Affiliations: Accenture Technology Labs, 161 N. Clark St., Chicago, IL 60601, USA. E-mail: [email protected] | Department of Computer Science, Rutgers University, 110 Frelinghuysen Rd., Piscataway, NJ 08854, USA. E-mail: {mlittman,borgida}@cs.rutgers.edu
Abstract: This paper considers the problem of learning task specific web-service descriptions from traces of users successfully completing a task. Unlike prior approaches, we take a traditional machine-learning perspective to the construction of web-service models from data. Our representation models both syntactic features of web-service schemas (including lists and optional elements), as well as semantic relations between objects in the task. Together, these learned models form a full schematic model of the dataflow. Our theoretical results, which are the main novelty in the paper, show that this structure can be learned efficiently: the number of traces required for learning grows polynomially with the size of the task. We also present real-world task descriptions mined from tasks using online services from Amazon and Google.
Keywords: Machine learning, web services, sample complexity, apprenticeship learning
