A Framework for Capturing and Analyzing Unstructured and Semi-structured Data for a Knowledge Management System
This addresses the need for organizations to leverage varied data sources like text, audio, and video for strategic decisions, but it appears incremental as it builds on existing machine learning techniques without major breakthroughs.
The researchers tackled the problem of extracting knowledge from diverse unstructured and semi-structured data for organizational decision-making by developing a framework that captures and analyzes such data using machine learning and integrates it into traditional knowledge management systems, with evaluation showing sentiment analysis on student review data via a Python API.
Mainstream knowledge management researchers generally agree that knowledge extracted from unstructured data and semi-structured data have become imperative for organizational strategic decision making. In this research, we develop a framework that captures and analyses unstructured data using machine learning techniques and integrates knowledge and insight gained from the data into traditional knowledge management systems. Unlike most frameworks published in the literature that focuses on a specific type of unstructured data, our frameworks cut across the varieties of unstructured data ranging from textual data from social network sites, online forums, discussion boards, reviews to audio data, image data and video data. We highlight some pre-processing and processing techniques for these data and also highlight some standard output. We evaluate the framework by developing a textual data application programming interface (API) using python and beautiful soup and we perform sentiment analysis on the students review data collected through the API.