Smart: Semantically mashup rest web services

TL;DR

The paper introduces SMART, a platform that enables users to create personalized REST web service mashups using simple keyword searches and semantic descriptions, eliminating the need for programming skills.

Contribution

It defines an ontology for REST services and integrates semantic matching and inference to facilitate user-driven, intelligent mashup creation without programming.

Findings

Semantic matching effectively identifies relevant web services.

Ontology-based inference enables automatic chaining of services.

User-friendly interface simplifies mashup construction.

Abstract

A mashup is a combination of information from more than one source, mixed up in a way to create something new, or at least useful. Anyone can find mashups on the internet, but these are always specifically designed for a predefined purpose. To change that fact, we implemented a new platform we called the SMART platform. SMART enables the user to make his own choices as for the REST web services he needs to call in order to build an intelligent personalized mashup, from a Google-like simple search interface, without needing any programming skills. In order to achieve this goal, we defined an ontology that can hold REST web services descriptions. These descriptions encapsulate mainly, the input type needed for a service, its output type, and the kind of relation that ties the input to the output. Then, by matching the user input query keywords, with the REST web services definitions in our ontology, we can find registered services individuals in this ontology, and construct the raw REST query for each service found. The wrap up from the keywords, into semantic definitions, in order to find the matching service individual, then the wrap down from the semantic service description of the found individual, to the raw REST call, and finally the wrap up of the result again into semantic individuals, is done for two main purposes: the first to let the user use simple keywords in order to build complex mashups, and the second to benefit from the ontology inference engine in a way, where services instances can be tied together into an intelligent mashup, simply by making each service output individuals, stand as the next service input.