Skip to Main content Skip to Navigation
Journal articles

Composing Data Services with Uncertain Semantics

Abstract : With the emergence of the open data movement, hundreds of thousands of datasets from various concerns (e.g., healthcare, elections, patents, etc.) are now freely available on Internet. The access to a good number of these datasets is carried out through Web services which provide a flexible and standard way to interact with data. In this context, user’s queries often require the composition of multiple data Web services to be answered. Defining the semantics of data services is the first step towards automating their composition. An interesting approach to define the semantics of data services is by describing them as semantic views over a domain ontology. However, defining such semantic views cannot always be done with certainty, especially when the service’s returned data are too complex. In this paper, we propose a probabilistic approach to model the semantics uncertainty of data services. In our approach, a data service with an uncertain semantics is described by several possible semantic views, each one is associated with a probability. Services along with their possible semantic views are represented in a Block-Independent-Disjoint (noted BID) probabilistic service registry, and interpreted based on the Possible Worlds Semantics. Based on our modeling, we study the problem of interpreting an existing composition involving services with uncertain semantics. We also study the problem of compositing uncertain data services to answer a user query, and propose an efficient method to compute the different possible compositions and their probabilities.
Document type :
Journal articles
Complete list of metadatas
Contributor : Djamal Benslimane <>
Submitted on : Thursday, September 24, 2015 - 10:23:22 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:32 PM



Abdelhamid Malki, Mahmoud Barhamgi, Djamal Benslimane, Malki Mimoun, Sidi Mohamed Benslimane. Composing Data Services with Uncertain Semantics. IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers, 2015, 27 (4), pp.936--949. ⟨10.1109/TKDE.2014.2359661⟩. ⟨hal-01205149⟩



Record views