IMAT: Intelligent Mobile Agent

With the evolution from a web of documents to a web of data, it is important we change the way we search for data among the huge amounts made available through social media, IoT devices, mobile devices, etc. A user should be able to express what he is searching for, in natural language or even through voice recognition, and be able to get the result he is seeking for, even if his search involves data of different nature and format: web pages, RSS feeds, RDF triples, web services calls, sensors data, etc. Another fact to consider is that Internet access is mainly done nowadays via mobile devices. In this paper, we propose a framework to answer those challenges by enabling the user to make a search from a Google-like simple interface, and thus create from his search keywords, an intelligent personalized mashup, without needing any programming skills, and yet integrating calls to REST web services, RSS feeds, RDF triples or any other data available on the web. In order to achieve this goal, we designed an ontology that can hold the description of any web resource, and implemented an automated service composition engine that takes advantage of this ontology. The engine has two main execution phases: The startup phase, implemented for performance improvement, which scans the semantically defined resources in order to create a HashMap linking each output to one or more chains of potential matching services. Matching services are services having an output linked by an inferred similarity to another service input. The processing phase, where a query analyzer inspects the ontology, and checks with Dbpedia and WordNet, in order to decompose the user request into input and output parameters. A type-based score model is used to decide which chain among the available chains to execute. The usefulness of our approach, was validated by developing a mobile client application which was used to test the implemented engine.