Architecture of Linked Data Applications

Heitmann, Benjamin; Cyganiak, Richard; Hayes, Conor; Decker, Stefan

doi:10.1201/b16859-5

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…Two early methods developed for privacy protection when answering queries about classes in a dataset are k-anonymity and l-diversity. In particular, kanonymity is used in [17,31] to answer queries in RDF datasets. Unfortunately, it is well-known these methods, in contrast to differential privacy, do not provide formal guarantees for privacy.…”

Section: Privacy Over Sparqlmentioning

confidence: 99%

Differential privacy and SPARQL

Buil-Aranda,

Lobo,

Olmedo

2024

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Differential privacy is a framework that provides formal tools to develop algorithms to access databases and answer statistical queries with quantifiable accuracy and privacy guarantees. The notions of differential privacy are defined independently of the data model and the query language at steak. Most differential privacy results have been obtained on aggregation queries such as counting or finding maximum or average values, and on grouping queries over aggregations such as the creation of histograms. So far, the data model used by the framework research has typically been the relational model and the query language SQL. However, effective realizations of differential privacy for SQL queries that required joins had been limited. This has imposed severe restrictions on applying differential privacy in RDF knowledge graphs and SPARQL queries. By the simple nature of RDF data, most useful queries accessing RDF graphs will require intensive use of joins. Recently, new differential privacy techniques have been developed that can be applied to many types of joins in SQL with reasonable results. This opened the question of whether these new results carry over to RDF and SPARQL. In this paper we provide a positive answer to this question by presenting an algorithm that can answer counting queries over a large class of SPARQL queries that guarantees differential privacy, if the RDF graph is accompanied with semantic information about its structure. We have implemented our algorithm and conducted several experiments, showing the feasibility of our approach for large graph databases. Our aim has been to present an approach that can be used as a stepping stone towards extensions and other realizations of differential privacy for SPARQL and RDF.

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Section: Privacy Over Sparqlmentioning

confidence: 99%

Differential privacy and SPARQL

Buil-Aranda,

Lobo,

Olmedo

2024

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“…Web in machine-understandable format (primarily RDF [1]) related to different domains. The LOD has been adopted in recommender systems in order to improve the performance of such systems as well as reduce the cold-start problem inherent to recommender systems [2,8,[12][13][14]. Also, the need for a semantic representation of data and user profiles has been identified as one of the next challenges in the field of recommender systems [11].…”

Section: Introductionmentioning

confidence: 99%

Measuring semantic distance for linked open data-enabled recommender systems

Piao

Breslin

2016

Proceedings of the 31st Annual ACM Symposium on Applied Computing

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The Linked Open Data (LOD) initiative has been quite successful in terms of publishing and interlinking data on the Web. On top of the huge amount of interconnected data, measuring relatedness between resources and identifying their relatedness could be used for various applications such as LOD-enabled recommender systems. In this paper, we propose various distance measures, on top of the basic concept of Linked Data Semantic Distance (LDSD), for calculating Linked Data semantic distance between resources that can be used in a LOD-enabled recommender system. We evaluated the distance measures in the context of a recommender system that provides the top-N recommendations with baseline methods such as LDSD. Results show that the performance is significantly improved by our proposed distance measures incorporating normalizations that use both of the resources and global appearances of paths in a graph.

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Interoperability of Representations

Brodaric

2017

International Encyclopedia of Geography

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The rapid growth of distributed geographical data and software has fueled development of interoperability approaches that enable such resources to function together for specific purposes. The problem at the heart of these approaches involves overcoming heterogeneity in the digital representation of geographical entities and related computing processes. Solutions uniformly include conversion to a preferred representation through transformation at various levels, such as systems, syntax, structure, semantics, and pragmatics. Major milestones include spatial database interoperability, Spatial Data Infrastructures, and Linked Data, which are characterized by the adoption of unique strategies at each interoperability level and by distinct architectural styles.

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Architecture of Linked Data Applications

Cited by 7 publications

References 21 publications

Differential privacy and SPARQL

Differential privacy and SPARQL

Measuring semantic distance for linked open data-enabled recommender systems

Interoperability of Representations

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