4,769 research outputs found
Co-evolution of RDF Datasets
Linking Data initiatives have fostered the publication of large number of RDF
datasets in the Linked Open Data (LOD) cloud, as well as the development of
query processing infrastructures to access these data in a federated fashion.
However, different experimental studies have shown that availability of LOD
datasets cannot be always ensured, being RDF data replication required for
envisioning reliable federated query frameworks. Albeit enhancing data
availability, RDF data replication requires synchronization and conflict
resolution when replicas and source datasets are allowed to change data over
time, i.e., co-evolution management needs to be provided to ensure consistency.
In this paper, we tackle the problem of RDF data co-evolution and devise an
approach for conflict resolution during co-evolution of RDF datasets. Our
proposed approach is property-oriented and allows for exploiting semantics
about RDF properties during co-evolution management. The quality of our
approach is empirically evaluated in different scenarios on the DBpedia-live
dataset. Experimental results suggest that proposed proposed techniques have a
positive impact on the quality of data in source datasets and replicas.Comment: 18 pages, 4 figures, Accepted in ICWE, 201
Hypermedia-based discovery for source selection using low-cost linked data interfaces
Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed-even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness
On the emergent Semantic Web and overlooked issues
The emergent Semantic Web, despite being in its infancy, has already received a lotof attention from academia and industry. This resulted in an abundance of prototype systems and discussion most of which are centred around the underlying infrastructure. However, when we critically review the work done to date we realise that there is little discussion with respect to the vision of the Semantic Web. In particular, there is an observed dearth of discussion on how to deliver knowledge sharing in an environment such as the Semantic Web in effective and efficient manners. There are a lot of overlooked issues, associated with agents and trust to hidden assumptions made with respect to knowledge representation and robust reasoning in a distributed environment. These issues could potentially hinder further development if not considered at the early stages of designing Semantic Web systems. In this perspectives paper, we aim to help engineers and practitioners of the Semantic Web by raising awareness of these issues
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On requirements for federated data integration as a compilation process
Data integration problems are commonly viewed as interoperability issues, where the burden of reaching a common ground for exchanging data is distributed across the peers involved in the process. While apparently an effective approach towards standardization and interoperability, it poses a constraint to data providers who, for a variety of reasons, require backwards compatibility with proprietary or non-standard mechanisms. Publishing a holistic data API is one such use case, where a single peer performs most of the integration work in a many-to-one scenario. Incidentally, this is also the base setting of software compilers, whose operational model is comprised of phases that perform analysis, linkage and assembly of source code and generation of intermediate code. There are several analogies with a data integration process, more so with data that live in the Semantic Web, but what requirements would a data provider need to satisfy, for an integrator to be able to query and transform its data effectively, with no further enforcements on the provider? With this paper, we inquire into what practices and essential prerequisites could turn this intuition into a concrete and exploitable vision, within Linked Data and beyond
Theory and Practice of Data Citation
Citations are the cornerstone of knowledge propagation and the primary means
of assessing the quality of research, as well as directing investments in
science. Science is increasingly becoming "data-intensive", where large volumes
of data are collected and analyzed to discover complex patterns through
simulations and experiments, and most scientific reference works have been
replaced by online curated datasets. Yet, given a dataset, there is no
quantitative, consistent and established way of knowing how it has been used
over time, who contributed to its curation, what results have been yielded or
what value it has.
The development of a theory and practice of data citation is fundamental for
considering data as first-class research objects with the same relevance and
centrality of traditional scientific products. Many works in recent years have
discussed data citation from different viewpoints: illustrating why data
citation is needed, defining the principles and outlining recommendations for
data citation systems, and providing computational methods for addressing
specific issues of data citation.
The current panorama is many-faceted and an overall view that brings together
diverse aspects of this topic is still missing. Therefore, this paper aims to
describe the lay of the land for data citation, both from the theoretical (the
why and what) and the practical (the how) angle.Comment: 24 pages, 2 tables, pre-print accepted in Journal of the Association
for Information Science and Technology (JASIST), 201
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The National Transport Data Framework
Report by Professor Peter Landshoff (Cambridge University) and
Professor John Polak (Imperial College London) on a project for
the Department for Transport.
emails: [email protected] [email protected] NTDF is designed to be a resource for data owners to deposit descriptions
into a central catalogue, so that people can search for data and find data
and understand their characteristics. The value of this is to individuals, to
commercial organizations, and to public bodies. For example, services that
provide better information to travellers will help to make their journey
less stressful and persuade them to make more use of public transport.
Transport operators need very diverse information to help them
plan developments to their services: demographic, geographical, economic etc.
And policy makers need a similar range of information to help them decide
how to divide their budget and afterwards to evaluate how valuable it has
been.This work was supported by the Department for Transport (DfT)
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