Forecasting of innovation in the light of semantic networks
| Autor: | Cigdem Baskici, Yavuz Ercil, Suat Atan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Information retrieval
Computer science business.industry Innovation management 020206 networking & telecommunications Subject (documents) 02 engineering and technology Field (computer science) Semantic network Text mining 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing Product (category theory) business General Environmental Science |
| Zdroj: | Procedia Computer Science. 158:443-449 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2019.09.074 |
| Popis: | The subject of this study is a strategic and competitive global innovation management. In this frame, a study was carried to reveal the dynamics of innovations from idea to product in three steps. In the first step, semantic network analysis was used in 400 papers with the highest citations published in 20 journals with the highest h5-index in the health field from 2013 to 2015. Web scraping and text mining tools based on R language were used to create semantic network structures. Semantic network analysis revealed that the focus of the articles was on cancer. The analysis carried out using the Sankey diagram revealed that scientists who work in cancer are most frequently involved in the lung, however, the scientists who related with the lung are not focused on treatment, and heart. The experts’ comments are due to challenges in the treatment of the lung cancer scientist may be focused on areas like diagnosis and phases of cancer. In the second step, 260,000 rows of Clinical Trials cases were analyzed. In the third step, an analysis was made on a total of 1000 patent documents selected from the lens.org site, which contains the databases in global patent offices. Methods used in the analysis of the articles in the first step were carried out in these documents. According to this, diagnosis, treatment and therapy words the most common pass with lung cancer through the documents. In addition, the most cited authors in the field of lung cancer were searched in patent documents. These authors are considered as academic backgrounds that feed new technologies. According to this, it has been found that there are no major matches among the most cited authors of 400 papers and most cited authors in the patent documents in the field of lung cancer. It has been found that patents feed on more specific journals rather than major medical journals. In addition, it was also found that the most cited authors of 400 papers were different from the patent owners. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|