Una revisión sistémica de los buscadores web inteligentes como mejora en la investigación científica e inteligencia artificial

• Autor: Jaramillo Atoche, Javier Eduardo
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Nacional de Frontera
• Repositorio: UNF-Aypate
• Lenguaje: español
• OAI Identifier: oai:ojs2.aypate.revista.unf.edu.pe:article/96
• Enlace del recurso: https://revistas.unf.edu.pe/index.php/aypate/article/view/96
• Nivel de acceso: acceso abierto
• Materia: Buscador web inteligente; inteligencia artificial; big data; investigación científica; Intelligent web search engine; artificial intelligence; scientific research

The world is confronted by challenges and opportunities arising from digital transformation and artificial intelligence. These technological changes have impacted the way we search for information on the web and have generated new forms of communication at a global level. Throughout history, the internet has evolved and different technologies and tools have been created that have facilitated access and interaction on the web, however, there are also challenges related to the understanding and proper application of artificial intelligence. This review raises the following questions: For such reason, the following questions were formulated: How to analyze intelligent web search engines as an improvement in scientific research? In addition, the sub-questions: How does big data influence the search for scientific and technological information, and how does artificial intelligence influence the search for information? In addition to the above, the objectives were formulated: To analyze intelligent web search engines as an improvement in scientific research. As specific objectives: To explain how big data influences the search for scientific information; To explain how artificial intelligence influences the search for information. Next, the methodology was established using the criteria established in the PICOC search strategy, based on the formulation of questions that allow the precise identification of the five key components of the information. In addition, the main databases such as Scopus were selected for bibliographic reference where the information of the component was found in key words and the search equation. As results, a bibliometric analysis was made of the information in Scopus divided into four (4) dimensions: "scientific production by type of documents", "annual scientific production of articles", "production by thematic area" and "production by country".  In addition, the graphs of the Scopus dimensions were created by means of algorithms and with the help of a programming language "Python" in Google Colab. Next, the bibliographic map was created using the "VOSWiever" tool, resulting in eleven (11) clusters. In the same way, the bibliometric network by artificial intelligence techniques through clustering generated eleven (11) groupings of the association of words. Finally, as conclusions, it is affirmed that there is an exponential growth in the search and analysis of web search engines. Likewise, there is an interest and influence in the search for scientific information, supported by the usability of big data and artificial intelligence. Finally, scientometrics generated a quantitative analysis and interpretation of the information supported by machine learning techniques.