Crowdsourcing common sense training data was born twenty years ago. It began with the idea to "harness the power of bored people on the Internet" to collect "what everyone knows but no one writes down". This was an era when we were all just starting to learn how to search the web, before people learned the dismal art of keywords, they tried typing their wants and needs. Search engines were woefully unequipped for these kinds of queries, and it was in that climate that we started ConceptNet, which we originally called OpenMind CommonSense (OMCS). Over the years, the effort and its methods evolved to address new applications. Today, we explore how structured common sense is becoming more relevant in NLP and the role it can play in helping solve problems of explainability, scalability, low-resourced languages, domain transfer, and AI bias. We look at how structured common sense and transformers are complementary and how we can combine them to keep the best of both worlds. Additionally, how does common sense need to evolve as we tackle larger and more complex and cross domain problems?

Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be used with modern NLP techniques such as word embeddings. ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges. Its knowledge is collected from many sources that include expert-created resources, crowd-sourcing, and games with a purpose. It is designed to represent the general knowledge involved in understanding language, improving natural language applications by allowing the application to better understand the meanings behind the words people use. When ConceptNet is combined with word embeddings acquired from distributional semantics (such as word2vec), it provides applications with understanding that they would not acquire from distributional semantics alone, nor from narrower resources such as WordNet or DBPedia. We demonstrate this with state-of-the-art results on intrinsic evaluations of word relatedness that translate into improvements on applications of word vectors, including solving SAT-style analogies.

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.

This editorial introduction describes the aims and scope of the special issue on Common Sense for Interactive Systems of the ACM Transactions on Interactive Intelligent Systems. It explains why the common sense knowledge problem is crucial for both artificial intelligence and human-computer interaction, and it shows how the four articles selected for this issue fit into the theme.

Most organizations have a wealth of knowledge about themselves available online, but little for a visitor to interact with on-site. At the MIT Media Lab, we have designed and deployed a novel intelligent signage system, the Glass Infrastructure (GI) that enables small groups of users to physically interact with this data and to discover the latent connections between people, projects, and ideas. The displays are built on an adaptive, unsupervised model of the organization developed using dimensionality reduction and common sense knowledge which automatically classifies and organizes the information.The GI is currently in daily use at the lab. We discuss the AI models development, the integration of AI into an HCI interface, and the use of the GI during the labs peak visitor periods. We show that the GI is used repeatedly by lab visitors and provides a window into the workings of the organization.

In AI, we often need to make sense of data that can be measured in many different dimensions -- thousands of dimensions or more -- especially when this data represents natural language semantics. Dimensionality reduction techniques can make this kind of data more understandable and more powerful, by projecting the data into a space of many fewer dimensions, which are suggested by the computer. Still, frequently, these results require more dimensions than the human mind can grasp at once to represent all the meaningful distinctions in the data.

In order to be helpful to people, the intelligent interfaces of the future will have to acquire, represent, and infer simple knowledge about everyday life and activities. While much work in AI has represented this knowledge at the word, sentence, and logical assertion level, we see a growing need to understand it at a larger granularity, that of stories.

We present a game-based interface for acquiring common sense knowledge. In addition to being interactive and entertaining, our interface guides the knowledge acquisition process to learn about the most salient characteristics of a particular concept. We use statistical classification methods to discover the most informative characteristics in the Open Mind Common Sense knowledge base, and use these characteristics to play a game of 20 Questions with the user. Our interface also allows users to enter knowledge more quickly than a more traditional knowledge-acquisition interface. An evaluation showed that users enjoyed the game and that it increased the speed of knowledge acquisition.

We present an overview of the workshop on Common Sense Knowledge and Goal-Oriented Interfaces held at the 2008 Intelligent User Interfaces conference. Six papers were accepted from diverse research groups, each offering innovative new research on interfaces that incorporate common sense knowledge and that are oriented around the goals of their users.

There is a mutually beneficial relationship between user interfaces and common sense reasoning and acquisition. Common sense knowledge enables interfaces to better understand and to be more grounded in the world of the user, thus improving the user's overall experience with the interface. This would not be possible without large sources of common sense knowledge, which likewise benefit from intelligent interfaces designed to make the knowledge acquisition processes more productive and enjoyable for the contributor. These two complementary interface types and their interaction are explored in this workshop.