Authors:
Antonio Capodieci
1
;
Daniele D’Aprile
1
;
Gianluca Elia
1
;
Francesca Grippa
2
and
Luca Mainetti
1
Affiliations:
1
University of Salento, Italy
;
2
Northeastern University, United States
Keyword(s):
Information Extraction, Information Gathering, Cultural Heritage Objects, Real-Time Data Extraction, Social Network Analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Business Intelligence Applications
;
Concept Mining
;
Data Analytics
;
Data Engineering
;
Interactive and Online Data Mining
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
;
Visual Data Mining and Data Visualization
Abstract:
This paper describes the design and implementation of a prototype to extract, collect and visually analyse
cultural digital resources using social network analysis empowered with semantic features. An initial
experiment involved the collection and visualization of connections between cultural digital resources - and
their providers - stored in the platform DiCet (an Italian Living Lab centred on Cultural Heritage and
Technology). This step helped to identify the most appropriate relational data model to use for the social
network visualization phase. We then run a second experiment using a web application designed to extract
relevant data from the platform Europeana.eu. The actors in our two-mode networks are Cultural Heritage
Objects (CHOs) shared by institutional and individual providers, such as galleries, museums, individual
experts and content aggregators. The links connecting nodes represent the digital resources associated to the
CHOs. The application of the prototype
offers insights on the most prominent providers, digital resources
and cultural objects over time. Through the application of semantic analysis, we were also able to identify
the most used words and the related sentiment associated to them.
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