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.

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 Accessing biological data in R with semantic web technologies

2014 ◽  
Author(s):  
Egon Willighagen
Keyword(s):  
Semantic Web ◽  
Brca1 Gene ◽  
Database Systems ◽  
Biological Data ◽  
Biological Database ◽  
Semantic Web Technologies ◽  
Data Set ◽  
Web Technologies ◽  
Web Standards ◽  
Rdf Data

Background. Semantic Web technologies are increasingly used in biological database systems. The improved expressiveness show advantages in tracking provenance and allowing knowledge to be more explicitly annotated. The list of semantic web standards needs a complementary set of tools to handle data in those formats to use them in bioinformatics workflows. Methods. The approach proposed in this paper uses the Apache Jena library to create an environment where semantic web technologies can be use in the statistical environment R. The code is exposed as two R packages available from the Comprehensive R Archive Network (CRAN). The RJava library and a custom convenience class is used to bridge between R and the Jena library. Results. We here present two examples showing how the Resource Description Framework (RDF) and SPARQL query standards can be employed in R. The first example takes input on BRCA1 SNPs from a BioMart and converts this into a RDF data set. The second example runs a query on an experimental remote SPARQL end point provided by Uniprot, and searches textual annotations of proteins encoded by the BRCA1 gene. The third example shows how the package can be used to handle RDF returned by OpenTox web services. Discussion. The two provided library bring basic semantic web technologies to R. While only a subset of Apache Jena is currently exposed, it provides key methods to deal with RDF data and resources. The libraries are freely available from the CRAN under the Affero GNU Public License version 3: http://cran.r-project.org/web/packages/rrdf/.

scholarly journals R2D2: A Dbpedia Chatbot Using Triple-Pattern Like Queries

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 217
Author(s):  
Haridimos Kondylakis ◽  
Dimitrios Tsirigotakis ◽  
Giorgos Fragkiadakis ◽  
Emmanouela Panteri ◽  
Alexandros Papadakis ◽  
...  
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Semantic Web Technologies ◽  
User Input ◽  
Web Technologies ◽  
Structured Input ◽  
Controlled Natural Language ◽  
Sparql Endpoint ◽  
Natural Language Interface ◽  
Available Information

Chatbots, also known as conversation agents, are programs that are able to simulate and reproduce an intelligent conversation with humans. Although this type of program is not new, the explosion of the available information and the rapid increase of the users seeking this information have renewed the interest in their development. In this paper, we present R2D2, an intelligent chatbot relying on semantic web technologies and offering an intelligent controlled natural language interface for accessing the information available in DBpedia. The chatbot accepts structured input, allowing users to enter triple-pattern like queries, which are answered by the underlying engine. While typing, an auto-complete service guides users on creating the triple patterns, suggesting resources available in the DBpedia. Based on user input (in the form of triple-pattern like queries), the corresponding SPARQL queries are automatically formulated. The queries are submitted to the corresponding DBpedia SPARQL endpoint, and then the result is received by R2D2 and augmented with maps and visuals and eventually presented to the user. The usability evaluation performed shows the advantages of our solution and its usefulness.

scholarly journals Accessing biological data with semantic web technologies

2013 ◽  
Author(s):  
Egon Willighagen
Keyword(s):  
Semantic Web ◽  
Brca1 Gene ◽  
Database Systems ◽  
Biological Data ◽  
Biological Database ◽  
Semantic Web Technologies ◽  
Data Set ◽  
Web Technologies ◽  
Web Standards ◽  
Rdf Data

