Abstract
Linked Open Data (LOD) available on the web not only encodes heterogamous structural relationship between entities of various types but is also a source of textual information associated with these entities. Governments at various levels can benefit from this data while making certain policy decisions. The goal of this work is to provide semi-automated means to access information from open data sources that is relevant to the policy proposal in question. This paper reports investigation of the approaches for thematic analysis of policy documents, identification and summarization techniques of linked open dataset relevant to the topics of the policy documents. The key focus of the work carried out includes the identification of the functionality based on end user needs, development of a linked open data search strategy model and investigation of techniques required to realize the needed functionality specified in the model. As a background work we have identified the use of machine learning techniques to automatically determining the main themes or topics from within the policy document for generating the search terms for searching the open and linked open data repositories, investigated the use of various approaches for finding topic connectivity structure in the linked open data cloud, methodology for topic diversity based relevance ranking of datasets and summarization algorithms for generating the dataset summaries.
Nasir Naveed, Thomas Gottron. (2017) Approaches for Summarization and Identification of Data Sources in the Linked Open Data Cloud, Journal of Applied and Emerging Sciences, Volume 7, Issue 1.
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