Journal of Knowledge Management, Volume 19, Issue 1, February 2015.
Purpose 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. Design/methodology/approach 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. Findings The study described here identifies the challenges and opportunities of a combination of a machine and a human-driven approach to the development of social networks from textual documents. The creation, visualization and enrichment of a social network are presented within a real world-scenario. The dataset from which the network is based is accessible via an API and thus shareable with the KM community for reuse and mash-ups. Originality/value This study presents original methods to address the issue of detecting and representing semantic relationships from text. Another element of novelty is in that it applies semantic web technologies to the construction and enhancement of the network and underlying dataset making the data readable across platforms and linkable with external datasets. This approach has the potential to make social networks dynamic and open to integration with external data sources.
Purpose 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. Design/methodology/approach 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. Findings The study described here identifies the challenges and opportunities of a combination of a machine and a human-driven approach to the development of social networks from textual documents. The creation, visualization and enrichment of a social network are presented within a real world-scenario. The dataset from which the network is based is accessible via an API and thus shareable with the KM community for reuse and mash-ups. Originality/value This study presents original methods to address the issue of detecting and representing semantic relationships from text. Another element of novelty is in that it applies semantic web technologies to the construction and enhancement of the network and underlying dataset making the data readable across platforms and linkable with external datasets. This approach has the potential to make social networks dynamic and open to integration with external data sources.