A Search Engine for Scientific Publications: a Cybersecurity Case Study
This work addresses the challenge for cybersecurity researchers and practitioners in efficiently navigating a large volume of scientific publications, though it is incremental as it builds on existing information retrieval and reading comprehension methods.
The authors tackled the problem of finding relevant information in the vast and interdisciplinary field of cybersecurity by proposing a new search engine that combines information retrieval and reading comprehension algorithms to extract answers from domain-specific documents, achieving great generalization capabilities that allow easy adaptation to other knowledge domains.
Cybersecurity is a very challenging topic of research nowadays, as digitalization increases the interaction of people, software and services on the Internet by means of technology devices and networks connected to it. The field is broad and has a lot of unexplored ground under numerous disciplines such as management, psychology, and data science. Its large disciplinary spectrum and many significant research topics generate a considerable amount of information, making it hard for us to find what we are looking for when researching a particular subject. This work proposes a new search engine for scientific publications which combines both information retrieval and reading comprehension algorithms to extract answers from a collection of domain-specific documents. The proposed solution although being applied to the context of cybersecurity exhibited great generalization capabilities and can be easily adapted to perform under other distinct knowledge domains.