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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Attwood, T.K.: The babel of bioinformatics. Science 290(5491), 471–473 (2000)
Cattaneo, G., Petrillo, U.F., Giancarlo, R., Roscigno, G.: An effective extension of the applicability of alignment-free biological sequence comparison algorithms with hadoop. J. Supercomput. 73(4), 1467–1483 (2017)
Felsenstein, J.: Numerical methods for inferring evolutionary trees. Quart. Rev. Biol. 57(4), 379–404 (1982)
Ferraro Petrillo, U., Roscigno, G., Cattaneo, G., Giancarlo, R.: Fastdoop: A versatile and efficient library for the input of fasta and fastq files for mapreduce hadoop bioinformatics applications. Bioinformatics 33(10), 1575–1577 (2017)
Koohy, H., Dyer, N.P., Reid, J.E., Koentges, G., Ott, S.: An alignment-free model for comparison of regulatory sequences. Bioinformatics 26(19), 2391–2397 (2010)
Nardiello, A.M., Piotto, S., Di Biasi, L., Sessa, L.: Pseudo-semantic approach to study model membranes. In: Piotto, S., Concilio, S., Sessa, L., Rossi, F. (eds.) BIONAM 2019 2019. LNB, pp. 120–127. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-47705-9_11
Piotto, S., Di Biasi, L., Concilio, S., Castiglione, A., Cattaneo, G.: Grimd: Distributed computing for chemists and biologists. Bioinformation 10(1), 43 (2014)
Piotto, S., Nardiello, A.M., Di Biasi, L., Sessa, L.: Encoding materials dynamics for machine learning applications. In: Piotto, S., Concilio, S., Sessa, L., Rossi, F. (eds.) BIONAM 2019 2019. LNB, pp. 128–136. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-47705-9_12
Sims, G.E., Jun, S.R., Wu, G.A., Kim, S.H.: Alignment-free genome comparison with feature frequency profiles (ffp) and optimal resolutions. Proc. Natl. Acad. Sci. 106(8), 2677–2682 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-23929-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23928-1
Online ISBN: 978-3-031-23929-8
eBook Packages: Computer ScienceComputer Science (R0)