Social Semantic Web Paper Summaries 5: Analysis of a Real Online Social Network Using Semantic Web Frameworks by Guillaume Ereteo


In 2018, I have taken a master’s course from Bogazici University, called Social Semantic Web (CMPE 58H), teached by Suzan Üsküdarlı. (

I have wrote summaries for a few papers there. Today, I have decided to share them with you. This is both to share my understanding on these papers and to show my approach on how to read papers. Summary of the fifth paper is below.


2018 yılında Boğaziçi Üniversitesinde Sosyal Semantik Web (CMPE 58H) diye bir ders almıştım, Suzan Üsküdarlı hocamız dersi veriyordu.(

O derste birkaç makale özeti yazdım. Şimdi, o özetleri paylaşmaya karar verdim. Bunu, hem bu makalelere bakış açımı yansıtsın, ilgililer varsa okusun diye yapıyorum, hem de kendi makale okuyuş yöntemimi paylaşmak için yapıyorum. Beşinci makalenin özeti aşağıda.

Title: Analysis of a Real Online Social Network Using Semantic Web Frameworks

Citation: Guillaume Ereteo, Michel Buffa, Fabien Gandom, Oliver Corby

About Author:

Guillaume Ereteo is a senior R&D engineer and backend developer that has interest on Machine Learning, Semantic Web, Big Data, Information Retrieval and Functional Programming as it writes in his personal homepage.


Author makes a quick introduction on the current state of Social Network Analysis with Semantic Technologies in section 1.

After that, he presents their own ontology on Social Network Analysis concepts and explains the ontology in section 2.1.

In section 2.2, author speaks of SNA concepts and how to extract them with SPARQL queries.

In section 3.1 author talks about how they merged their ontology with SIOC

In section 3.2 author mentions how they used Corese to transform’s data into RDF/XML format.

The results are interpreted in section 4. Author tells about the semantic network they formed via ipernity’s data and comments on the properties of the network.

Author concludes the text in section 5 with also assessing further problems on Social Network Analysis that they currently work on.


1- Introduction

Organizing social network data is a major challange.

This social network data cannot be represented in conventional SNA graphs without loss of knowledge, Semantic Web technologies offer a nice solution for this via Rich Typed Graphs and SPARQL query language.

The article proposes to extend traditional SNA algorithms and to create new ones.

2- Semantic SNA

Most of social data needs to be converted.

They designed SemSNA ontology to represent SNA notions. This ontology helped researchers to manage the life cycle of the analysis more efficiently.

2.1 New Version of SemSNA Ontology

At first they used strategic position based on Freeman’s centrality however in the latter versions, they introduced many definitions and used those.

SNA Concept: Superclass of all concepts.

isDefinedForProperty: Indicates for what the concept is defined.

hasSNAConcept: binds Resources to Concepts.

SNAIndice: Valued concepts like centrality.

Centrality: Includes various centrality types.

hasValue: Numerical property to measure centrality etc.

2.2 Extract SNA Concepts with SPARQL

SPARQL is sufficient to transform or modify networks however it does not include SNA metrics querying. The team used Corese to compute such metrics.

A table is presented to define graph, number of actors, number of relations, path, length, density, component, degree, geosedic, diameter, centrality, betweenness in mathematical terms. Also SPARQL queries to extract these metrics.

3 Linking Online Interaction Data to the Semantic Web

The team analyzed’s data.

3.1 SemSNI: Extending SIOC to Model Social Networking Interactions


3.2 Generating RDF data from a Relational Database with Corese

Corese lets the user to nest SQL queries in SPARQL queries. There is also a function generating URIs from database IDs. This way the team was able to transform relational database data into semantic data.

4 Results


Researchers develop techniques to examine a real life social network found on the Internet and use them to examine the network’s properties. Author firstly introduces the current state of Social Network Analysis and then asserts the results of their techniques.

Author’s conclusions:

Author underlines the importance of Semantic Web technologies when analyzing social networks and states that they prove this fact with their framework SemSNA.

Your conclusions

I think researchers made quite a good job developing their framework to analyze real life networks. Their techniques and results are explained clearly. The text also acts as an overview of previous work which makes it even more benefitial.


I think the practical case that this paper stands on is an eye opener to many people since it is a real life analyzing process. Text is organized well with schemes, tables, a plain language and proper outlining. I reserve my 10/10 for exceptional papers however I am also having a difficulty finding a problem with this paper. This text is great.

Rate: 9

NLP Engineer at PragmaCraft. Former Researcher at Bogazici University Medical Imaging Lab. twitter:ahmetmeleq ///