Peer-Reviewed Journal Details
Mandatory Fields
Adel Rezk, M. and Ojo, A. and Hassan, I.A.
2017
January
Mining governmental collaboration through semantic profiling of open data catalogues and publishers
Published
()
Optional Fields
506
253
264
Due to the increasing adoption of open data among governments worldwide especially in the European Union area, a deeper analysis of the newly published data is becoming a mandate. Apart from analyzing the published dataset itself we aimed on analyzing published dataset catalogues. A dataset catalogue or a dataset metadata contains features that describe what the data is about in a textual representation. So, we first acquire data from open data portals, choose descriptive dataset catalogue features, and then construct an aggregated textual representation of the datasets. Afterwards we enrich those textual representations using Natural Language Processing (NLP) methods to create a new comparable data feature “Named Entities”. By mining the new data feature we are able to produce datasets and publishers relatedness network. Those networks are used to point similarities between the published data across multiple open data portals. Pointing all possible collaborations for integrating and standardizing data features and types would increase the value of da1ta and ease its analysis process. © IFIP International Federation for Information Processing 2017.
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029593119&doi=10.1007%2f978-3-319-65151-4_24&partnerID=40&md5=e48df45060d1da268885591f450dd070
10.1007/978-3-319-65151-4_24
Grant Details
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