Background. Semantic Web technologies are increasingly used in biological database systems. The improved expressiveness show advantages in tracking provenance and allowing knowledge to be more explicitly annotated. The list of semantic web standards needs a complementary set of tools to handle data in those formats to use them in bioinformatics workflows. Methods. The approach proposed in this paper uses the Apache Jena library to create an environment where semantic web technologies can be use in the statistical environment R. The code is exposed as two R packages available from the Comprehensive R Archive Network (CRAN). The RJava library and a custom convenience class is used to bridge between R and the Jena library. Results. We here present two examples showing how the Resource Description Framework (RDF) and SPARQL query standards can be employed in R. The first example takes input on BRCA1 SNPs from a BioMart and converts this into a RDF data set. The second example runs a query on an experimental remote SPARQL end point provided by Uniprot, and searches textual annotations of proteins encoded by the BRCA1 gene. Discussion. The two provided library bring basic semantic web technologies to R. While only a subset of Apache Jena is currently exposed, it provides key methods to deal with RDF data and resources. The libraries are freely available from the CRAN under the Affero GNU Public License version 3: http://cran.r-project.org/web/packages/rrdf/.

scholarly journals Using Semantic Web Technologies to Annotate and Align Microarray Designs

2009 ◽  
Vol 8 ◽  
pp. CIN.S2335 ◽  
Author(s):  
Sebastian Szpakowski ◽  
James McCusker ◽  
Michael Krauthammer
Keyword(s):  
Semantic Web ◽  
Query Language ◽  
Gene Expression Microarray ◽  
Expression Microarray ◽  
Semantic Web Technologies ◽  
Cancer Data ◽  
Web Technologies ◽  
Web Environment ◽  
Mapping Array ◽  
Genomic Element

In this paper, we annotate and align two different gene expression microarray designs using the Genomic ELement Ontology (GELO). GELO is a new ontology that leverages an existing community resource, Sequence Ontology (SO), to create views of genomically-aligned data in a semantic web environment. We start the process by mapping array probes to genomic coordinates. The coordinates represent an implicit link between the probes and multiple genomic elements, such as genes, transcripts, miRNA, and repetitive elements, which are represented using concepts in SO. We then use the RDF Query Language (SPARQL) to create explicit links between the probes and the elements. We show how the approach allows us to easily determine the element coverage and genomic overlap of the two array designs. We believe that the method will ultimately be useful for integration of cancer data across multiple omic studies. The ontology and other materials described in this paper are available at http://krauthammerlab.med.yale.edu/wiki/Gelo .

scholarly journals Towards Verbalizing SPARQL Queries in Arabic

2016 ◽  
Vol 6 (2) ◽  
pp. 937-944
Author(s):  
I. Al Agha ◽  
O. El-Radie
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Query Language ◽  
Arabic Language ◽  
Syntactic Analysis ◽  
Information Need ◽  
Translation Process ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Different Characteristics

With the wide spread of Open Linked Data and Semantic Web technologies, a larger amount of data has been published on the Web in the RDF and OWL formats. This data can be queried using SPARQL, the Semantic Web Query Language. SPARQL cannot be understood by ordinary users and is not directly accessible to humans, and thus they will not be able to check whether the retrieved answers truly correspond to the intended information need. Driven by this challenge, natural language generation from SPARQL data has recently attracted a considerable attention. However, most existing solutions to verbalize SPARQL in natural language focused on English and Latin-based languages. Little effort has been made on the Arabic language which has different characteristics and morphology. This work aims to particularly help Arab users to perceive SPARQL queries on the Semantic Web by translating SPARQL to Arabic. It proposes an approach that gets a SPARQL query as an input and generates a query expressed in Arabic as an output. The translation process combines both morpho-syntactic analysis and language dependencies to generate a legible and understandable Arabic query. The approach was preliminary assessed with a sample query set, and results indicated that 75% of the queries were correctly translated into Arabic.

Semantic Web Technologies for e-Learning: Models and Implementation

Informatica ◽  
2015 ◽  
Vol 26 (2) ◽  
pp. 221-240 ◽  
Author(s):  
Valentina Dagienė ◽  
Daina Gudonienė ◽  
Renata Burbaitė
Keyword(s):  
Semantic Web ◽  
Learning Models ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
E Learning
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