RDF Resources Research Articles

Integration of Open Educational Resources Using Semantic Platform

Universities around the world have been taking advantage of open educational resources (OERs) for publishing educational material. The teachers and students can access, the published material for teaching, learning and research through institutional repositories. This article proposes the design, construction and validation of the semantic platform (MIIDAS), considering two case studies of higher-level educational institutions (civil and military schools). The MIIDAS Platform integrates educational resources in institutional repositories related to the areas of Computing and Electronics engineering through the incorporation of Semantic Web technologies. The descriptions of resources consider metadata of the OAI-PMH protocol and also add educational metadata. The platform uses RDF resource descriptions, ontologies and the SPARQL query language to improve resource retrieval, in addition to proposing guidelines for the management of educational resources. The results of validation of the platform are presented through the development of evaluation instruments, which suggest possible ways to improve the platform. The article provides the basis for developing platforms that help in spreading open educational resources and allow the construction of semantically enrich data sets.

S-Paths: Set-based visual exploration of linked data driven by semantic paths

Meaningful information about an RDF resource can be obtained not only by looking at its properties, but by putting it in the broader context of similar resources. Classic navigation paradigms on the Web of Data that employ a follow-your-nose strategy fail to provide such context, and put strong emphasis on first-level properties, forcing users to drill down in the graph one step at a time. We introduce the concept of semantic paths: starting from a set of resources, we follow and analyse chains of triples and characterize the sets of values at their end. We investigate a navigation strategy based on aggregation, relying on path characteristics to determine the most readable representation. We implement this approach in S-Paths, a browsing tool for linked datasets that systematically identifies the best rated view on a given resource set, leaving users free to switch to another resource set, or to get a different perspective on the same set by selecting other semantic paths to visualize.

Schema-agnostic SPARQL-driven faceted search benchmark generation

In this work, we present a schema-agnostic faceted browsing benchmark generation framework for RDF data and SPARQL engines. Faceted search is a technique that allows narrowing down sets of information items by applying constraints over their properties, whereas facets correspond to properties of these items. While our work can be used to realise real-world faceted search user interfaces, our focus lies on the construction and benchmarking of faceted search queries over knowledge graphs. The RDF model exhibits several traits that seemingly make it a natural foundation for faceted search: all information items are represented as RDF resources, property values typically already correspond to meaningful semantic classifications, and with SPARQL there is a standard language for uniformly querying instance and schema information.However, although faceted search is ubiquitous today, it is typically not performed on the RDF model directly. Two major sources of concern are the complexity of query generation and the query performance. To overcome the former, our framework comes with an intermediate domain-specific language. Thereby our approach is SPARQL-driven which means that every faceted search information need is intensionally expressed as a single SPARQL query. In regard to the latter, we investigate the possibilities and limits of real-time SPARQL-driven faceted search on contemporary triple stores. We report on our findings by evaluating systems performance and correctness characteristics when executing a benchmark generated using our generation framework.All components, namely the benchmark generator, the benchmark runners and the underlying faceted search framework, are published freely available as open source.

Generating RDF resources from web open data portals

<span>Open data are available from various private and public institutions in different resource formats. There are already great number of open data that are published using open data portals, where datasets and resources are mainly presented in tabular or sheet formats. However, such formats have some barriers with application developments and web standards. One of the web recommenced standards for semantic web application is RDF. There are various research efforts have been focused on presenting open data in RDF formats. However, no framework has transformed tabular open data into RDFs considering the HTML tags and properties of the resources and datasets. Therefore, a methodology is required to generate RDF resources from this type of open data resources. This methodology applies data transformations of open data from a tabular format to RDF files for the Saudi Open Data Portal. The methodology successfully transforms open data resources in sheet format into RDF resources. Recommendations and future work are given to enhance the development of building open data.</span>

The Empusa code generator and its application to GBOL, an extendable ontology for genome annotation

The RDF data model facilitates integration of diverse data available in structured and semi-structured formats. To obtain a coherent RDF graph the chosen ontology must be consistently applied. However, addition of new diverse data causes the ontology to evolve, which could lead to accumulation of unintended erroneous composites. Thus, there is a need for a gate keeping system that compares the intended content described in the ontology with the actual content of the resource. The Empusa code generator facilitates creation of composite RDF resources from disparate sources. Empusa can convert a schema into an associated application programming interface (API), that can be used to perform data consistency checks and generates Markdown documentation to make persistent URLs resolvable. Using Empusa consistency is ensured within and between the ontology and the content of the resource. As an illustration of the potential of Empusa, we present the Genome Biology Ontology Language (GBOL). GBOL uses and extends current ontologies to provide a formal representation of genomic entities, along with their properties, relations and provenance.

