Paragraph Tables: A Storage Scheme Based on RDF Document Structure

Matono, Akiyoshi; Kojima, Isao

doi:10.1007/978-3-642-32597-7_21

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…RDF data management typically involves scalable storage and efficient queries of RDF data, and RDF data storage is the infrastructure of RDF data management. Currently, a number of research efforts have proposed multiple mechanisms for RDF data storage, including triple store , , column store , , property table store , , graph‐based store , and NoSQL (for “ not only SQL ”) databases in cloud environment . Some research prototypes (e.g., SW‐Store, RDF‐3X, MonetDB, and Hexastore) are correspondingly constructed.…”

Section: Related Workmentioning

confidence: 99%

Modeling fuzzy data with RDF and fuzzy relational database models

2018

Int. J. Intell. Syst.

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The Resource Description Framework (RDF) is a flexible model for representing information about resources on the Web. As a World Wide Web Consortium recommendation, RDF has rapidly gained popularity. With the widespread acceptance of RDF on the Web and in the enterprise, a huge amount of RDF data is being proliferated and becoming available. In real‐world applications, there are many practical situations where the descriptions of objects are accompanied by a degree of uncertainty. In an open Web environment, it is common case that the data from different sources may often contain inconsistent or imprecise information. In this paper, a fuzzy RDF data model is proposed. We present the syntax and semantics of fuzzy RDF data model. In particular, we investigate in the paper how to formally map fuzzy RDF data to relational databases.

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Section: Related Workmentioning

confidence: 99%

Modeling fuzzy data with RDF and fuzzy relational database models

2018

Int. J. Intell. Syst.

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“…The third approach for storing RDF data is called property stores (e.g. Matono et al ., 2005; Sintek & Kiesel, 2006; Levandoski & Mokbel, 2009), in which one relational table is created for each RDF data type and a relational table contains the properties as n -ary table columns for the same subject. Actually, property stores are type-oriented stores (Bornea et al ., 2013).…”

Section: Resource Description Framework Stores In Traditional Databasesmentioning

confidence: 99%

“…Each triple in the form ( subject , predicate , object ) is classified into categories by Matono et al . (2005) according to the type of predicate, and then subgraphs are constructed for each category. The graph is decomposed into five subgraphs according to the type of predicate: class inheritance graphs, property inheritance graphs, type graphs, domain-range graphs, and generic graphs.…”

Section: Resource Description Framework Stores In Traditional Databasesmentioning

confidence: 99%

“…Each extracted item is assigned an identifier and a path expression and stored in the corresponding relational table. Because the proposed scheme retains schema information and path expressions for each resource, the path-based relational RDF database (Matono et al ., 2005) can process path-based queries efficiently and store RDF instance data without schema information.…”

Section: Resource Description Framework Stores In Traditional Databasesmentioning

confidence: 99%

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Storing massive Resource Description Framework (RDF) data: a survey

Capretz

2016

The Knowledge Engineering Review

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The Resource Description Framework (RDF) is a flexible model for representing information about resources on the Web. As a W3C (World Wide Web Consortium) Recommendation, RDF has rapidly gained popularity. With the widespread acceptance of RDF on the Web and in the enterprise, a huge amount of RDF data is being proliferated and becoming available. Efficient and scalable management of RDF data is therefore of increasing importance. RDF data management has attracted attention in the database and Semantic Web communities. Much work has been devoted to proposing different solutions to store RDF data efficiently. This paper focusses on using relational databases and NoSQL (for ‘not only SQL (Structured Query Language)’) databases to store massive RDF data. A full up-to-date overview of the current state of the art in RDF data storage is provided in the paper.

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“…Recently, Matono and Kojima [12] construct so-called paragraph tables which are similar to property tables from adjacent RDF triples that are physically correlated. However, this method relies on well-structured input RDF documents and the parse order of RDF triples.…”

Section: Related Workmentioning

confidence: 99%

Self-organizing structured RDF in MonetDB

Pham

2013

2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW)

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The semantic web uses RDF as its data model, providing ultimate flexibility for users to represent and evolve data without need of a schema. Yet, this flexibility poses challenges in implementing efficient RDF stores, leading from plans with very many self-joins to a triple table, difficulties to optimize these, and a lack of data locality since without a notion of multiattribute data structure, clustered indexing opportunities are lost. Apart from performance issues, users of huge RDF graphs often have problems formulating queries as they lack any systemsupported notion of the structure in the data. In this research, we exploit the observation that real RDF data, while not as regularly structured as relational data, still has the great majority of triples conforming to regular patterns. We conjecture that a system that would recognize this structure automatically would both allow RDF stores to become more efficient and also easier to use. Concretely, we propose to derive self-organizing RDF that stores data in PSO format in such a way that the regular parts of the data physically correspond to relational columnar storage; and propose RDFscan/RDFjoin algorithms that compute starpatterns over these without wasting effort in self-joins. These regular parts, i.e. tables, are identified on ingestion by a schema discovery algorithm-as such users will gain an SQL view of the regular part of the RDF data. This research aims to produce a state-of-the-art SPARQL frontend for MonetDB as a by-product, and we already present some preliminary results on this platform.

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Paragraph Tables: A Storage Scheme Based on RDF Document Structure

Cited by 6 publications

References 6 publications

Modeling fuzzy data with RDF and fuzzy relational database models

Modeling fuzzy data with RDF and fuzzy relational database models

Storing massive Resource Description Framework (RDF) data: a survey

Self-organizing structured RDF in MonetDB

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