Manuel Salvadores

SPARQL query rewriting for implementing data integration over linked data

There has been lately an increased activity of publishing structured data in RDF due to the activity of the Linked Data community. The presence on the Web of such a huge information cloud, ranging from academic to geographic to gene related information, poses a great challenge when it comes to reconcile heterogeneous schemas adopted by data publishers. For several years, the Semantic Web community has been developing algorithms for aligning data models (ontologies). Nevertheless, exploiting such ontology alignments for achieving data integration is still an under supported research topic. The semantics of ontology alignments, often defined over a logical frameworks, implies a reasoning step over huge amounts of data, that is often hard to implement and rarely scales on Web dimensions. This paper presents an algorithm for achieving RDF data mediation based on SPARQL query rewriting. The approach is based on the encoding of rewriting rules for RDF patterns that constitute part of the structure of a SPARQL query.

Put in your postcode, out comes the data: a case study

Article describes the UK Open Government Data project which the two authors have been leading and the planned launch of data.gov.uk a single point of access for all public non-personal government datasets. It outlines the benefits that will flow from more accessible and open data. The article first appeared in The Times 18th Nov 2009 http://www.thetimes.co.uk/tto/law/columnists/article2049543.ece

The design and implementation of minimal RDFS backward reasoning in 4store

This paper describes the design and implementation of Minimal RDFS semantics based on a backward chaining approach and implemented on a clustered RDF triple store. The system presented, called 4sr, uses 4store as base infrastructure. In order to achieve a highly scalable system we implemented the reasoning at the lowest level of the quad store, the bind operation. The bind operation runs concurrently in all the data slices allowing the reasoning to be processed in parallel among the cluster. Throughout this paper we provide detailed descriptions of the architecture, reasoning algorithms, and a scalability evaluation with the LUBM benchmark. 4sr is a stable tool available under a GNU GPL3 license and can be freely used and extended by the community1.

Managing dynamic virtual organizations to get effective cooperation in collaborative grid environments

This paper presents how to manage Virtual Organizations to enable efficient collaboration and/or cooperation as a result of a flexible and parametrical model. The CAM (Collaborative/Cooperative Awareness Management) model promotes collaboration around resources-sharing infrastructures, endorsing interaction by means of a set of rules. This model focuses on responding to specific demanding circumstances at a given moment, while optimizes resources communication and behavioural agility to get a common goal: the establishment of collaborative dynamic virtual organizations. This paper also describes how CAM works in some specific examples and scenarios, and how the CAM Rules-Based Management Application (based on Web Services and named WS-CAM) has been designed and validated to encourage resources to be involved in collaborative performances, tackling efficiently demanding situations without hindering the own purposes of each of these resources.

A rule based resources management for collaborative grid environments

Something that is still missing, but strongly needed, in collaborative grid environments is a stable, flexible and dynamic resource management. This management should optimise collaboration and cooperation among several resources keeping resources constraints, preconditions and rules. This paper presents how to achieve these objectives, by means of a Collaborative Awareness Management (CAM) model. CAM optimises resources collaboration, promotes resources cooperation and responds to the specific demanded circumstances. This paper also describes how this model works in some specific examples and scenarios, emphasising on how the WS-CAM Rules-Based Management Application has been designed, implemented, and validated to accomplish these purposes.

4s-reasoner: RDFS Backward Chained Reasoning Support in 4store

This paper describes the design and implementation of backward chained clustered RDFS reasoning in 4store. The system presented, called “4s-reasoner”, adds no overhead to the import phase and yet performs reasonably well at the query phase. We also demonstrate that our solution scales over clusters of commodity servers providing an optimal solution that balances infrastructure cost and performance over tested data sets with up to 500M triples. In addition we have shared our implementation under GNU license and a first release is available to be used by the community.

LinksB2N: Automatic Data Integration for the Semantic Web

The ongoing trend towards open data embraced by the Semantic Web has started to produce a large number of data sources. These data sources are published using RDF vocabularies, and it is possible to navigate throughout the data due to their graph topology. This paper presents LinksB2N, an algorithm for discovering information overlaps in RDF data repositories and performing data integration with no human intervention over data sets that partially share the same domain. LinksB2N identifies equivalent RDF resources from different data sets with several degrees of confidence. The algorithm relies on a novel approach that uses clustering techniques to analyze the distribution of unique objects that contain overlapping information in different data graphs. Our contribution is illustrated in the context of the Market Blended Insight project by applying the LinksB2N algorithm to data sets in the order of hundreds of millions of RDF triples containing relevant information in the domain of business to business (B2B) marketing analysis.

AMBLE: An Awareness Model for Balancing the Load in collaborative grid Environments

In this paper, we present a new extension and reinterpretation of one of the most successful models of awareness in computer supported cooperative work (CSCW), called the spatial model of interaction (SMI), which manage awareness of interaction through a set of key concepts, to manage task delivery in collaborative distributed systems. This model also applies some theoretical principles and theories of multi-agents systems to create a collaborative and cooperative environment that can be able to provide an autonomous, efficient and independent management of the amount of resources available in a grid environment. This model has been implemented using Web services and some experimental results carried out over a real and heterogeneous grid are presented with the end of emphasizing the performance speedup of the system using the AMBLE model

Distributed human computation framework for linked data co-reference resolution

Distributed Human Computation (DHC) is 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 has many research problems that are considered as AI-complete. E.g. co-reference resolution, which involves determining whether different URIs refer to the same entity, is 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 when integrating distributed datasets. Traditionally machine-learning algorithms are used as a solution for this but 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 coreference 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 dereferenceable in the Open Linked Data Cloud.

Domain-Specific Backlinking Services in the Web of Data

This paper describes an Open Linked Data backlinking service, a generic architecture component to support the discovery of useful links between items across highly connected data sets. Using Public Sector Information (PSI) currently available as Linked Data, we demonstrate that contemporary publishing practices do not adequately support the ability to navigate or automatically traverse between resources published by different vendors, or the capacity to discover information relevant to a particular URI. Although some useful services in this area have been developed, such as large triple indexes of published data, and the collection of same. As relationships between individuals, we believe that an important component is missing: a mechanism to discover the backlinks to relevant resources that cannot be found by direct URI resolution. We present the implementation of such a component, integrating data from various PSI sources.