6533b856fe1ef96bd12b2930

RESEARCH PRODUCT

Semantic web service discovery system for road traffic information services

Juan José Martínez DuráFrancisco R. Soriano GarcíaDolores M. Llidó EscriváJ. Javier Samper Zapater

subject

Information retrievalComputer sciencebusiness.industryGeneral EngineeringSemantic web servicesSimilarity measurecomputer.software_genreRoad traffic information systemsSocial Semantic WebComputer Science ApplicationsKnowledge discoverySemantic similarityKnowledge extractionArtificial IntelligenceInformation systemInformation retrievalSemantic integrationRelevance (information retrieval)Semantic Web StackData miningWeb servicebusinessMatchmakingcomputerSemantic matching

description

Create a multi-agent platform for a traveller information system (FIPA standards).Extend Paulucci algorithm with the use of seven similarity measures.Weight the similarity measure according to semantic relation and parameter nature.Improved running-time with a filtering pre-process for non-functional parameters.Improved the recall by measuring the sibling relationship concepts. We describe a multi-agent platform for a traveller information system, allowing travellers to find the road traffic information web service (WSs) that best fits their requirements. After studying existing proposals for discovery of semantic WS, we implemented a hybrid matching algorithm, which is described in detail here. Semantic WS profiles are annotated semantically as an OWL-S and also the traveller request is represented as a OWL-S profile. The algorithm assigns different weights and measures to each advertisedWS profile parameter, depending on their relevance, type and nature. To do this we have extended Paolucci's Algorithm and adapted it to our scenario. We have added new similarity measures, in particular, the use of the 'sibling' relationship, to improve the recall, allowing relevant services to be discovered by the users yet not retrieved by other algorithms. Although we have increased the similarity concept relations, we have improved the run-time using a pre-process filter step that reduces the set of potentially useful WS. This improves the scalability of the semantic matching algorithm.

10.1016/j.eswa.2015.01.005