Juan F. Sequeda

On directly mapping relational databases to RDF and OWL

Mapping relational databases to RDF is a fundamental problem for the development of the Semantic Web. We present a solution, inspired by draft methods defined by the W3C where relational databases are directly mapped to RDF and OWL. Given a relational database schema and its integrity constraints, this direct mapping produces an OWL ontology, which, provides the basis for generating RDF instances. The semantics of this mapping is defined using Datalog. Two fundamental properties are information preservation and query preservation. We prove that our mapping satisfies both conditions, even for relational databases that contain null values. We also consider two desirable properties: monotonicity and semantics preservation. We prove that our mapping is monotone and also prove that no monotone mapping, including ours, is semantic preserving. We realize that monotonicity is an obstacle for semantic preservation and thus present a non-monotone direct mapping that is semantics preserving.

Ultrawrap: SPARQL execution on relational data

The Semantic Webs promise of web-wide data integration requires the inclusion of legacy relational databases, i.e. the execution of SPARQL queries on RDF representation of the legacy relational data. We explore a hypothesis: existing commercial relational databases already subsume the algorithms and optimizations needed to support effective SPARQL execution on existing relationally stored data. The experiment is embodied in a system, Ultrawrap, that encodes a logical representation of the database as an RDF graph using SQL views and a simple syntactic translation of SPARQL queries to SQL queries on those views. Thus, in the course of executing a SPARQL query, the SQL optimizer uses the SQL views that represent a mapping of relational data to RDF, and optimizes its execution. In contrast, related research is predicated on incorporating optimizing transforms as part of the SPARQL to SQL translation, and/or executing some of the queries outside the underlying SQL environment. Ultrawrap is evaluated using two existing benchmark suites that derive their RDF data from relational data through a Relational Database to RDF (RDB2RDF) Direct Mapping and repeated for each of the three major relational database management systems. Empirical analysis reveals two existing relational query optimizations that, if applied to the SQL produced from a simple syntactic translations of SPARQL queries (with bound predicate arguments) to SQL, consistently yield query execution time that is comparable to that of SQL queries written directly for the relational representation of the data. The analysis further reveals the two optimizations are not uniquely required to achieve a successful wrapper system. The evidence suggests effective wrappers will be those that are designed to complement the optimizer of the target database.

Translating SQL Applications to the Semantic Web

The content of most Web pages is dynamically derived from an underlying relational database. Thus, the success of the Semantic Web hinges on enabling access to relational databases and their content by semantic methods. We define a system for automatic transformation of SQL DDL schemas into OWL DL ontologies. This system goes further than earlier efforts in that the entire system is expressed in first-order logic. We leverage the formal approach to show the system is complete with respect to a space of the possible relations that can be formed among relational tables as a consequence of primary and foreign key combinations. The full set of transformation rules is stratified, thus the system can be executed directly by a Datalog interpreter.

Formalisation and experiences of R2RML-based SPARQL to SQL query translation using morph

R2RML is used to specify transformations of data available in relational databases into materialised or virtual RDF datasets. SPARQL queries evaluated against virtual datasets are translated into SQL queries according to the R2RML mappings, so that they can be evaluated over the underlying relational database engines. In this paper we describe an extension of a well-known algorithm for SPARQL to SQL translation, originally formalised for RDBMS-backed triple stores, that takes into account R2RML mappings. We present the result of our implementation using queries from a synthetic benchmark and from three real use cases, and show that SPARQL queries can be in general evaluated as fast as the SQL queries that would have been generated by SQL experts if no R2RML mappings had been used.

Review: survey of directly mapping sql databases to the semantic web

The Semantic Web anticipates integrated access to a large number of information sources on the Internet represented as Resource Description Framework (RDF). Given the large number of websites that are backed by SQL databases, methods that automate the translation of those databases to RDF are crucial. One approach, taken by a number of researchers, is to directly map the SQL schema to an equivalent Web Ontology Language (OWL) or RDF Schema representation, which in turn, implies an RDF representation for the relational data. This paper reviews this research, and derives a consolidated, overarching set of translation rules expressible as a stratified Datalog program. We present all the possible key combinations in an SQL schema and consider their implied semantic properties. We review the approaches and characterize them with respect to the scope of their coverage of SQL constructs.

