Querying the Semantic Web via Rules

2020 ◽  
Author(s):  
Marcelo Arenas ◽  
Georg Gottlob ◽  
Andreas Pieris
Keyword(s):  
Semantic Web ◽  
Query Language ◽  
Central Issue ◽  
General Mechanism ◽  
Regular Expressions ◽  
Graph Structure ◽  
Rule Based ◽  
Core Fragment ◽  
Navigation Patterns ◽  
Rdf Data

The problem of querying RDF data is a central issue for the development of the Semantic Web. The query language SPARQL has become the standard language for querying RDF since its W3C standardization in 2008. However, the 2008 version of this language missed some important functionalities: reasoning capabilities to deal with RDFS and OWL vocabularies, navigational capabilities to exploit the graph structure of RDF data, and a general form of recursion much needed to express some natural queries. To overcome those limitations, a new version of SPARQL, called SPARQL 1.1, was released in 2013, which includes entailment regimes for RDFS and OWL vocabularies, and a mechanism to express navigation patterns through regular expressions. Nevertheless, there are useful navigation patterns that cannot be expressed in SPARQL 1.1, and the language lacks a general mechanism to express recursive queries. This chapter is a gentle introduction to a tractable rule-based query language, in fact, an extension of Datalog with value invention, stratified negation, and falsum, that is powerful enough to define SPARQL queries enhanced with the desired functionalities focussing on a core fragment of the OWL 2 QL profile of OWL 2.

scholarly journals Using superimposed multidimensional schemas and OLAP patterns for RDF data analysis

2018 ◽  
Vol 8 (1) ◽  
pp. 18-37 ◽  
Author(s):  
Median Hilal ◽  
Christoph G. Schuetz ◽  
Michael Schrefl
Keyword(s):  
Data Analysis ◽  
Semantic Web ◽  
Query Language ◽  
Query Languages ◽  
Sparql Query ◽  
Semantic Web Technologies ◽  
Rdf Data ◽  
Self Service ◽  
Description Framework ◽  
Definition Of

Abstract The foundations for traditional data analysis are Online Analytical Processing (OLAP) systems that operate on multidimensional (MD) data. The Resource Description Framework (RDF) serves as the foundation for the publication of a growing amount of semantic web data still largely untapped by companies for data analysis. Most RDF data sources, however, do not correspond to the MD modeling paradigm and, as a consequence, elude traditional OLAP. The complexity of RDF data in terms of structure, semantics, and query languages renders RDF data analysis challenging for a typical analyst not familiar with the underlying data model or the SPARQL query language. Hence, conducting RDF data analysis is not a straightforward task. We propose an approach for the definition of superimposed MD schemas over arbitrary RDF datasets and show how to represent the superimposed MD schemas using well-known semantic web technologies. On top of that, we introduce OLAP patterns for RDF data analysis, which are recurring, domain-independent elements of data analysis. Analysts may compose queries by instantiating a pattern using only the MD concepts and business terms. Upon pattern instantiation, the corresponding SPARQL query over the source data can be automatically generated, sparing analysts from technical details and fostering self-service capabilities.

scholarly journals Creating RESTful APIs over SPARQL endpoints using RAMOSE

Semantic Web ◽  
2021 ◽  
pp. 1-19
Author(s):  
Marilena Daquino ◽  
Ivan Heibi ◽  
Silvio Peroni ◽  
David Shotton
Keyword(s):  
Semantic Web ◽  
Service Providers ◽  
Query Language ◽  
Sparql Query ◽  
Direct Access ◽  
Technical Solution ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Sparql Endpoint ◽  
Rdf Data

Semantic Web technologies are widely used for storing RDF data and making them available on the Web through SPARQL endpoints, queryable using the SPARQL query language. While the use of SPARQL endpoints is strongly supported by Semantic Web experts, it hinders broader use of RDF data by common Web users, engineers and developers unfamiliar with Semantic Web technologies, who normally rely on Web RESTful APIs for querying Web-available data and creating applications over them. To solve this problem, we have developed RAMOSE, a generic tool developed in Python to create REST APIs over SPARQL endpoints. Through the creation of source-specific textual configuration files, RAMOSE enables the querying of SPARQL endpoints via simple Web RESTful API calls that return either JSON or CSV-formatted data, thus hiding all the intrinsic complexities of SPARQL and RDF from common Web users. We provide evidence that the use of RAMOSE to provide REST API access to RDF data within OpenCitations triplestores is beneficial in terms of the number of queries made by external users of such RDF data using the RAMOSE API, compared with the direct access via the SPARQL endpoint. Our findings show the importance for suppliers of RDF data of having an alternative API access service, which enables its use by those with no (or little) experience in Semantic Web technologies and the SPARQL query language. RAMOSE can be used both to query any SPARQL endpoint and to query any other Web API, and thus it represents an easy generic technical solution for service providers who wish to create an API service to access Linked Data stored as RDF in a triplestore.

