bilalbenma / clodg2

cLODg2 -- conference Linked Open Data generator version 2

cLODg2 is the enhanced version of cLODg. It implements a methodology to produce Linked Data to describe a scientific conference and its publications, participants and events. To generate conference metadata we assume that you have initial data available (with some specific format). We then use use D2R conversion rules to produce metadata described with Conference Ontology. The workflow (modelled as an UML activity diagram in the following figure) includes two main activities, i.e., (i) Linked Data generation and (ii) Linked Data enrichment that start after an initialisation step required to customise the Linked Data generation process properly according to the inputs that consist of the CSV files containing the input data and the D2RQ mapping that will serve for converting CSV files to RDF.

The Linked Data generation activity is composed of the following actions (i.e., steps):

• Data gathering. This action allows the system to collect input data served as CSV files and. We remark that those data are about a scientific event like a conference or a workshop and come from a conference management system;
• RDB populuation. This action aims at populating a relational database (RDB) from the CSV files gathered from the previous action. The RDB is based on HyperSQL, which is a lightweight open-source Java database;
• D2R conversion. The previous action, i.e., RDB population, is preparatory to this step. In fact, cLODg2 relies on the D2R framework to perform the conversion of a non-RDF source to RDF. The conversion is guided by the mapping provided as input. This mapping is described by using the D2RQ mapping language. cLODg2 is released along with two D2RQ mapping formalisations: (i) to the Semantic Web Dog Food (SWDF), thus compliant with its related ontology; (ii) to Scholarlydata, which is the evolution of the Semantic Web Dog Food based based on an improvement of the Semantic Web Conference Ontology, adopting best ontology design practices.

Similarly, the Linked Data enrichment activity is composed of the following actions:

• Reasoning-based alignment. Input of this action are the RDF triples produced by the Linked Data generation activity. The output is the materialisation of a set of RDF tiples that enable the alignemnt to other ontologies and vocabularies, i.e., the SWDF ontology, SPAR ontologies, Dolce D0, the Organization Ontology, FOAF, SKOS, icatzd, and the Collections Ontology. The alignment triples are materialised by means of OWL-DL reasoning, which is enabled by the Apache Jena inference layer;
• Linking to other Linked Datasets. This action is aimed at producing in- stance level alignments, expressed via owl:sameAs axioms. The target linked datasets are ORCID and DOI. ORCID (Open Researcher and Contributor ID) provides persistent digital identifiers for scientific researchers and academic authors. A digital object identifier (DOI) is a serial code used to uniquely identify digital objects, particularly used for electronic documents. The alignments to ORCID are produced by relying on the public API provided by ORCID. The references to DOI are produced by relying on the API provided by Crossref, performing a search on each article title.