Difference between revisions of "PapersQ4"

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We found the following papers:
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Recommend papers, including paper title, abstract, conclusion, and future work, addressing the problem of Transforming Relational Databases into Semantic Web published in an A or A* conferences along with authors names.
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We recommend the following papers:
 
   
 
   
 
{{#ask: [[Category:Paper]] [[Has problem::Transforming Relational Databases into Semantic Web]] [[Event in series::<q> [[Category:Event series]] [[Has CORE2017 Rank::A*||A]] </q>]]
 
{{#ask: [[Category:Paper]] [[Has problem::Transforming Relational Databases into Semantic Web]] [[Event in series::<q> [[Category:Event series]] [[Has CORE2017 Rank::A*||A]] </q>]]

Latest revision as of 07:36, 13 July 2018

Recommend papers, including paper title, abstract, conclusion, and future work, addressing the problem of Transforming Relational Databases into Semantic Web published in an A or A* conferences along with authors names.


We recommend the following papers:

PaperAuthorsVenueyearabstractconclusionfuture work
D2RQ – Treating Non-RDF Databases as Virtual RDF GraphsChristian Bizer
Andy Seaborne
ISWC2004As Semantic Web technologies are getting mature, there is a growing need for RDF applications to access the content of huge, live, non-RDF, legacy databases without having to replicate the whole database into RDF. In this poster we present D2RQ, a declarative language to describe mappings between application-specific relational database schemata and RDF-S/OWL ontologies. D2RQ allows RDF applications to treat non-RDF relational data bases as virtual RDF graphs, which can be queried using RDQL.D2RQ offers a flexible, easy-to-use access mechanism to non-RDF databases. It allows the integration of legacy databases into the data access architecture currently standardized by the W3C Data Access Working GroupNo future work exists.
Updating Relational Data via SPARQL/UpdateMatthias Hert
Gerald Reif
Harald C. Gall
EDBT2010Relational Databases are used in most current enterprise environments to store and manage data. The semantics of the data is not explicitly encoded in the relational model, but implicitly on the application level. Ontologies and Semantic Web technologies provide explicit semantics that allows data to be shared and reused across application, enterprise, and community boundaries. Converting all relational data to RDF is often not feasible, therefore we adopt an ontology-based access to relational databases. While existing approaches focus on read-only access, we present our approach OntoAccess that adds ontology-based write access to relational data. OntoAccess consists of the update-aware RDB to RDF mapping language R3M and algorithms for translating SPARQL/Update operations to SQL. This paper presents the mapping language, the translation algorithms, and a prototype implementation of OntoAccess.In this paper, we presented our approach OntoAccess that enables the manipulation of relational data via SPARQL/Update. We introduced the update-aware RDB to RDF mapping language R3M that captures additional information about the database schema, in particular about integrity constraints. This information enables the detection of update requests that are invalid from the RDB perspective. Such requests cannot be executed by the database engine as they would violate integrity constraints of the database schema. The information can also be exploited to provide semantically rich feedback to the client. Therefore, the causes for the rejection of a request and possible directions for improvement can be reported in an appropriate format.Future work is planned for various aspects of OntoAccess. Further research needs to be done on bridging the conceptual gap between RDBs and the Semantic Web. Ontology-

based write access to the relational data creates completely new challenges on this topic with respect to read-only approaches. The presence of schema constraints in the database can lead to the rejection of update requests that would otherwise be accepted by a native triple store. A feedback protocol that provides semantically rich information about the cause of a rejection and possible directions for improvement plays a major role in bridging the gap. Other database constraints such as assertions have to be evaluated as well to see

if they can reasonably be supported in the mapping. Also, a more formal definition of the mapping language will be provided. Furthermore, we will extend our prototype implementation to support the SPARQL/Update MODIFY operation, SPARQL queries, and the just mentioned feedback protocol.
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