Neil Benn

Two-Staged Approach for Semantically Annotating and Brokering TV-related Services

Nowadays, more and more distributed digital TV and TV-related resources are published on the Web, such as Electronic Personal TV Guide (EPG) data. To enable applications to access these resources easily, the TV resource data is commonly provided by Web service technologies. The huge variety of data related to the TV domain and the wide range of services that provide it, raises the need to have a broker to discover, select and orchestrate services to satisfy the runtime requirements of applications that invoke these services. The variety of data and heterogeneous nature of the service capabilities makes it a challenging domain for automated web-service discovery and composition. To overcome these issues, we propose a two-stage service annotation approach, which is resolved by integrating Linked Services and IRS-III semantic web services framework, to complete the lifecycle of service annotating, publishing, deploying, discovering, orchestration and dynamic invocation. This approach satisfies both developer’s and application’s requirements to use Semantic Web Services (SWS) technologies manually and automatically.

Two-Fold Service Matchmaking --- Applying Ontology Mapping for Semantic Web Service Discovery

Semantic Web Services (SWS) aim at the automated discovery and orchestration of Web services on the basis of comprehensive, machine-interpretable semantic descriptions. Since SWS annotations usually are created by distinct SWS providers, semantic-level mediation, i.e. mediation between concurrent semantic representations, is a key requirement for SWS discovery. Since semantic-level mediation aims at enabling interoperability across heterogeneous semantic representations, it can be perceived as a particular instantiation of the ontology mapping problem. While recent SWS matchmakers usually rely on manual alignments or subscription to a common ontology, we propose a two-fold SWS matchmaking approach, consisting of (a) a general-purpose semantic-level mediator and (b) comparison and matchmaking of SWS capabilities. Our semantic-level mediation approach enables the implicit representation of similarities across distinct SWS by grounding service descriptions in so-called Mediation Spaces (MS). Given a set of SWS and their respective grounding, a SWS matchmaker automatically computes instance similarities across distinct SWS ontologies and matches the request to the most suitable SWS. A prototypical application illustrates our approach.

Autonomous Matchmaking Web Services

Current Semantic Web Services research investigates how to dynamically discover assemble and invoke Web services. Despite many research efforts, Semantic Web Services are still not fully recognized in industry. One important reason is the dissevered description layers of syntax and semantics. In other words, semantics is only useful for a service broker to discover services whereas service requesters still need to invoke services based on syntactic descriptions. In this paper, we view semantics from another angle to reform the Web service framework completely (even for input messages and output messages during invocation) by using only RDF and Linked Open Data. We introduce Autonomous Matchmaking Web Services in which Web services are brokering themselves to notify the service registry whether they are suitable to the requesters. This framework is designated to more efficiently work for dynamically assembling services at run time in a massively distributed environment.

Interoperable Multimedia Metadata through Similarity-Based Semantic Web Service Discovery

The increasing availability of multimedia (MM) resources, Web services as well as content, on the Web raises the need to automatically discover and process resources out of distributed repositories. However, the heterogeneity of applied metadata schemas and vocabularies --- ranging from XML-based schemas such as MPEG-7 to formal knowledge representation approaches --- raises interoperability problems. To enable MM metadata interoperability by means of automated similarity-computation, we propose a hybrid representation approach which combines symbolic MM metadata representations with a grounding in so-called Conceptual Spaces (CS). In that, we enable automatic computation of similarities across distinct metadata vocabularies and schemas in terms of spatial distances in shared CS. Moreover, such a vector-based approach is particularly well suited to represent MM metadata, given that a majority of MM parameters is provided in terms of quantified metrics. To prove the feasibility of our approach, we provide a prototypical implementation facilitating similarity-based discovery of publicly available MM services, aiming at federated MM content retrieval out of heterogeneous repositories.