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Per-service supervised learning for identifying desired WoT apps from user requests in natural language
- Source :
- PLoS ONE, PLoS ONE, Vol 12, Iss 11, p e0187955 (2017)
- Publication Year :
- 2017
- Publisher :
- Public Library of Science, 2017.
-
Abstract
- Web of Things (WoT) platforms are growing fast so as the needs for composing WoT apps more easily and efficiently. We have recently commenced the campaign to develop an interface where users can issue requests for WoT apps entirely in natural language. This requires an effort to build a system that can learn to identify relevant WoT functions that fulfill user's requests. In our preceding work, we trained a supervised learning system with thousands of publicly-available IFTTT app recipes based on conditional random fields (CRF). However, the sub-par accuracy and excessive training time motivated us to devise a better approach. In this paper, we present a novel solution that creates a separate learning engine for each trigger service. With this approach, parallel and incremental learning becomes possible. For inference, our system first identifies the most relevant trigger service for a given user request by using an information retrieval technique. Then, the learning engine associated with the trigger service predicts the most likely pair of trigger and action functions. We expect that such two-phase inference method given parallel learning engines would improve the accuracy of identifying related WoT functions. We verify our new solution through the empirical evaluation with training and test sets sampled from a pool of refined IFTTT app recipes. We also meticulously analyze the characteristics of the recipes to find future research directions.
- Subjects :
- Conditional random field
Service (systems architecture)
Computer and Information Sciences
Computer science
Interface (Java)
Inference
lcsh:Medicine
Social Sciences
02 engineering and technology
computer.software_genre
Bioinformatics
Research and Analysis Methods
Computer Architecture
Web of Things
Database and Informatics Methods
Named-entity recognition
Human–computer interaction
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Humans
Learning
Computer Simulation
Engines
Syntax
lcsh:Science
Natural Language Processing
Language
Language Acquisition
Grammar
Multidisciplinary
Mechanical Engineering
Supervised learning
lcsh:R
Linguistics
Named Entity Recognition
Mobile Applications
Incremental learning
Information Retrieval
Languages
Engineering and Technology
020201 artificial intelligence & image processing
lcsh:Q
User interface
Information Technology
computer
Natural language
Natural Language
Research Article
User Interfaces
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 12
- Issue :
- 11
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....be20620f76d98d47d8e4a496661c917c