Latent Dirichlet Allocation and t-Distributed Stochastic Neighbor Embedding Enhance Scientific Reading Comprehension of Articles Related to Enterprise Architecture
| العنوان: | Latent Dirichlet Allocation and t-Distributed Stochastic Neighbor Embedding Enhance Scientific Reading Comprehension of Articles Related to Enterprise Architecture |
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| المؤلفون: | Rüdiger Buchkremer, Fabian Gampfer, Nils Horn |
| المصدر: | AI Volume 2 Issue 2 Pages 11-194 AI, Vol 2, Iss 11, Pp 179-194 (2021) |
| بيانات النشر: | MDPI AG, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | 0209 industrial biotechnology, Modeling language, Computer science, media_common.quotation_subject, Enterprise architecture, text mining, 02 engineering and technology, Latent Dirichlet allocation, symbols.namesake, 020901 industrial engineering & automation, Reading (process), 0202 electrical engineering, electronic engineering, information engineering, natural language processing, tf–idf, General Environmental Science, media_common, Information retrieval, GRASP, latent dirichlet allocation, 020207 software engineering, QA75.5-76.95, reading comprehension, t-distributed stochastic neighbor embedding, Reading comprehension, Electronic computers. Computer science, enterprise architecture, symbols, General Earth and Planetary Sciences |
| الوصف: | As the amount of scientific information increases steadily, it is crucial to improve fast-reading comprehension. To grasp many scientific articles in a short period, artificial intelligence becomes essential. This paper aims to apply artificial intelligence methodologies to examine broad topics such as enterprise architecture in scientific articles. Analyzing abstracts with latent dirichlet allocation or inverse document frequency appears to be more beneficial than exploring full texts. Furthermore, we demonstrate that t-distributed stochastic neighbor embedding is well suited to explore the degree of connectivity to neighboring topics, such as complexity theory. Artificial intelligence produces results that are similar to those obtained by manual reading. Our full-text study confirms enterprise architecture trends such as sustainability and modeling languages. |
| وصف الملف: | application/pdf |
| تدمد: | 2673-2688 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f28f6cc2e8fc05e70dfbd281a8fb0a68 https://doi.org/10.3390/ai2020011 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....f28f6cc2e8fc05e70dfbd281a8fb0a68 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 26732688 |
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