Using Semantic Web Technologies to Underpin the SNOMED CT Query Language

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.

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Using Semantic Web Technologies to Annotate and Align Microarray Designs

Cancer Informatics ◽

10.4137/cin.s2335 ◽

2009 ◽

Vol 8 ◽

pp. CIN.S2335 ◽

Cited By ~ 1

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 .

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Towards Verbalizing SPARQL Queries in Arabic

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.630 ◽

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.

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