Open Mind Common Sense: Knowledge Acquisition from the General Public

Singh, Push; Lin, Thomas; Mueller, Erik T.; Lim, Grace; Perkins, Travell; Zhu, Wan Li

doi:10.1007/3-540-36124-3_77

Cited by 297 publications

(219 citation statements)

References 10 publications

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“…Horn formulas are, for example, a natural framework for representing systems of rules for expert systems. An interesting potential new application area for Horn formulas is the automated, interactive development of large-scale knowledge bases of commonsense knowledge (see [36] for the description of such a project). This application has algorithmic aspects involving knowledge representation, reasoning, learning and knowledge update.…”

Section: Introductionmentioning

confidence: 99%

On Approximate Horn Formula Minimization

Bhattacharya

DasGupta

Mubayi

et al. 2010

Automata, Languages and Programming

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The minimization problem for Horn formulas is to find a Horn formula equivalent to a given Horn formula, using a minimum number of clauses. A 2 log 1−ǫ (n) -inapproximability result is proven, which is the first inapproximability result for this problem. We also consider several other versions of Horn minimization. The more general version which allows for the introduction of new variables is known to be too difficult as its equivalence problem is co-NP-complete. Therefore, we propose a variant called Steinerminimization, which allows for the introduction of new variables in a restricted manner. Steiner-minimization of Horn formulas is shown to be MAX-SNP-hard. In the positive direction, a o(n), namely, O(n log log n/(log n) 1/4 )-approximation algorithm is given for the Steiner-minimization of definite Horn formulas. The algorithm is based on a new result in algorithmic extremal graph theory, on partitioning bipartite graphs into complete bipartite graphs, which may be of independent interest. Inapproximability results and approximation algorithms are also given for restricted versions of Horn minimization, where only clauses present in the original formula may be used.

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Section: Introductionmentioning

confidence: 99%

On Approximate Horn Formula Minimization

Bhattacharya

DasGupta

Mubayi

et al. 2010

Automata, Languages and Programming

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“…The goal of the Open Mind Common Sense (OMCS) [Singh et al 2002] project is to provide intuition to AI systems and applications by giving them access to a broad collection of basic knowledge, along with the computational tools to work with it. This knowledge helps applications to understand the way objects relate to each other in the world, people's goals when they go about their daily lives, and the emotional content of events or situations.…”

Section: The Open Mind Common Sense Knowledge Basementioning

confidence: 99%

Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying

Dinakar

Jones

Havasi

et al. 2012

ACM Trans. Interact. Intell. Syst.

315

228

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Cyberbullying (harassment on social networks) is widely recognized as a serious social problem, especially for adolescents. It is as much a threat to the viability of online social networks for youth today as spam once was to email in the early days of the Internet. Current work to tackle this problem has involved social and psychological studies on its prevalence as well as its negative effects on adolescents. While true solutions rest on teaching youth to have healthy personal relationships, few have considered innovative design of social network software as a tool for mitigating this problem. Mitigating cyberbullying involves two key components: robust techniques for effective detection and reflective user interfaces that encourage users to reflect upon their behavior and their choices.Spam filters have been successful by applying statistical approaches like Bayesian networks and hidden Markov models. They can, like Google's GMail, aggregate human spam judgments because spam is sent nearly identically to many people. Bullying is more personalized, varied, and contextual. In this work, we present an approach for bullying detection based on state-of-the-art natural language processing and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying (e.g. appearance, intelligence, racial and ethnic slurs, social acceptance, and rejection), and construct BullySpace, a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers.On the intervention side, we explore a set of reflective user-interaction paradigms with the goal of promoting empathy among social network participants. We propose an "air traffic control"-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in-context, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies.

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“…The idea of directly engaging end-users for knowledge acquisition was explored in Open Mind Common Sense [6], the Open Directory Project, and Wikipedia. These projects fall under headings of crowdsourcing and community based-design, and they embody the idea of distributed collaborative work.…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…HDCM applies ideas from Crowdsourcing [6] and Human-Computation [7] to the problem of enumerating the stimuli-response space of a conversation. HDCM acquires the knowledge from the people who have it, the domain novices and experts.…”

Section: Introductionmentioning

confidence: 99%

Human-Centered Distributed Conversational Modeling: Efficient Modeling of Robust Virtual Human Conversations

Rossen

Lind

Lok

2009

Intelligent Virtual Agents

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Abstract. Currently, applications that focus on providing conversations with virtual humans require extensive work to create robust conversational models. We present a new approach called Human-centered Distributed Conversational Modeling. Using this approach, users create conversational models in a distributed manner. To do this, end-users interact with virtual humans to provide new stimuli (questions and statements), and domain-specific experts (e.g. medical/psychology educators) provide new virtual human responses. Using this process, users become the primary developers of conversational models. We tested our approach by creating an example application, Virtual People Factory. Using Virtual People Factory, a pharmacy instructor and 186 pharmacy students were able to create a robust conversational model in 15 hours. This is approximately 10% of the time typical in current approaches and results in more comprehensive coverage of the conversational space. In addition, surveys demonstrate the acceptability of this approach by both educators and students.

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Open Mind Common Sense: Knowledge Acquisition from the General Public

Cited by 297 publications

References 10 publications

On Approximate Horn Formula Minimization

On Approximate Horn Formula Minimization

Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying

Human-Centered Distributed Conversational Modeling: Efficient Modeling of Robust Virtual Human Conversations

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