Intelligent information extraction from scholarly document databases
DOI:
https://doi.org/10.37380/jisib.v10i2.584Keywords:
Business intelligence, concept map, information extraction, knowledge management, literature review, natural language process, NLP, semantic searchAbstract
Extracting knowledge from big document databases has long been a challenge. Most researchers do a literature review and manage their document databases with tools that just provide a bibliography and when retrieving information (a list of concepts and ideas), there is a severe lack of functionality. Researchers do need to extract specific information from their scholarly document databases depending on their predefined breakdown structure. Those databases usually contain a few hundred documents, information requirements are distinct in each research project, and technique algorithms are not always the answer. As most retrieving and information extraction algorithms require manual training, supervision, and tuning, it could be shorter and more efficient to do it by hand and dedicate time and effort to perform an effective semantic search list definition that is the key to obtain the desired results. A robust relative importance index definition is the final step to obtain a ranked importance concept list that will be helpful both to measure trends and to find a quick path to the most appropriate paper in each case.
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