RiAiR: A Framework for Sensitive RDFProtection

The Semantic Web and the Linked Open Data (LOD) initiatives promote the integration and combination of RDF data on the Web. In some cases, data need to be analyzed and protected before publication in order to avoid the disclosure of sensitive information. However, existing RDF techniques do not ensure that sensitive information cannot be discovered since all RDF resources are linked in the Semantic Web and the combination of different datasets could produce or disclose unexpected sensitive information. In this context, we propose a framework, called RiAiR, which reduces the complexity of the RDF structure in order to decrease the interaction of the expert user for the classification of RDF data into identifiers, quasi-identifiers, etc. An intersection process suggests disclosure sources that can compromise the data. Moreover, by a generalization method, we decrease the connections among resources to comply with the main objectives of integration and combination of the Semantic Web. Results show a viability and high performance for a scenario where heterogeneous and linked datasets are present.

On the Effect of Geometries Simplification on Geo-spatial Link Discovery

Link discovery is central to the integration and use of data across RDF knowledge bases. Geospatial information is increasingly represented according to the Linked Data principles. Resources within such datasets are described by means of vector geometry, where link discovery approaches have to deal with millions of point sets consisting of billions of points. In this paper, we study the effect of simplifying the resources’ geometries on runtime and F-measure of link discovery approaches. In particular, we evaluate link discovery approaches for computing the point-set distances as well as the topological relations among RDF resources with geospatial representation. The results obtained on two different real datasets suggest that most geospatial link discovery approaches achieve up to 67x speedup using simplification, while the average loss in their F-measure is less than 15%. Our implementation is open-source and available at http://github.com/dice-group/limes.

LOD Explorer: Presenting the Web of Data

The quantity of data published on the Web according to principles of Linked Data is increasing intensely. However, this data is still largely limited to be used up by domain professionals and users who understand Linked Data technologies. Therefore, it is essential to develop tools to enhance intuitive perceptions of Linked Data for lay users. The features of Linked Data point to various challenges for an easy-to-use data presentation. In this paper, Semantic Web and Linked Data technologies are overviewed, challenges to the presentation of Linked Data is stated, and LOD Explorer is presented with the aim of delivering a simple application to discover triplestore resources. Furthermore, to hide the technical challenges behind Linked Data and provide both specialist and non-specialist users, an interactive and effective way to explore RDF resources.

Logical comparison over RDF resources in bio-informatics

Comparison of resources is a frequent task in different bio-informatics applications, including drug-target interaction, drug repositioning and mechanism of action understanding, among others. This paper proposes a general method for the logical comparison of resources modeled in Resource Description Framework and shows its distinguishing features with reference to the comparison of drugs. In particular, the method returns a description of the commonalities between resources, rather than a numerical value estimating their similarity and/or relatedness. The approach is domain-independent and may be flexibly adapted to heterogeneous use cases, according to a process for setting parameters which is completely explicit. The paper also presents an experiment using the dataset Bioportal as knowledge source; the experiment is fully reproducible, thanks to the elicitation of criteria and values for parameter customization.

Defining and computing Least Common Subsumers in RDF

Several application scenarios in the Web of Data share the need to identify the commonalities between a pair of RDF resources. Motivated by such needs, we propose the definition and the computation of Least Common Subsumers (LCSs) in RDF. To this aim, we provide some original and fundamental reformulations, to deal with the peculiarities of RDF. First, we adapt a few definitions from Graph Theory to paths and connectedness in RDF-graphs. Second, we define rootedRDF-graphs (r-graphs), in order to focus on a particular resource inside an RDF-graph. Third, we change the definitions of LCSs originally set up for Description Logics to r-graphs. According to the above reformulations, we investigate the computational properties of LCS in RDF, and find a polynomial-time characterization using a form of graph composition. This result remarkably distinguishes LCSs from Entailment in RDF, which is an NP-complete graph matching problem. We then devise algorithms for computing an LCS. A prototypical implementation works as a proof-of-concept for the whole approach in three application scenarios, and shows usefulness and feasibility of our proposal. Most of our examples are taken directly from real datasets, and are fully replicable thanks to the fact that the choice about which triples are selected for the computation is made explicit and flexible.

