DLDB: Extending Relational Databases to Support Semantic Web Queries

Pan, Zhengxiang; Heflin, Jeff

doi:10.21236/ada451847

Cited by 143 publications

(111 citation statements)

References 6 publications

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“…Most DB-based work such as [4][5][6][7] rely on relational database engines for indexing and querying. The primary database schema used for storing triples is a vertical schema with various optimizations.…”

Section: Related Workmentioning

confidence: 99%

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Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

et al. 2007

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Abstract. As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we breifly describe how Semplore is used for searching Wikipedia and an IBM customer's product information.

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Section: Related Workmentioning

confidence: 99%

“…[1][2][3]) or a DB-based (e.g. [4][5][6][7]) approach. The IR-based work does not provide structured query capability and the DB-based work lacks support to keyword searches.…”

Section: Introductionmentioning

confidence: 99%

Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

et al. 2007

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“…Nevertheless, most of these approaches do not use relational databases as a data source, but to store RDF triples in tailored tables, exploting the improved query performance of current relational databases (e.g. [13], [16], or [11]). The main drawback of such approaches is, that the corresponding data has to be available in RDF, i.e.…”

Section: Related Workmentioning

confidence: 99%

Database to Semantic Web Mapping Using RDF Query Languages

Laborda

Conrad

2006

Conceptual Modeling - ER 2006

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Abstract. One of the main drawbacks of the Semantic Web is the lack of semantically rich data, since most of the information is still stored in relational databases. In this paper, we present an approach to map legacy data stored in relational databases into the Semantic Web using virtually any modern RDF query language, as long as it is closed within RDF. Consequently, a Semantic Web developer does not need to learn and adopt a new mapping language, but he may perform the mapping task using his preferred RDF query language. MotivationDespite the vision of a Semantic Web [6] and many efforts helping to realize it, the actual Semantic Web still lacks of enough semantic data. Most information is still modeled and stored in relational databases and thus out of reach for many Semantic Web applications. As a consequence, such applications need to create a corresponding mapping between the relational and the semantic models by themselves for being able to access relational data. Realizing this situation, some efforts have arisen to straighten out this deplorable situation. Most approaches translate relational data into a Semantic Web representation using a proprietary mapping language (cf. Section 2). In [19] we have introduced Relational.OWL, our technique to automatically transform relational data into a machine processable and understandable representation (cf. Section 3.1). Nevertheless, such a representation does not include real semantics, since it converts the schema of a database automatically into an ontology and the data items as its instances, i.e. the data is described as it was in the database. For many Semantic Web applications, this is a reasonable technique, since they are able to quickly access legacy data stored in a relational database using their own built-in functionality. However, such a representation could be inappropriate, if the data has to be processed for further reasoning tasks. In this paper we present how to map relational data into the Semantic Web using virtually any modern RDF query language, as long as the language is closed within RDF, i.e. it returns valid RDF graphs as query results. For this purpose, data and schema components of the original relational database are

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“…Our motivation of using the mature relational database technology is provided by the fact that provenance metadata growth rate is potentially very high since provenance is generated automatically for every scientific experiment. On the Semantic Web, large volumes of RDF data are managed with the so called RDF stores, and majority of them, including Jena [118,119], Sesame [23], 3store [56,57], KAON [107], RStar [71], OpenLink Virtuoso [42], DLDB [81], RDFSuite [9,105], DBOWL [77], PARKA [101], and RDFBroker [100], use an RDBMS as a backend to manage RDF data. Although a general-purpose relational RDF store (see [15] for a survey) can be used for provenance metadata management, the following provenance-specific requirements bring about several optimization strategies for schema design, data mapping, and query mapping, enabling us to develop a provenance metadata management system that is more efficient and flexible than one that is simply based on an existing RDF store.…”

Section: Introductionmentioning

confidence: 99%

RDFProv: A relational RDF store for querying and managing scientific workflow provenance

Chebotko¹,

Lu²,

Fei³

et al. 2010

Data & Knowledge Engineering

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Provenance metadata has become increasingly important to support scientific discovery reproducibility, result interpretation, and problem diagnosis in scientific workflow environments. The provenance management problem concerns the efficiency and effectiveness of the modeling, recording, representation, integration, storage, and querying of provenance metadata. Our approach to provenance management seamlessly integrates the interoperability, extensibility, and inference advantages of Semantic Web technologies with the storage and querying power of an RDBMS to meet the emerging requirements of scientific workflow provenance management. In this paper, we elaborate on the design of a relational RDF store, called RDFPROV, which is optimized for scientific workflow provenance querying and management. Specifically, we propose: i) two schema mapping algorithms to map an OWL provenance ontology to a relational database schema that is optimized for common provenance queries; ii) three efficient data mapping algorithms to map provenance RDF metadata to relational data according to the generated relational database schema, and iii) a schema-independent SPARQL-to-SQL translation algorithm that is optimized on-the-fly by using the type information of an instance available from the input provenance ontology and the statistics of the sizes of the tables in the database. Experimental results are presented to show that our algorithms are efficient and scalable. The comparison with two popular relational RDF stores, Jena and Sesame, and two commercial native RDF stores, AllegroGraph and BigOWLIM, showed that our optimizations result in improved performance and scalability for provenance metadata management. Finally, our case study for provenance management in a real-life biological simulation workflow showed the production quality and capability of the RDFPROV system. Although presented in the context of scientific workflow provenance management, many of our proposed techniques apply to general RDF data management as well.

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DLDB: Extending Relational Databases to Support Semantic Web Queries

Cited by 143 publications

References 6 publications

Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

Database to Semantic Web Mapping Using RDF Query Languages

RDFProv: A relational RDF store for querying and managing scientific workflow provenance

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