Abstract
BackgroundIn this paper, we investigate the effectiveness of a CRF-based learning method for identifying necessary Web of Things (WoT) application components that would satisfy the users’ requests issued in natural language. For instance, a user request such as “archive all sports breaking news” can be satisfied by composing a WoT application that consists of ESPN breaking news service and Dropbox as a storage service.Findings We built an engine that can identify the necessary application components by recognizing a main act (MA) or named entities (NEs) from a given request. We trained this engine with the descriptions of WoT applications (called recipes) that were collected from IFTTT WoT platform. IFTTT hosts over 300 WoT entities that offer thousands of functions referred to as triggers and actions. There are more than 270,000 publicly-available recipes composed with those functions by real users. Therefore, the set of these recipes is well-qualified for the training of our MA and NE recognition engine.ConlusionsWe share our unique experience of generating the training and test set from these recipe descriptions and assess the performance of the CRF-based language method. Based on the performance evaluation, we introduce further research directions.
Highlights
In this paper, we investigate the effectiveness of a CRF-based learning method for identifying necessary Web of Things (WoT) application components that would satisfy the users’ requests issued in natural language
We could not account for any context of a token that we labeled with a named entities (NEs)
We devised a CRF-based learning framework to generate an engine that can recognize desired triggers and actions for user requests specified in natural language
Summary
We investigate the effectiveness of a CRF-based learning method for identifying necessary Web of Things (WoT) application components that would satisfy the users’ requests issued in natural language. Findings: We built an engine that can identify the necessary application components by recognizing a main act (MA) or named entities (NEs) from a given request. We trained this engine with the descriptions of WoT applications (called recipes) that were collected from IFTTT WoT platform. IFTTT is a platform that hosts Web of Things (WoT) entities that are referred to as channels.1 These channels offer functionalities such as triggers and actions which are ingredients for event-driven applications called recipes. In Hyun et al (2015), we presented the ultimate goal of enhancing user experience by demonstrating a conceptual system that automatically composes and executes an IFTTT recipe given a user request issued entirely in natural language.
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