Transformation Approach of Open Web Data to Linked Open Data


  • Amina Meherehera Ecole Nationale Supérieure d’Informatique, ESI, Algiers, Algeria
  • Imane Mekideche Ecole Nationale Supérieure d’Informatique, ESI, Algiers, Algeria
  • Leila Zemmouchi-Ghomari Ecole Nationale Supérieure de Technologie, ENST, Algiers, Algeria
  • Abdessamed Réda Ghomari LMCS Laboratory, Ecole Nationale Supérieure d’Informatique, ESI, Algiers, Algeria



Web Data, Open Data, Linked Data, Semantic Web, Transformation Approaches


A large amount of data available over the Web and, in particular, the open data have, generally, heterogeneous formats and are not machine-readable. One promising solution to overcome the problems of heterogeneity and automatic interpretation is the Linked Data initiative, which aims to provide unified practices for publishing and contextually to link data on the Web, by using World Wide Web Consortium standards and the Semantic Web technologies. LinkedIn data promote the Web’s transformation from a web of documents to a web of data, ensuring that machines and software agents can interpret the semantics of data correctly and therefore infer new facts and return relevant web data search results.

This paper presents an automatic generic transformation approach that manipulates several input formats of open web data to linked open data. This work aims to participate actively in the movement of publishing data compliant with linked data principles.


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Author Biographies

Amina Meherehera, Ecole Nationale Supérieure d’Informatique, ESI, Algiers, Algeria

Amina Meherhera, currently Data Engineer at Dataimpact since Septemeber, 2020. She attended the Ecole Nationale Supérieure d’Informatique of Algiers (ESI-ex INI) from 2015 to 2020 and she graduated on 2020 with master degree and engineering degree in computer science. Her master graduation project was about state of the art of existing approaches to transform web open data to linked data and her engineering project was about proposing a new approach that covers the limits of the approaches studied in the state of tha art. The work was a part of a governmental project held by LMCS (Laboratoire des Méthodes de Conception des Systèmes) where she worked as a researcher from September, 2019 to Sepetember, 2020.

Imane Mekideche, Ecole Nationale Supérieure d’Informatique, ESI, Algiers, Algeria

Imene Mekideche is a Junior Software engineer in the National Employment Agency, Algeria with a passion for computer science, web development and technology. She earned her engineer’s and Master’s degrees in Computer Science Engineering from Ecole Nationale Supérieure d’Informatique, ESI. The graduation’s project was an opportunity to be involved in the scientific research field, the topic was about proposing a new approach for transforming open data to linked data. She is currently, a freelancer frontend developer with Unchained Agency (United Arab Emirates), in parallel of freelance, her main job is with the National Employment Agency – Algeria as a software engineer.

Leila Zemmouchi-Ghomari, Ecole Nationale Supérieure de Technologie, ENST, Algiers, Algeria

Leila Zemmouchi-Ghomari, received her PhD in Computer Science from Ecole Nationale Supérieure d’Informatique (ESI), Algiers, in January 2014. Her research interests focus on ontology engineering, knowledge engineering, semantic web, linked data and open data. She works currently as an Associate Professor at ENST: Ecole Nationale Supérieure de Technologie, Algiers, Algeria.

Abdessamed Réda Ghomari, LMCS Laboratory, Ecole Nationale Supérieure d’Informatique, ESI, Algiers, Algeria

Abdessamed Réda Ghomari, received his PhD in Computer Science (Information System) from ESI (Ecole Nationale Supérieure d’Informatique), Algiers, 2008. He is currently a Full Professor at ESI, Algiers, Algeria. Since 2001, he is an Information System Management Team Head at the LMCS (the Laboratory of Systems Design Methodologies). His research interests focus on Risks Management, Data governance, Open Government data, Collective intelligence and Crisis Informatics.


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