maintaining ease-of-use, we introduce a novel use of semi-structured natural language fragments as the knowledge representation of commonsense concepts. In this paper, we present a methodology for reasoning flexibly about these semi-structured natural language fragments. We also examine the tradeoffs associated with representing commonsense knowledge in formal logic versus in natural language. We conclude that the flexibility of natural language makes it a highly suitable representation for achieving practical inferences over text, such as context finding, inference chaining, and conceptual analogy.
EM-ONE: An Architecture for Reflective Commonsense Thinking
, 2005
"... This thesis describes EM-ONE, an architecture for commonsense thinking capable of reflective reasoning about situations involving physical, social, and mental dimensions. EM-ONE uses as its knowledge base a library of commonsense narratives, each describing the physical, social, and mental activity ..."
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Cited by 48 (0 self)
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This thesis describes EM-ONE, an architecture for commonsense thinking capable of reflective reasoning about situations involving physical, social, and mental dimensions. EM-ONE uses as its knowledge base a library of commonsense narratives, each describing the physical, social, and mental activity that occurs during an interaction between several actors. EM-ONE reasons with these narratives by applying "mental critics, " procedures that debug problems that exist in the outside world or within EM-ONE itself. Mental critics draw upon commonsense narratives to suggest courses of action, methods for deliberating about the circumstances and consequences of those actions, and—when things go wrong—ways to reflect upon and debug the activity of previously invoked mental critics. Mental critics are arranged into six layers, the reactive, deliberative, reflective, self-reflective, self-conscious, and self-ideals layers. The selection of mental critics within these six layers is itself guided by a separate collection
The Restaurant Game: Learning Social Behavior and Language from Thousands of Players Online
"... We envision a future in which conversational virtual agents collaborate with humans in games and training simulations. A representation of common ground for everyday scenarios is essential for these agents if they are to be effective collaborators and communicators. Effective collaborators can infer ..."
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Cited by 38 (8 self)
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We envision a future in which conversational virtual agents collaborate with humans in games and training simulations. A representation of common ground for everyday scenarios is essential for these agents if they are to be effective collaborators and communicators. Effective collaborators can infer a partner’s goals and predict future actions. Effective communicators can infer the meaning of utterances based on semantic context. This article introduces a computational model of common ground called a Plan Network, a statistical model that encodes context-sensitive expected patterns of behavior and language, with dependencies on social roles and object affordances. We describe a methodology for unsupervised learning of a Plan Network using a multiplayer video game, visualization of this network, and evaluation of the learned model with respect to human judgment of typical behavior. Specifically, we describe learning the Restaurant Plan Network from data collected from over 5,000 gameplay sessions of a minimal investment multiplayer online (MIMO) role-playing game called The Restaurant Game. Our results demonstrate a kind of social common sense for virtual agents, and have implications for automatic authoring of content in the future.
