Methodology for AI-Based Search Strategy of Scientific Papers: Exemplary Search for Hybrid and Battery Electric Vehicles in the Semantic Scholar Database
The rapid development of artificial intelligence (AI) has significantly enhanced productivity, particularly in repetitive tasks. In the scientific domain, literature review stands out as a key area where AI-based tools can be effectively applied. This study presents a methodology for developing a se...
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MDPI AG
2024-12-01
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| author | Florian Wätzold Bartosz Popiela Jonas Mayer |
| author_facet | Florian Wätzold Bartosz Popiela Jonas Mayer |
| author_sort | Florian Wätzold |
| collection | DOAJ |
| description | The rapid development of artificial intelligence (AI) has significantly enhanced productivity, particularly in repetitive tasks. In the scientific domain, literature review stands out as a key area where AI-based tools can be effectively applied. This study presents a methodology for developing a search strategy for systematic reviews using AI tools. The Semantic Scholar database served as the foundation for the search process. The methodology was tested by searching for scientific papers related to batteries and hydrogen vehicles with the aim of enabling an evaluation for their potential applications. An extensive list of vehicles and their operational environments based on international standards and literature reviews was defined and used as the main input for the exemplary search. The AI-supported search yielded approximately 60,000 results, which were subjected to an initial relevance assessment. For the relevant papers, a neighbourhood analysis based on citation and reference networks was conducted. The final selection of papers, covering the period from 2013 to 2023, included 713 papers assessed after the initial review. An extensive discussion of the results is provided, including their categorisation based on search terms, publication years, and cluster analysis of powertrains, as well as operational environments of the vehicles involved. This case study illustrates the effectiveness of the proposed methodology and serves as a starting point for future research. The results demonstrate the potential of AI-based tools to enhance productivity when searching for scientific papers. |
| format | Article |
| id | doaj-art-9510b39f29ef4c1ea0200b088049826e |
| institution | OA Journals |
| issn | 2304-6775 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Publications |
| spelling | doaj-art-9510b39f29ef4c1ea0200b088049826e2025-08-20T02:01:21ZengMDPI AGPublications2304-67752024-12-011244910.3390/publications12040049Methodology for AI-Based Search Strategy of Scientific Papers: Exemplary Search for Hybrid and Battery Electric Vehicles in the Semantic Scholar DatabaseFlorian Wätzold0Bartosz Popiela1Jonas Mayer2Electrical Energy Storage Technology, Technische Universität Berlin, Einsteinufer 11, 10587 Berlin, GermanyBundesanstalt für Materialforschung und-prüfung (BAM), Abteilung 3 Gefahrgutumschließungen; Energiespeicher, Unter den Eichen 44-46, 12203 Berlin, GermanyCitrus Search, 81547 Munich, GermanyThe rapid development of artificial intelligence (AI) has significantly enhanced productivity, particularly in repetitive tasks. In the scientific domain, literature review stands out as a key area where AI-based tools can be effectively applied. This study presents a methodology for developing a search strategy for systematic reviews using AI tools. The Semantic Scholar database served as the foundation for the search process. The methodology was tested by searching for scientific papers related to batteries and hydrogen vehicles with the aim of enabling an evaluation for their potential applications. An extensive list of vehicles and their operational environments based on international standards and literature reviews was defined and used as the main input for the exemplary search. The AI-supported search yielded approximately 60,000 results, which were subjected to an initial relevance assessment. For the relevant papers, a neighbourhood analysis based on citation and reference networks was conducted. The final selection of papers, covering the period from 2013 to 2023, included 713 papers assessed after the initial review. An extensive discussion of the results is provided, including their categorisation based on search terms, publication years, and cluster analysis of powertrains, as well as operational environments of the vehicles involved. This case study illustrates the effectiveness of the proposed methodology and serves as a starting point for future research. The results demonstrate the potential of AI-based tools to enhance productivity when searching for scientific papers.https://www.mdpi.com/2304-6775/12/4/49search strategymethodologyartificial intelligenceliterature reviewbattery electric vehicleshydrogen-powered vehicles |
| spellingShingle | Florian Wätzold Bartosz Popiela Jonas Mayer Methodology for AI-Based Search Strategy of Scientific Papers: Exemplary Search for Hybrid and Battery Electric Vehicles in the Semantic Scholar Database Publications search strategy methodology artificial intelligence literature review battery electric vehicles hydrogen-powered vehicles |
| title | Methodology for AI-Based Search Strategy of Scientific Papers: Exemplary Search for Hybrid and Battery Electric Vehicles in the Semantic Scholar Database |
| title_full | Methodology for AI-Based Search Strategy of Scientific Papers: Exemplary Search for Hybrid and Battery Electric Vehicles in the Semantic Scholar Database |
| title_fullStr | Methodology for AI-Based Search Strategy of Scientific Papers: Exemplary Search for Hybrid and Battery Electric Vehicles in the Semantic Scholar Database |
| title_full_unstemmed | Methodology for AI-Based Search Strategy of Scientific Papers: Exemplary Search for Hybrid and Battery Electric Vehicles in the Semantic Scholar Database |
| title_short | Methodology for AI-Based Search Strategy of Scientific Papers: Exemplary Search for Hybrid and Battery Electric Vehicles in the Semantic Scholar Database |
| title_sort | methodology for ai based search strategy of scientific papers exemplary search for hybrid and battery electric vehicles in the semantic scholar database |
| topic | search strategy methodology artificial intelligence literature review battery electric vehicles hydrogen-powered vehicles |
| url | https://www.mdpi.com/2304-6775/12/4/49 |
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