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Knowledge graph for aquaculture recommendation system
Tejaswini, H.; Manohara Pai, M.M.; Radhika, M.P. (2021). Knowledge graph for aquaculture recommendation system, in: 2021 IEEE Mysore Sub Section International Conference (MysuruCon). . https://dx.doi.org/10.1109/MysuruCon52639.2021.9641685
In: (2021). 2021 IEEE Mysore Sub Section International Conference (MysuruCon). IEEE: USA. ISBN 978-0-7381-4662-1 . https://dx.doi.org/10.1109/MysuruCon52639.2021, more

Available in  Authors 
Document type: Conference paper

Authors  Top 
  • Tejaswini, H.
  • Manohara Pai, M.M.
  • Radhika, M.P.

Abstract
    Aquaculture is a growing industry. It would be beneficial to the fish farmers if the data about the fish, such as the ecosystem, food and related information are available to them to increase the fish yield. Most of the fish species data of the aquaculture domain are stored using relational databases. However, the relational tables work well only for structured data. It would help the fishermen if the data can be visualized and provided with a suitable recommendation system that recommends the best species and best ecosystem. In this paper, an approach to store the details of fish species of the brackish water of the west coast of Karnataka, India using Neo4j is presented. Further, a recommendation system to retrieve the best fish species for a particular ecosystem is proposed. The data relating to fish species, names, threatened status, taxonomy, fish species location, type of water they survive in are stored as a connected graph in the Neo4j graph database. This helps the aquatic scientists and aquaculture users visualize the relationship among the fish species and get suitable recommendations on fish species based on their interests. The proposed system is scalable and is capable of processing any complex relationship for providing recommendations.

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