Mediation of semantic web services in IRS-III

Business applications composed of heterogeneous distributed components or Web services need mediation to resolve data and process mismatches at runtime. This paper describes mediation in IRS-III, a framework and platform for developing WSMO-based Semantic Web Services. We present our approach to mediation within Semantic Web Services and highlight the role of WSMO mediator types when solving mismatches at the semantic level between a service requester and a service provider. We describe the components of our mediation framework and how it can handle data, goal and process mediation during the activities of selection, composition and invocation of Semantic Web Services

Development of Integrative Bioinformatics Applications using Cloud Computing resources and Knowledge Organization Systems (KOS).

Use of semantic web abstractions, in particular of domain neural Knowledge Organization Systems (KOS), to manage distributed, cloud based, integrative bioinformatics infrastructure. This presentation derives from recent publication:

Almeida JS, Deus HF, Maass W. (2010) S3DB core: a framework for RDF generation and management in bioinformatics infrastructures. BMC Bioinformatics. 2010 Jul 20;11(1):387. [PMID 20646315].

These PowerPoint slides were presented at Semantic Web Applications and Tools for Life Sciences December 10th, 2010, Berlin, Germany (http://www.swat4ls.org/2010/progr.php), keynote 9-10 am

Development of Integrative Bioinformatics Applications using Cloud Computing resources and Knowledge Organization Systems (KOS).

Use of semantic web abstractions, in particular of domain neural Knowledge Organization Systems (KOS), to manage distributed, cloud based, integrative bioinformatics infrastructure. This presentation derives from recent publication:

Almeida JS, Deus HF, Maass W. (2010) S3DB core: a framework for RDF generation and management in bioinformatics infrastructures. BMC Bioinformatics. 2010 Jul 20;11(1):387. [PMID 20646315].

These PowerPoint slides were presented at Semantic Web Applications and Tools for Life Sciences December 10th, 2010, Berlin, Germany (http://www.swat4ls.org/2010/progr.php), keynote 9-10 am

Distributed human computation framework for linked data co-reference resolution

Distributed Human Computation (DHC) is a technique used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI is envisioned to be a decentralised world-wide information space for sharing machine-readable data with minimal integration costs. There are many research problems in the Semantic Web that are considered as AI-complete problems. An example is co-reference resolution, which involves determining whether different URIs refer to the same entity. This is considered to be a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution in the Semantic Web when integrating distributed datasets. The traditional way to solve this problem is to design machine-learning algorithms. However, they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are published to the Linked Data Cloud

Towards runtime discovery, selection and composition of semantic services

Service-orientation is gaining momentum in distributed software applications, mainly because it facilitates interoperability and allows application designers to abstract from underlying implementation technologies. Service composition has been acknowledged as a promising approach to create composite services that are capable of supporting service user needs, possibly by personalising the service delivery through the use of context information or user preferences. In this paper we discuss the challenges of automatic service composition, and present DynamiCoS, which is a novel framework that aims at supporting service composition on demand and at runtime for the benefit of service end-users. We define the DynamiCoS framework based on a service composition life-cycle. Framework mechanisms are introduced to tackle each of the phases and requirements of this life-cycle. Semantic services are used in our framework to enable reasoning on the service requests issued by end users, making it possible to automate service discovery, selection and composition. We validate our framework with a prototype that we have built in order to experiment with the mechanisms we have designed. The prototype was evaluated in a testing environment using some use case scenarios. The results of our evaluation give evidences of the feasibility of our approach to support runtime service composition. We also show the benefits of semantic-based frameworks for service composition, particularly for end-users who will be able to have more control on the service composition process

Supporting the Mobile Querying of Existing Online Semantic Web Data for Context-Aware Applications

[EN] Mobile devices are increasingly multifunctional and personal, providing mobile applications with the necessary user information to achieve personalization. At the same time, detection technologies let such devices find nearby physical entities and thus map the user's environment. By exploiting existing online Semantic Web sources about these detected entities, mobile applications can further improve personalization. SCOUT is a mobile application framework that supports linking physical entities to online semantic data sources. It provides applications with an integrated, query-able view on these sources and the user's environment. The authors developed a tailored data management approach to efficiently access these distributed online semantic sources.Sven Casteleyn is supported by EC Marie Curie grant FP7- PEOPLE-2009-IEF, number 254383.Van Woensel, W.; Casteleyn, S.; Paret, E.; De Troyer, O. (2011). Supporting the Mobile Querying of Existing Online Semantic Web Data for Context-Aware Applications. IEEE Internet Computing. 15(6):32-39. https://doi.org/10.1109/MIC.2011.108323915

An Approach of Semantic Similarity Measure between Documents Based on Big Data

Semantic indexing and document similarity is an important information retrieval system problem in Big Data with broad applications. In this paper, we investigate MapReduce programming model as a specific framework for managing distributed processing in a large of amount documents. Then we study the state of the art of different approaches for computing the similarity of documents. Finally, we propose our approach of semantic similarity measures using WordNet as an external network semantic resource. For evaluation, we compare the proposed approach with other approaches previously presented by using our new MapReduce algorithm. Experimental results review that our proposed approach outperforms the state of the art ones on running time performance and increases the measurement of semantic similarity

Distributed Semantic Web data management in HBase and MySQL cluster

Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C\u27s Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management