Abstract This paper describes a novel approach to the development and semantic enhancement of a social network to support the analysis and interpretation of digital oral history data from jazz archives and special collections. A multi-method approach was applied including automated named entity recognition and extraction to create a social network, and crowdsourcing techniques to semantically enhance the data through the classification of relations and the integration of contextual information. Linked Open Data standards provided the knowledge representation technique for the dataset underlying the network. The study described here identifies the challenges and opportunities of a combined machine-driven and human-driven approach to the development of social networks from textual documents. The creation, visualization and enrichment of a social network are presented using a real-world scenario. The dataset from which the network is based is accessible via an API and thus shareable with the web development community for reuse and mash-ups.  Originality/value
An analysis and an interpretation of archives and collections through historical data are presented in REF .
Semantic network edges: a human-machine approach to represent typed relations in social networks
J. Knowledge Management
J. Knowledge Management
Computer Science