Defining and Computing Least Common Subsumers in RDF

Several application scenarios in the Web of Data share the need to identify the commonalities between a pair of RDF resources. Motivated by such needs, we propose the definition and the computation of Least Common Subsumers (LCSs) in RDF. To this aim, we provide some original and fundamental reformulations, to deal with the peculiarities of RDF. First, we adapt a few definitions from Graph Theory to paths and connectedness in RDF-graphs. Second, we define rooted RDF-graphs (rr-graphs), in order to focus on a particular resource inside an RDF-graph. Third, we change the definitions of LCSs originally set up for Description Logics to rr-graphs. According to the above reformulations, we investigate the computational properties of LCS in RDF, and find a polynomial-time characterization using a form of graph composition. This result remarkably distinguishes LCSs from Entailment in RDF, which is an NP-complete graph matching problem. We then devise algorithms for computing an LCS. A prototypical implementation works as a proof-of-concept for the whole approach in three application scenarios, and shows usefulness and feasibility of our proposal. Most of our examples are taken directly from real datasets, and are fully replicable thanks to the fact that the choice about which triples are selected for the computation is made explicit and flexible.

RDF2Graph a tool to recover, understand and validate the ontology of an RDF resource.

BackgroundSemantic web technologies have a tremendous potential for the integration of heterogeneous data sets. Therefore, an increasing number of widely used biological resources are becoming available in the RDF data model. There are however, no tools available that provide structural overviews of these resources. Such structural overviews are essential to efficiently query these resources and to assess their structural integrity and design, thereby strengthening their use and potential.ResultsHere we present RDF2Graph, a tool that automatically recovers the structure of an RDF resource. The generated overview allows to create complex queries on these resources and to structurally validate newly created resources.ConclusionRDF2Graph facilitates the creation of complex queries thereby enabling access to knowledge stored across multiple RDF resources. RDF2Graph facilitates creation of high quality resources and resource descriptions, which in turn increases usability of the semantic web technologies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0038-9) contains supplementary material, which is available to authorized users.

Sherlock: A Semi-automatic Framework for Quiz Generation Using a Hybrid Semantic Similarity Measure.

In this paper, we present a semi-automatic system (Sherlock) for quiz generation using linked data and textual descriptions of RDF resources. Sherlock is distinguished from existing quiz generation systems in its generic framework for domain-independent quiz generation as well as in the ability of controlling the difficulty level of the generated quizzes. Difficulty scaling is non-trivial, and it is fundamentally related to cognitive science. We approach the problem with a new angle by perceiving the level of knowledge difficulty as a similarity measure problem and propose a novel hybrid semantic similarity measure using linked data. Extensive experiments show that the proposed semantic similarity measure outperforms four strong baselines with more than 47 % gain in clustering accuracy. In addition, we discovered in the human quiz test that the model accuracy indeed shows a strong correlation with the pairwise quiz similarity.

PTSLGA: A Provenance Tracking System for Linked Data Generating Application

Tracking provenance of RDF resources is an important task in Linked Data generating applications. It takes on a central function in gathering information as well as workflow. Various Linked Data generating applications have evolved for converting legacy data to RDF resources. These data belong to bibliographic, geographic, government, publications, and cross-domains. However, most of them do not support tracking data and workflow provenance for individual RDF resources. In such cases, it is required for those applications to track, store and disseminate provenance information describing their source data and involved operations. In this article, we introduce an approach for tracking provenance of RDF resources. Provenance information is tracked during the conversion process and it is stored into the triple store. Thereafter, this information is disseminated using provenance URIs. The proposed framework has been analyzed using Harvard Library Bibliographic Datasets. The evaluation has been made on datasets through converting legacy data into RDF and Linked Data with provenance. The outcome has been quiet promising in the sense that it enables data publishers to generate relevant provenance information while taking less time and efforts.

Aligning ontologies with subsumption and equivalence relations in Linked Data

With the profusion of RDF resources and Linked Data, ontology alignment has gained significance in providing highly comprehensive knowledge embedded in disparate sources. Ontology alignment, however, in Linking Open Data (LOD) has traditionally focused more on the instance-level rather than the schema-level. Linked Data supports schema-level alignment, provided that instance-level alignment is already established. Linked Data is a hotbed for instance-based schema alignment, which is considered a better solution for aligning classes with ambiguous or obscure names. This study proposes an instance-based schema alignment algorithm, IUT, which builds a unified taxonomy to discover subsumption and equivalence relations between two classes. A scaling algorithm is also developed that reduces pair-wise similarity computations during the taxonomy construction. The IUT is tested with DBpedia and YAGO2, and compared with two state-of-the-art schema alignment algorithms in light of four alignment tasks with different combinations of the two data sets. The experiment results show that the IUT outperforms the two algorithms in efficiency and effectiveness, and demonstrate the IUT can provide an instance-based schema alignment solution with scalability and high performance, for ontologies containing a large number of instances in LOD.

