Abstract
Thanks to the services provided by the major cloud computing providers, the rise of Artificial Intelligence (AI) appears to be inevitable. Information analysis and processing, where the primary purpose is to extract knowledge and recombine it to create new knowledge, is an interesting research topic where AI is commonly applied. This research focuses on the semantic search problem: semantic search refers to the ability of search engines to evaluate the intent and context of search phrases while offering content to users. This study aims to see if introducing two biologically inspired characteristics, “weighting” and “correlated” characters, may increase semantic analysis performance. First, we built a preliminary prototype, ARISE, a semantic search engine using a new Artificial Network architecture built upon a new type of Artificial Neuron. Then, we trained and tested ARISE on the PubMed datasets.
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Di Biasi, L., Santoro, J., Piotto, S. (2022). ARISE: Artificial Intelligence Semantic Search Engine. In: Schneider, J.J., Weyland, M.S., Flumini, D., Füchslin, R.M. (eds) Artificial Life and Evolutionary Computation. WIVACE 2021. Communications in Computer and Information Science, vol 1722. Springer, Cham. https://doi.org/10.1007/978-3-031-23929-8_18
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DOI: https://doi.org/10.1007/978-3-031-23929-8_18
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