Abstract
This is a technical and theoretical paper conceptualizing problems to be solved and a few solutions in order to have groundwork for a more ‘natural search’ to make effective educational use of the Internet on any later filtered search, in this case, in renewable energy innovation. Additionally, this platform for geolocating energy innovations would generate interest in energy resource development trends and encourage industrial progress in the energy field. To do this, our interest is in erecting a strong foundation in natural search to build a better filtered search in other words. However, pragmatically, how can this be done if popular search engines return different kinds of filtered biased searches from the start? We analyze a current prototype platform designed by our team to be a fresh educational platform with a global heat map for geolocation of energy innovations. This platform attempts to “hack” (in a positive way, as a creative work around) and to approximate a natural search using various methods. In short, we discuss a doubly pressing issue in education and sustainability: first, finding a natural search, and second, filtering it upon energy innovations and sharing that information effectively. We list many problems and some solutions toward a more unbiased natural search in discussing our solutions for sampling innately low-‘page ranked’ energy innovations around the world. We aim to create a platform to better to visualize and to plot trends and locations of energy innovation across the world for expanding that innovation, yet it depends on having a more accurate and trustworthy database to start with in a natural search.
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Ku, S., Kim, S., You, M., Whitaker, M.D. (2023). Building the Groundwork for a Natural Search, to Make Accurate and Trustworthy Filtered Searches: The Case of a New Educational Platform with a Global Heat Map to Geolocate Innovations in Renewable Energy. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_25
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