Accelerating Innovation Through Analogy Mining

The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods. Previous approaches include costly hand-created databases that have high relational structure (e.g., predicate calculus representations) but are very sparse. Simpler machine-learning/information-retrieval similarity metrics can scale to large, natural-language datasets, but struggle to account for structural similarity, which is central to analogy. In this paper we explore the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas compared to analogies retrieved by traditional methods. Our results suggest a promising approach to enabling computational analogy at scale is to learn and leverage weaker structural representations.Comment: KDD 201

TopicViz: Semantic Navigation of Document Collections

When people explore and manage information, they think in terms of topics and themes. However, the software that supports information exploration sees text at only the surface level. In this paper we show how topic modeling -- a technique for identifying latent themes across large collections of documents -- can support semantic exploration. We present TopicViz, an interactive environment for information exploration. TopicViz combines traditional search and citation-graph functionality with a range of novel interactive visualizations, centered around a force-directed layout that links documents to the latent themes discovered by the topic model. We describe several use scenarios in which TopicViz supports rapid sensemaking on large document collections

The Cognitive Atlas: Employing Interaction Design Processes to Facilitate Collaborative Ontology Creation

The Cognitive Atlas is a collaborative knowledge-building project that aims to develop an ontology that characterizes the current conceptual framework among researchers in cognitive science and neuroscience. The project objectives from the beginning focused on usability, simplicity, and utility for end users. Support for Semantic Web technologies was also a priority in order to support interoperability with other neuroscience projects and knowledge bases. Current off-the-shelf semantic web or semantic wiki technologies, however, do not often lend themselves to simple user interaction designs for non-technical researchers and practitioners; the abstract nature and complexity of these systems acts as point of friction for user interaction, inhibiting usability and utility. Instead, we take an alternate interaction design approach driven by user centered design processes rather than a base set of semantic technologies. This paper reviews the initial two rounds of design and development of the Cognitive Atlas system, including interactive design decisions and their implementation as guided by current industry practices for the development of complex interactive systems

Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking

Efficiently reviewing scholarly literature and synthesizing prior art are crucial for scientific progress. Yet, the growing scale of publications and the burden of knowledge make synthesis of research threads more challenging than ever. While significant research has been devoted to helping scholars interact with individual papers, building research threads scattered across multiple papers remains a challenge. Most top-down synthesis (and LLMs) make it difficult to personalize and iterate on the output, while bottom-up synthesis is costly in time and effort. Here, we explore a new design space of mixed-initiative workflows. In doing so we develop a novel computational pipeline, Synergi, that ties together user input of relevant seed threads with citation graphs and LLMs, to expand and structure them, respectively. Synergi allows scholars to start with an entire threads-and-subthreads structure generated from papers relevant to their interests, and to iterate and customize on it as they wish. In our evaluation, we find that Synergi helps scholars efficiently make sense of relevant threads, broaden their perspectives, and increases their curiosity. We discuss future design implications for thread-based, mixed-initiative scholarly synthesis support tools.Comment: ACM UIST'2

Crowdsourcing and Human Computation: Systems, Studies and Platforms

Crowdsourcing and human computation are transforming human-computer interaction, and CHI has led the way. The seminal publication in human computation was initially published in CHI in 2004 [1], and the first paper investigating Mechanical Turk as a user study platform has amassed over one hundred citations in two years [5]. However, we are just beginning to stake out a coherent research agenda for the field. This workshop will bring together researchers in the young field of crowdsourcing and human computation and produce three artifacts: a research agenda for the field, a vision for ideal crowdsourcing platforms, and a group-edited bibliography. These resources will be publically disseminated on the web and evolved and maintained by the community

NICE: Social translucence through UI intervention

ABSTRACT Social production systems such as Wikipedia rely on attracting and motivating volunteer contributions to be successful. One strong demotivating factor can be when an editor's work is discarded, or "reverted", by others. In this paper we demonstrate evidence of this effect and design a novel interface aimed at improving communication between the reverting and reverted editors. We deployed the interface in a controlled experiment on the live Wikipedia site, and report on changes in the behavior of 487 contributors who were reverted by editors using our interface. Our results suggest that simple interface modifications (such as informing Wikipedians that the editor they are reverting is a newcomer) can have substantial positive effects in protecting against contribution loss in newcomers and improving the quality of work done by more experienced contributors

The Cognitive Atlas: Toward a Knowledge Foundation for Cognitive Neuroscience

Cognitive neuroscience aims to map mental processes onto brain function, which begs the question of what “mental processes” exist and how they relate to the tasks that are used to manipulate and measure them. This topic has been addressed informally in prior work, but we propose that cumulative progress in cognitive neuroscience requires a more systematic approach to representing the mental entities that are being mapped to brain function and the tasks used to manipulate and measure mental processes. We describe a new open collaborative project that aims to provide a knowledge base for cognitive neuroscience, called the Cognitive Atlas (accessible online at http://www.cognitiveatlas.org), and outline how this project has the potential to drive novel discoveries about both mind and brain

Understanding human-machine networks: A cross-disciplinary survey

© 2017 ACM. In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of sociotechnical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends