A competitive environment for exploratory query expansion

Most information workers query digital libraries many times a day. Yet people have little opportunity to hone their skills in a controlled environment, or compare their performance with others in an objective way. Conversely, although search engine logs record how users evolve queries, they lack crucial information about the user's intent. This paper describes an environment for exploratory query expansion that pits users against each other and lets them compete, and practice, in their own time and on their own workstation. The system captures query evolution behavior on predetermined information-seeking tasks. It is publicly available, and the code is open source so that others can set up their own competitive environments

Hi, how can I help you?: Automating enterprise IT support help desks

Question answering is one of the primary challenges of natural language understanding. In realizing such a system, providing complex long answers to questions is a challenging task as opposed to factoid answering as the former needs context disambiguation. The different methods explored in the literature can be broadly classified into three categories namely: 1) classification based, 2) knowledge graph based and 3) retrieval based. Individually, none of them address the need of an enterprise wide assistance system for an IT support and maintenance domain. In this domain the variance of answers is large ranging from factoid to structured operating procedures; the knowledge is present across heterogeneous data sources like application specific documentation, ticket management systems and any single technique for a general purpose assistance is unable to scale for such a landscape. To address this, we have built a cognitive platform with capabilities adopted for this domain. Further, we have built a general purpose question answering system leveraging the platform that can be instantiated for multiple products, technologies in the support domain. The system uses a novel hybrid answering model that orchestrates across a deep learning classifier, a knowledge graph based context disambiguation module and a sophisticated bag-of-words search system. This orchestration performs context switching for a provided question and also does a smooth hand-off of the question to a human expert if none of the automated techniques can provide a confident answer. This system has been deployed across 675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201

User Intent Communication in Robot-Assisted Shopping for the Blind

The research reported in this chapter describes our work on robot-assisted shopping for the blind. In our previous research, we developed RoboCart, a robotic shopping cart for the visually impaired (Gharpure, 2008; Kulyukin et al., 2008; Kulyukin et al., 2005). RoboCart's operation includes four steps: 1) the blind shopper (henceforth the shopper) selects

Intents-based Service Discovery and Integration

With the proliferation of Web services, when developing a new application, it makes sense to seek and leverage existing Web services rather than implementing the corresponding components from scratch. Therefore, significant research efforts have been devoted to the techniques for service discovery and integration. However, most of the existing techniques are based on the ternary participant classification of the Web service architecture which only takes into consideration the involvement of service providers, service brokers, and application developers. The activities of application end users are usually ignored. This thesis presents an Intents-based service discovery and integration approach at the conceptual level inspired by two industrial protocols: Android Intents and Web Intents. The proposed approach is characterized by allowing application end users to participate in the process of service seeking. Instead of directly binding with remote services, application developers can set an intent which semantically represents their service goal. An Intents user agent can resolve the intent and generate a list of candidate services. Then application end users can choose a service as the ultimate working service. This thesis classifies intents into explicit intents, authoritative intents, and naïve intents, and examines in depth the issue of naïve intent resolution analytically and empirically. Based on the empirical analysis, an adaptive intent resolution approach is devised. This thesis also presents a design for the Intents user agent and demonstrates its proof-of-concept prototype. Finally, Intents and the Intents user agent are applied to integrate Web applications and native applications on mobile devices