1,944 research outputs found
Managing and Consuming Completeness Information for RDF Data Sources
The ever increasing amount of Semantic Web data gives rise to the question: How complete is the data? Though generally data on the Semantic Web is incomplete, many parts of data are indeed complete, such as the children of Barack Obama and the crew of Apollo 11. This thesis aims to study how to manage and consume completeness information about Semantic Web data. In particular, we first discuss how completeness information can guarantee the completeness of query answering. Next, we propose optimization techniques of completeness reasoning and conduct experimental evaluations to show the feasibility of our approaches. We also provide a technique to check the soundness of queries with negation via reduction to query completeness checking. We further enrich completeness information with timestamps, enabling query answers to be checked up to when they are complete. We then introduce two demonstrators, i.e., CORNER and COOL-WD, to show how our completeness framework can be realized. Finally, we investigate an automated method to generate completeness statements from text on the Web via relation cardinality extraction
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
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)
Publishing and Consuming Semantic Views for Construction of Knowledge Graphs
The main goal of semantic integration is to provide a virtual semantic view that is semantically connected to
data so that applications can have integrated access to data sources through the virtual Knowledge Graph. A
semantic view can be published on a semantic portal to make it reusable for building Knowledge Graphs for
different applications. This paper takes the first step towards publishing a semantic view on a semantic portal.
This paper has three main contributions. First, we introduce a vocabulary for specifying semantic views. Then,
we introduce a vocabulary for specification and quality assessment of Knowledge Graph. Third, we describe
an approach to automatize the construction of a high-quality Knowledge Graph reusing a semantic view
Completeness and Consistency Analysis for Evolving Knowledge Bases
Assessing the quality of an evolving knowledge base is a challenging task as
it often requires to identify correct quality assessment procedures.
Since data is often derived from autonomous, and increasingly large data
sources, it is impractical to manually curate the data, and challenging to
continuously and automatically assess their quality.
In this paper, we explore two main areas of quality assessment related to
evolving knowledge bases: (i) identification of completeness issues using
knowledge base evolution analysis, and (ii) identification of consistency
issues based on integrity constraints, such as minimum and maximum cardinality,
and range constraints.
For completeness analysis, we use data profiling information from consecutive
knowledge base releases to estimate completeness measures that allow predicting
quality issues. Then, we perform consistency checks to validate the results of
the completeness analysis using integrity constraints and learning models.
The approach has been tested both quantitatively and qualitatively by using a
subset of datasets from both DBpedia and 3cixty knowledge bases. The
performance of the approach is evaluated using precision, recall, and F1 score.
From completeness analysis, we observe a 94% precision for the English DBpedia
KB and 95% precision for the 3cixty Nice KB. We also assessed the performance
of our consistency analysis by using five learning models over three sub-tasks,
namely minimum cardinality, maximum cardinality, and range constraint. We
observed that the best performing model in our experimental setup is the Random
Forest, reaching an F1 score greater than 90% for minimum and maximum
cardinality and 84% for range constraints.Comment: Accepted for Journal of Web Semantic
Federated Query Processing over Heterogeneous Data Sources in a Semantic Data Lake
Data provides the basis for emerging scientific and interdisciplinary data-centric applications with the potential of improving the quality of life for citizens. Big Data plays an important role in promoting both manufacturing and scientific development through industrial digitization and emerging interdisciplinary research. Open data initiatives have encouraged the publication of Big Data by exploiting the decentralized nature of the Web, allowing for the availability of heterogeneous data generated and maintained by autonomous data providers. Consequently, the growing volume of data consumed by different applications raise the need for effective data integration approaches able to process a large volume of data that is represented in different format, schema and model, which may also include sensitive data, e.g., financial transactions, medical procedures, or personal data. Data Lakes are composed of heterogeneous data sources in their original format, that reduce the overhead of materialized data integration. Query processing over Data Lakes require the semantic description of data collected from heterogeneous data sources. A Data Lake with such semantic annotations is referred to as a Semantic Data Lake. Transforming Big Data into actionable knowledge demands novel and scalable techniques for enabling not only Big Data ingestion and curation to the Semantic Data Lake, but also for efficient large-scale semantic data integration, exploration, and discovery. Federated query processing techniques utilize source descriptions to find relevant data sources and find efficient execution plan that minimize the total execution time and maximize the completeness of answers. Existing federated query processing engines employ a coarse-grained description model where the semantics encoded in data sources are ignored. Such descriptions may lead to the erroneous selection of data sources for a query and unnecessary retrieval of data, affecting thus the performance of query processing engine. In this thesis, we address the problem of federated query processing against heterogeneous data sources in a Semantic Data Lake. First, we tackle the challenge of knowledge representation and propose a novel source description model, RDF Molecule Templates, that describe knowledge available in a Semantic Data Lake. RDF Molecule Templates (RDF-MTs) describes data sources in terms of an abstract description of entities belonging to the same semantic concept. Then, we propose a technique for data source selection and query decomposition, the MULDER approach, and query planning and optimization techniques, Ontario, that exploit the characteristics of heterogeneous data sources described using RDF-MTs and provide a uniform access to heterogeneous data sources. We then address the challenge of enforcing privacy and access control requirements imposed by data providers. We introduce a privacy-aware federated query technique, BOUNCER, able to enforce privacy and access control regulations during query processing over data sources in a Semantic Data Lake. In particular, BOUNCER exploits RDF-MTs based source descriptions in order to express privacy and access control policies as well as their automatic enforcement during source selection, query decomposition, and planning. Furthermore, BOUNCER implements query decomposition and optimization techniques able to identify query plans over data sources that not only contain the relevant entities to answer a query, but also are regulated by policies that allow for accessing these relevant entities. Finally, we tackle the problem of interest based update propagation and co-evolution of data sources. We present a novel approach for interest-based RDF update propagation that consistently maintains a full or partial replication of large datasets and deal with co-evolution
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