Mapping Relational Databases (RDB) to RDF is an active field of research. The majority of data on the current Web is stored in RDBs. Therefore, bridging the conceptual gap between the relational model and RDF is needed to make the data available on the Semantic Web. In addition, recent research has shown that Semantic Web technologies are useful beyond the Web, especially if data from different sources has to be exchanged or integrated. Many mapping languages and approaches were explored leading to the ongoing standardization effort of the World Wide Web Consortium (W3C) carried out in the RDB2RDF Working Group (WG). The goal and contribution of this paper is to provide a featurebased comparison of the state-of-the-art RDB-to-RDF mapping languages. It should act as a guide in selecting a RDB-to-RDF mapping language for a given application scenario and its requirements w.r.t. mapping features. Our comparison framework is based on use cases and requirements for mapping RDBs to RDF as identified by the RDB2RDF WG. We apply this comparison framework to the state-of-the-art RDB-to-RDF mapping languages and report the findings in this paper. As a result, our classification proposes four categories of mapping languages: direct mapping, read-only general-purpose mapping, readwrite general-purpose mapping, and special-purpose mapping. We further provide recommendations for selecting a mapping language. WG). The goal and contribution of this paper is to provide a feature-based comparison of the state-of-the-art RDB-to-RDF mapping languages. It should act as a guide in selecting a RDB-to-RDF mapping language for a given application scenario and its requirements w.r.t. mapping features. Our comparison framework is based on use cases and requirements for mapping RDBs to RDF as identified by the RDB2RDF WG. We apply this comparison framework to the state-of-the-art RDB-to-RDF mapping languages and report the findings in this paper. As a result, our classification proposes four categories of mapping languages: direct mapping, read-only general-purpose mapping, readwrite general-purpose mapping, and special-purpose mapping. We further provide recommendations for selecting a mapping language. A Comparison of RDB-to-RDF Mapping Languages
Relational Databases (RDBs) 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 at 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 a mediation approach for ontology-based access to RDBs. Existing mapping approaches focus on read-only access via SPARQL or as Linked Data but other data access interfaces exist, including approaches for updating RDF data. In this paper we present OntoAccess, an extensible platform for ontology-based read and write access to existing relational data. It encapsulates the translation logic in the core layer that provides the foundation of an extensible set of data access interfaces in the interface layer. We further present the formal definition of our RDB-to-RDF mapping, the architecture of our mediator platform, and a performance evaluation of the prototype implementation. Updating Relational Data via SPARQL/Update ABSTRACTRelational 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 updateaware 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.
The Semantic Web provides a standardized, well-established framework to define and work with ontologies. It is especially apt for machine processing. However, researchers in the field of software evolution have not really taken advantage of that so far.In this paper, we address the potential of representing software evolution knowledge with ontologies and Semantic Web technology, such as Linked Data and automated reasoning.We present SEON, a pyramid of ontologies for software evolution, which describes stakeholders, their activities, artifacts they create, and the relations among all of them. We show the use of evolution-specific ontologies for establishing a shared taxonomy of software analysis services, for defining extensible meta-models, for explicitly describing relationships among artifacts, and for linking data such as code structures, issues (change requests), bugs, and basically any changes made to a system over time.For validation, we discuss three different approaches, which are backed by SEON and enable semantically enriched software evolution analysis. These techniques have been fully implemented as tools and cover software analysis with web services, a natural language query interface for developers, and large-scale software visualization. Abstract The Semantic Web provides a standardized, well-established framework to define and work with ontologies. It is especially apt for machine processing. However, researchers in the field of software evolution have not really taken advantage of that so far. In this paper, we address the potential of representing software evolution knowledge with ontologies and Semantic Web technology, such as Linked Data and automated reasoning. We present Seon, a pyramid of ontologies for software evolution, which describes stakeholders, their activities, artifacts they create, and the relations among all of them. We show the use of evolution-specific ontologies for establishing a shared taxonomy of software analysis services, for defining extensible meta-models, for explicitly describing relationships among artifacts, and for linking data such as code structures, issues (change requests), bugs, and basically any changes made to a system over time. For validation, we discuss three different approaches, which are backed by Seon and enable semantically enriched software evolution analysis. These techniques have been fully implemented as tools and cover software analysis with web services, a natural language query interface for developers, and large-scale software visualization.
This proposal explores the promotion of existing relational databases to Semantic Web Endpoints. It presents the benefits of ontology-based read and write access to existing relational data as well as the need for specialized, scalable reasoning over that data. We introduce our approach for translating SPARQL/Update operations to SQL, describe how scalable reasoning can be realized by using the power of the database system, and outline two case studies for evaluating our approach. Relational Databases as Semantic Web Endpoints Matthias HertDepartment of Informatics, University of Zurich, Binzmuehlestrasse 14, CH-8050 Zurich, Switzerland hert@ifi.uzh.chAbstract. This proposal explores the promotion of existing relational databases to Semantic Web Endpoints. It presents the benefits of ontologybased read and write access to existing relational data as well as the need for specialized, scalable reasoning over that data. We introduce our approach for translating SPARQL/Update operations to SQL, describe how scalable reasoning can be realized by using the power of the database system, and outline two case studies for evaluating our approach.
Business-critical legacy applications often rely on relational databases to sustain daily operations. Introducing Semantic Web technology in newly developed systems is often difficult, as these systems need to run in tandem with their predecessors and cooperatively read and update existing data.A common pattern is to incrementally migrate data from a legacy system to its successor by running the new system in parallel, with a data bridge in between. Existing approaches that can be deployed as a data bridge in theory, restrict Semantic Web-enabled applications to read legacy data in practice, disallowing update operations completely.This paper explains how our RDB-to-RDF platform OntoAccess can be used to transition legacy systems into Semantic Web-enabled applications. By means of a case study, we exemplify how we successfully made a bridge between one of our own large-scale legacy systems and its long-term replacement. We elaborate on challenges we faced during the migration process and how we were able to overcome them. How to "Make a Bridge to the New Town" using OntoAccessMatthias Hert, Giacomo Ghezzi, Michael Würsch, and Harald C. Gall s.e.a.l. -software architecture and evolution lab Department of Informatics University of Zurich, Switzerland {hert,ghezzi,wuersch,gall}@ifi.uzh.ch Abstract. Business-critical legacy applications often rely on relational databases to sustain daily operations. Introducing Semantic Web technology in newly developed systems is often difficult, as these systems need to run in tandem with their predecessors and cooperatively read and update existing data. A common pattern is to incrementally migrate data from a legacy system to its successor by running the new system in parallel, with a data bridge in between. Existing approaches that can be deployed as a data bridge in theory, restrict Semantic Web-enabled applications to read legacy data in practice, disallowing update operations completely. This paper explains how our RDB-to-RDF platform OntoAccess can be used to transition legacy systems into Semantic Web-enabled applications. By means of a case study, we exemplify how we successfully made a bridge between one of our own large-scale legacy systems and its long-term replacement. We elaborate on challenges we faced during the migration process and how we were able to overcome them.
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