A Tool for Transforming Semantic Web Rule Language to SPARQL Infererecing Notation

2020 ◽  
Vol 16 (1) ◽  
pp. 87-115
Author(s):  
Nick Bassiliades
Keyword(s):  
Semantic Web ◽  
Query Language ◽  
Markup Language ◽  
Web Ontology Language ◽  
Rule Based ◽  
Horn Logic ◽  
Industry Standard ◽  
Ontology Language ◽  
Widespread Acceptance ◽  
Semantic Web Rule Language

Semantic web rule language (SWRL) combines web ontology language (OWL) ontologies with horn logic rules of the rule markup language (RuleML) family. Being supported by ontology editors, rule engines and ontology reasoners, it has become a very popular choice for developing rule-based applications on top of ontologies. However, SWRL is probably not going to become a WWW Consortium standard, prohibiting industrial acceptance. On the other hand, SPARQL Inferencing Notation (SPIN) has become a de-facto industry standard to represent SPARQL rules and constraints on semantic web models, building on the widespread acceptance of SPARQL (SPARQL Protocol and RDF Query Language). In this article, we argue that the life of existing SWRL rule-based ontology applications can be prolonged by converting them to SPIN. To this end, we have developed the SWRL2SPIN tool in Prolog that transforms SWRL rules into SPIN rules, considering the object-orientation of SPIN, i.e. linking rules to the appropriate ontology classes and optimizing them, as derived by analysing the rule conditions.

scholarly journals Semantic Web Mining using RDF Data

2016 ◽  
Vol 133 (10) ◽  
pp. 14-19 ◽  
Author(s):  
V. A. ◽  
Amruta A.
Keyword(s):  
Semantic Web ◽  
Web Mining ◽  
Rdf Data

A Trace Query Language for Rule-Based Models

2018 ◽  
pp. 220-237
Author(s):  
Jonathan Laurent ◽  
Hector F. Medina-Abarca ◽  
Pierre Boutillier ◽  
Jean Yang ◽  
Walter Fontana
Keyword(s):  
Query Language ◽  
Rule Based

scholarly journals IDSM ChemWebRDF: SPARQLing small-molecule datasets

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jakub Galgonek ◽  
Jiří Vondrášek
Keyword(s):  
Data Storage ◽  
Small Molecule ◽  
Web Application ◽  
Query Language ◽  
Data Interoperability ◽  
Sparql Endpoint ◽  
Data Source ◽  
Rdf Data ◽  
Relational Form ◽  
Federated Queries

AbstractThe Resource Description Framework (RDF), together with well-defined ontologies, significantly increases data interoperability and usability. The SPARQL query language was introduced to retrieve requested RDF data and to explore links between them. Among other useful features, SPARQL supports federated queries that combine multiple independent data source endpoints. This allows users to obtain insights that are not possible using only a single data source. Owing to all of these useful features, many biological and chemical databases present their data in RDF, and support SPARQL querying. In our project, we primary focused on PubChem, ChEMBL and ChEBI small-molecule datasets. These datasets are already being exported to RDF by their creators. However, none of them has an official and currently supported SPARQL endpoint. This omission makes it difficult to construct complex or federated queries that could access all of the datasets, thus underutilising the main advantage of the availability of RDF data. Our goal is to address this gap by integrating the datasets into one database called the Integrated Database of Small Molecules (IDSM) that will be accessible through a SPARQL endpoint. Beyond that, we will also focus on increasing mutual interoperability of the datasets. To realise the endpoint, we decided to implement an in-house developed SPARQL engine based on the PostgreSQL relational database for data storage. In our approach, data are stored in the traditional relational form, and the SPARQL engine translates incoming SPARQL queries into equivalent SQL queries. An important feature of the engine is that it optimises the resulting SQL queries. Together with optimisations performed by PostgreSQL, this allows efficient evaluations of SPARQL queries. The endpoint provides not only querying in the dataset, but also the compound substructure and similarity search supported by our Sachem project. Although the endpoint is accessible from an internet browser, it is mainly intended to be used for programmatic access by other services, for example as a part of federated queries. For regular users, we offer a rich web application called ChemWebRDF using the endpoint. The application is publicly available at https://idsm.elixir-czech.cz/chemweb/.

Semantic Web Standards for Publishing and Integrating Open Data

2014 ◽  
pp. 28-47
Author(s):  
Axel Polleres ◽  
Simon Steyskal
Keyword(s):  
Semantic Web ◽  
World Wide ◽  
Open Data ◽  
Standard Data ◽  
Web Standards ◽  
Web Of Data ◽  
Structured Information ◽  
Potential Risks ◽  
Rdf Data ◽  
The Web

The World Wide Web Consortium (W3C) as the main standardization body for Web standards has set a particular focus on publishing and integrating Open Data. In this chapter, the authors explain various standards from the W3C's Semantic Web activity and the—potential—role they play in the context of Open Data: RDF, as a standard data format for publishing and consuming structured information on the Web; the Linked Data principles for interlinking RDF data published across the Web and leveraging a Web of Data; RDFS and OWL to describe vocabularies used in RDF and for describing mappings between such vocabularies. The authors conclude with a review of current deployments of these standards on the Web, particularly within public Open Data initiatives, and discuss potential risks and challenges.

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