As a class of context-aware systems, context-aware service recommendation (CASR) aims to bind high-quality services to users, w.r.t. their context requirements (e.g., invocation time, location, social profiles, connectivity). However, current CASR lacks a rich context modelling and does not allow for multi-relational interactions between users and services in different contexts. We propose a context-sensitive service recommendation, by constructing a contextual service knowledge graph (C-SKG), which we translated into a low-dimensional vector space to facilitate its processing. Dilated Recurrent Neural Networks are applied to allow a context-aware C-SKG embedding, based on the principles of subgraph-aware proximity. A recommendation algorithm, finally, returns the top-rated services w.r.t. the target user’s context and the proximity degrees.

Over two decades, context awareness has been incorporated into recommender systems in order to provide, not only the top-rated items to consumers but also the ones that are suitable to the user context. As a class of context-aware systems, context-aware service recommendation (CASR) aims to bind high-quality services to users, while taking into account their context requirements, including invocation time, location, social profiles, connectivity, and so on. However, current CASR approaches are not scalable with the huge amount of service data (QoS and context information, users reviews and feedbacks). In addition, they lack a rich representation of contextual information, as they adopt a simple matrix view. Moreover, current CASR approaches adopt the traditional user-service relation and they do not allow for multi-relational interactions between users and services in different contexts. To offer a scalable and context-sensitive service recommendation with great analysis and learning capabilities, we provide a rich and multi-relational representation of the CASR knowledge, based on the concept of knowledge graph. The constructedcontext-aware service knowledge graph(C-SKG) is, then, transformed into a low-dimensional vector space to facilitate its processing. For this purpose, we adopt Dilated Recurrent Neural Networks to propose a context-aware knowledge graph embedding, based on the principles of first-order and subgraph-aware proximity. Finally, a recommendation algorithm is defined to deliver the top-rated services according to the target user's context. Experiments have proved the accuracy and scalability of our solution, compared to state-of-the-art CASR approaches.

With the emergence of the open data movement, hundreds of thousands of datasets from various concerns are now freely available on the Internet. The access to a good number of these datasets is carried out through Web services which provide a 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 outputs 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.

In this demo paper, we present a new privacy preserving composition execution system. Our system allows to execute queries over multiple data services without revealing any extra information to any of the involved services. None of involved services (and their providers) is able to infer any information about the data the other services provide beyond what is permitted

In this paper, we propose a new approach to automatically compose data providing Web services. Our approach exploits existing mature works done in data integration systems. Specifically, data providing services are modeled as RDF Parameterized views over mediated ontologies. Then, an RDF oriented algorithm to compose services based on query rewriting techniques was devised. We apply also an optimization algorithm on the generated composition to speed up its execution. The results of our experiments show that the algorithms scale up very well up to a large number of services, covering thus most realistic applications.

International audience

International audience

This paper presents an approach whose objective is to support Web services substitution. Substitution means replacing a component with another component, as long as the replacing component produces the same output and satisfies the same requirements as the replaced component. Motives for substitution include Web service's non-responsiveness to client requests and better arrangement with another, competitor Web service. To perform Web services substitution with less impact on the ongoing, and sometimes critical, business processes, the approach proposes deploying communities of Web services. A community promotes the dynamic binding of Web services through a common interface, known as open service connectivity. The open service connectivity directs requests to and responses from Web services regardless of how these latter are specified, implemented, and located

The diversity of spatial information systems promotes the need to integrate heterogeneous spatial or geographic information systems (GIS) in a cooperative environment. The paper describes the research project ISIS (Interoperable Spatial Information System) which is a semantic mediation approach to support GIS interoperability. Its key characteristic is a dynamic resolution of semantic conflicts which is adequate for achieving autonomy, flexibility and extensibility. We propose a spatial OO data model and a mediation architecture based on multi-agent paradigm to support GIS interoperability.