Using adaptable SPARQL to manipulate and to retrieve domain ontologies: application to social intelligence domain

SPARQL SPARQL Protocol and RDF Query Language is a protocol and a request language which supports semantics of the RDF Resource Description Framework data. Based on previous research works, we noticed different lacks such as a poor flexibility when faced with functional and technical changes. According to first results, we aim to propose, depending on changing context including profile and location, suitable ontologies which can be accessed 'upon request'. However, SPARQL queries are not able to take into account the users' requirements update and their contexts changes as well. Faced to this limitation, we propose to use aspect paradigm to extend SPARQL and to promote adaptable SPARQL queries. Consequently, the ontologies of the project are invoked by adaptable SPARQL queries. We propose a genuine implementation in this paper.

A Semantic Wiki Based on Spatial Hypertext

Spatial Hypertext Wiki (ShyWiki) is a wiki which represents knowledge using notes that are spatially distributed in wiki pages and have visual characteristics such as colour, size, or font type. The use of spatial and visual characteristics in wikis is important to improve human comprehension, creation and organization of knowledge. Another important capability in wikis is to allow machines to process knowledge. Wikis that formally structure knowledge for this purpose are called semantic wikis. This paper describes how ShyWiki can make use of spatial hypertext in order to be a semantic wiki. ShyWiki can represent knowledge at different levels of formality. Users of ShyWiki can annotate the content and represent semantic relations without being experts of semantic web data description languages. The spatial hypertext features make it suitable for users to represent unstructured knowledge and implicit graphic relations among concepts. In addition, semantic web and spatial hypertext features are combined to represent structured knowledge. The semantic web features of ShyWiki improve navigation and publish the wiki knowledge as RDF resources, including the implicit relations that are analyzed using a spatial parser.

Creating voiD descriptions for Web-scale data

When working with large amounts of crawled semantic data as provided by the Billion Triple Challenge (BTC), it is desirable to present the data in a manner best suited for end users. This includes conceiving and presenting explanatory metainformation. The Vocabulary of Interlinked Data (voiD) has been proposed as a means to annotate sets of RDF resources to facilitate not only human understanding, but also query optimization. In this article we introduce tools that automatically generate voiD descriptions for large datasets. Our approach comprises different means to identify (sub)datasets and annotate the derived subsets according to the voiD specification. Due to the complexity of Web-scale Linked Data, all algorithms used for partitioning and augmenting are implemented in a cloud environment utilizing the MapReduce paradigm. We employed the Billion Triple Challenge 2010 dataset [6] to evaluate our approach, and present the results in this article. We have released a tool named voiDgen to the public that allows the generation of metainformation for such large datasets.

Creating voiD Descriptions for Web-scale Data

When working with large amounts of crawled semantic data as provided by the Billion Triple Challenge (BTC), it is desirable to present the data in a manner best suited for end users. This includes conceiving and presenting explanatory metainformation. The Vocabulary of Interlinked Data (voiD) has been proposed as a means to annotate sets of RDF resources to facilitate not only human understanding, but also query optimization. In this article we introduce tools that automatically generate voiD descriptions for large datasets. Our approach comprises different means to identify (sub)datasets and annotate the derived subsets according to the voiD specification. Due to the complexity of Web-scale Linked Data, all algorithms used for partitioning and augmenting are implemented in a cloud environment utilizing the MapReduce paradigm. We employed the Billion Triple Challenge 2010 dataset [6] to evaluate our approach, and present the results in this article. We have released a tool named voiDgen to the public that allows the generation of metainformation for such large datasets.

Efficient Querying of Distributed RDF Sources in Mobile Settings based on a Source Index Model

Recent technological developments in the area of mobile devices in combination with the ubiquity of mobile connectivity have turned handheld devices into rich mobile web clients. On the other hand, we witness an increased number of web resources that are no longer only available in HTML but also in RDF format. In order to access relevant information in a mobile setting, we often want to query a subset of this large amount of heterogeneous RDF resources based on various context factors. We present a semantic technology-based client-side solution for efficient querying of large sets of distributed RDF sources without query endpoints. Our solution continuously extracts metadata from distributed RDF sources and manages this metadata in a local Source Index Model (SIM). When a new query has to be processed, the SIM is consulted to identify, assemble and query only potentially relevant resources. We do not plan to replace existing mobile query engines, but rather build on them to efficiently and transparently query large sets of distributed online RDF sources. The evaluation of our enhanced query handler reveals significant improvements in the overall query execution time based on the proposed SIM.