ActiveRDF: Embedding Semantic Web data into object-oriented languages

Oren, Eyal; Heitmann, Benjamin; Decker, Stefan

doi:10.1016/j.websem.2008.04.003

Cited by 40 publications

(16 citation statements)

References 19 publications

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“…Oren et al [39] describes the ActiveRDF approach for mapping between object oriented programming languages and the RDF data model. Another more recent approach to map between object oriented porgramming languages and the RDF data model is the Object triple mapping (OTM) described by Quasthoff et al [42].…”

Section: Graph Access Layermentioning

confidence: 99%

“…Accessing RDF data from a component requires mapping of an RDF graph to the data model used by the component [39].…”

Section: Mismatch Of Data Models Between Componentsmentioning

confidence: 99%

“…The graph-based navigation interface allows faceted browsing of the SIOC data and is implemented through the BrowseRDF Ruby component [38]. The graph access layer is provided by ActiveRDF [39], which is an object-oriented Ruby API for accessing RDF data. The data homogenisation service is implemented in two steps: (1) generic object consolidation is provided by the OWLIM extension for Sesame 11 , written in Java, and accessed via SPARQL, and (2) domain specific integration of SIOC data is implemented as Ruby code.…”

Section: Analysis Of An Example Applicationmentioning

confidence: 99%

“…Several such libraries are currently being developed such as ActiveRDF, which maps the native data model of a programming language to RDF [39], and the SIOC PHP API 14 for implementing access to SIOC data. Best practices for converting relational databases to RDF are implemented in the D2R server 15 , while any23 16 implements best practices for conversion of many structured data formats to RDF.…”

Section: More Guidelines Best Practices and Design Patternsmentioning

confidence: 99%

See 3 more Smart Citations

Architecture of Linked Data Applications

Heitmann¹,

Cyganiak²,

Hayes³

et al. 2014

Emerging Directions in Database Systems and Applications

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Section: Graph Access Layermentioning

confidence: 99%

“…Accessing RDF data from a component requires mapping of an RDF graph to the data model used by the component [39].…”

Section: Mismatch Of Data Models Between Componentsmentioning

confidence: 99%

Section: Analysis Of An Example Applicationmentioning

confidence: 99%

Section: More Guidelines Best Practices and Design Patternsmentioning

confidence: 99%

See 2 more Smart Citations

Architecture of Linked Data Applications

Heitmann¹,

Cyganiak²,

Hayes³

et al. 2014

Emerging Directions in Database Systems and Applications

Self Cite

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“…[Oren et al, 2008] describes the ActiveRDF approach for mapping between object oriented programming languages and the RDF data model. Another more recent approach to map between object oriented porgramming languages and the RDF data model is the Object triple mapping (OTM) described by [Quasthoff & Meinel, 2012].…”

Section: Graph Access Layermentioning

confidence: 99%

An open framework for multi-source, cross-domain personalisation with semantic interest graphs

Heitmann

2012

Proceedings of the Sixth ACM Conference on Recommender Systems

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The work in this thesis addresses new challenges and opportunities for online personalisation posed by the emergence of new infrastructures for sharing user preferences and for access to open repositories of data. As a result of these new infrastructures, user profiles can now include data from multiple sources about preferences in multiple domains. This new kind of user profile data requires a cross-domain personalisation approach. However, current cross-domain personalisation approaches are restricted to proprietary social networking ecosystems.The main problem that we address in this thesis, is to enable cross-domain recommendations without the use of a proprietary and closed infrastructure. Towards this goal, we propose an open framework for cross-domain personalisation. Our framework consists of two parts: a conceptual architecture for recommender systems, and our cross-domain personalisation approach. The main enabling technology for our framework is Linked Open Data, as it provides a common data presentation for user preferences and cross-domain links between concepts from many different domains.As part of our framework, we first propose a conceptual architecture for Linked Open Data recommender systems that provides guidelines and best practices for the typical high level components required for providing personalisation in open ecosystems using Linked Open Data. The architecture has a strong empirical founding, as it based on an empirical survey of 124 RDF-based applications.Then we introduce and thoroughly evaluate SemStim, an unsupervised, graph-based algorithm for cross-domain personalisation. It leverages multi-source, domain-neutral user profiles and the semantic network of DBpedia in order to generate recommendations for different source and target domains.The results of our evaluation show that SemStim is able to provide cross-domain recommendations, without any overlap between target and source domains and without using any ratings in the target domain.We show how we instantiate our proposed conceptual architecture for a prototype implementation that is the outcome of the ADVANSSE collaboration project with CISCO Galway. The prototype shows how to implement our framework for a real-world use case and data.Our open framework for cross-domain personalisation provides an alternative to existing proprietary cross-domain personalisation approaches. As such, it opens up the potential for novel and innovative personalised services without the risk of user lock-in and data silos.iv

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