Keyword-based, context-aware selection of natural language query patterns

Abstract

Pervasive access to distributed data sources by means of mobile devices is becoming a frequent realistic operational context in many application domains. In these scenarios data access may be thwarted by the scarce knowledge that users have of the application and of the underlying data schemas and complicated by limited query interfaces, due to the small size of the devices.\ud \ud A viable solution to this problem could be expressing the queries in natural language; however, in applications like medical emergencies, data management systems must obey requirements such as very fast and precise data access which make this solution infeasible.\ud \ud To reduce the time needed to get answers to user queries, the paper proposes a lightweight, context-aware approach based on the combination of keywords with natural language queries. The method employs ontologies and query patterns to support the users in formulating the most appropriate query for retrieving the desired data. Precision and query efficiency are further improved by focusing searches only to the data which are meaningful w.r.t. the current context, thus supporting the users' situation awareness.\ud The approach has been integrated in the SAFE system, developed for mobile and Web, and has been applied in cardiology to support medical personnel in emergency interventions on patients affected by chronic cerebro-vascular diseases. Experimental results have shown that the proposed solution significantly reduces the time to get useful data w.r.t. traditional form-based approaches

A multilevel graph approach for IoT-based complex scenario management through situation awareness and semantic approaches

“…The concept of context has been extended to all aspects that characterize the user concerning the capabilities that information systems can deliver. For this reason, complex and general context models have been proposed to support context-aware applications, which leverage them, for example, to adapt interfaces, tailor a set of application-relevant data [8], increase the accuracy of information retrieval, discover services, and compose services [9], make user interaction implicit, and create intelligent environments [22].…”

“…Properly filtered data can be used to achieve the Context-Awareness [8][9][10]. An in-depth comprehension of the context is critical to choosing or designing a suitable framework, which allows extracting relevant information to process data, model reality, and provide answers.…”

QUASL: A framework for question answering and its Application to business intelligence

“…its structure), as pointed out by Orsi & al. [23]. Li et al [24] approximate this problem for answering top-k queries efficiently.…”

“…Our proposal is thus much more powerful, e.g., to provide means for including user context or more complicated natural language patterns and relate them with other background knowledge, which is of utermost importance as stated in [26]. Secondly, SAFE [23] is an answering system dedicated to mobile devices in the medical domain. It uses patterns, i.e.…”