Mapping between the OBO and OWL ontology languages

BackgroundOntologies are commonly used in biomedicine to organize concepts to describe domains such as anatomies, environments, experiment, taxonomies etc. NCBO BioPortal currently hosts about 180 different biomedical ontologies. These ontologies have been mainly expressed in either the Open Biomedical Ontology (OBO) format or the Web Ontology Language (OWL). OBO emerged from the Gene Ontology, and supports most of the biomedical ontology content. In comparison, OWL is a Semantic Web language, and is supported by the World Wide Web consortium together with integral query languages, rule languages and distributed infrastructure for information interchange. These features are highly desirable for the OBO content as well. A convenient method for leveraging these features for OBO ontologies is by transforming OBO ontologies to OWL.ResultsWe have developed a methodology for translating OBO ontologies to OWL using the organization of the Semantic Web itself to guide the work. The approach reveals that the constructs of OBO can be grouped together to form a similar layer cake. Thus we were able to decompose the problem into two parts. Most OBO constructs have easy and obvious equivalence to a construct in OWL. A small subset of OBO constructs requires deeper consideration. We have defined transformations for all constructs in an effort to foster a standard common mapping between OBO and OWL. Our mapping produces OWL-DL, a Description Logics based subset of OWL with desirable computational properties for efficiency and correctness. Our Java implementation of the mapping is part of the official Gene Ontology project source.ConclusionsOur transformation system provides a lossless roundtrip mapping for OBO ontologies, i.e. an OBO ontology may be translated to OWL and back without loss of knowledge. In addition, it provides a roadmap for bridging the gap between the two ontology languages in order to enable the use of ontology content in a language independent manner.

OBDA: Query Rewriting or Materialization? In Practice, Both!

Given a source relational database, a target OWL ontology and a mapping from the source database to the target ontology, Ontology-Based Data Access (OBDA) concerns answering queries over the target ontology using these three components. This paper presents the development of UltrawrapOBDA, an OBDA system comprising bidirectional evaluation; that is, a hybridization of query rewriting and materialization. We observe that by compiling the ontological entailments as mappings, implementing the mappings as SQL views and materializing a subset of the views, the underlying SQL optimizer is able to reduce the execution time of a SPARQL query by rewriting the query in terms of the views specified by the mappings. To the best of our knowledge, this is the first OBDA system supporting ontologies with transitivity by using SQL recursion. Our contributions include: (1) an efficient algorithm to compile ontological entailments as mappings; (2) a proof that every SPARQL query can be rewritten into a SQL query in the context of mappings; (3) a cost model to determine which views to materialize to attain the fastest execution time; and (4) an empirical evaluation comparing with a state-of-the-art OBDA system, which validates the cost model and demonstrates favorable execution times.

QODI: Query as Context in Automatic Data Integration

QODI is an automatic ontology-based data integration system (OBDI). QODI is distinguished in that the ontology mapping algorithm dynamically determines a partial mapping specific to the reformulation of each query. The query provides application context not available in the ontologies alone; thereby the system is able to disambiguate mappings for different queries. The mapping algorithm decomposes the query into a set of paths, and compares the set of paths with a similar decomposition of a source ontology. Using test sets from three real world applications, QODI achieves favorable results compared with AgreementMaker, a leading ontology matcher, and an ontology-based implementation of the mapping methods detailed for Clio, the state-of-the-art relational data integration and data exchange system.

Querying Semantic Data on the Web

The Semantic Web is the initiative of the W3C to make information on the Web readable not only by humans but also by machines. RDF is the data model for Semantic Web data, and SPARQL is the standard query language for this data model. In recent years, we have witnessed a constant growth in the amount of RDF data available on the Web, which has motivated the theoretical study of fundamental aspects of RDF and SPARQL. The goal of this paper is two-fold: to introduce SPARQL, which is a fundamental technology for the development of the Semantic Web, and to present some interesting and non-trivial problems on RDF data management at a Web scale, that we think the database community should address.

How to consume linked data on the web: tutorial description

In the past two years, the amount of data published in RDF and following the Linked Data principles has increased dramatically. Everyday people are publishing datasets as Linked Data. However, applications that consume Linked Data are not mainstream yet. To overcome this issue, we present a beginners tutorial on consuming Linked Data. We will discuss existing techniques how users can currently consume Linked Data and use it in their current applications.