Publication
Machine Reading at Scale: A Search Engine for Scientific and Academic Research
| dc.contributor.author | Sousa, Norberto | |
| dc.contributor.author | Oliveira, Nuno | |
| dc.contributor.author | Praça, Isabel | |
| dc.date.accessioned | 2023-01-25T11:33:03Z | |
| dc.date.available | 2023-01-25T11:33:03Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | The Internet, much like our universe, is ever-expanding. Information, in the most varied formats, is continuously added to the point of information overload. Consequently, the ability to navigate this ocean of data is crucial in our day-to-day lives, with familiar tools such as search engines carving a path through this unknown. In the research world, articles on a myriad of topics with distinct complexity levels are published daily, requiring specialized tools to facilitate the access and assessment of the information within. Recent endeavors in artificial intelligence, and in natural language processing in particular, can be seen as potential solutions for breaking information overload and provide enhanced search mechanisms by means of advanced algorithms. As the advent of transformer-based language models contributed to a more comprehensive analysis of both text-encoded intents and true document semantic meaning, there is simultaneously a need for additional computational resources. Information retrieval methods can act as low-complexity, yet reliable, filters to feed heavier algorithms, thus reducing computational requirements substantially. In this work, a new search engine is proposed, addressing machine reading at scale in the context of scientific and academic research. It combines state-of-the-art algorithms for information retrieval and reading comprehension tasks to extract meaningful answers from a corpus of scientific documents. The solution is then tested on two current and relevant topics, cybersecurity and energy, proving that the system is able to perform under distinct knowledge domains while achieving competent performance. | pt_PT |
| dc.description.sponsorship | This work has received funding from the following projects: UIDB/00760/2020 and UIDP/00760/2020. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.3390/systems10020043 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/21850 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | MDPI | pt_PT |
| dc.relation | Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development | |
| dc.relation | Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development | |
| dc.relation.publisherversion | https://www.mdpi.com/2079-8954/10/2/43 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Natural language processing | pt_PT |
| dc.subject | Deep learning | pt_PT |
| dc.subject | Question answering system | pt_PT |
| dc.subject | Reading comprehension | pt_PT |
| dc.subject | Information retrieva | pt_PT |
| dc.subject | Machine reading at scale | pt_PT |
| dc.title | Machine Reading at Scale: A Search Engine for Scientific and Academic Research | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development | |
| oaire.awardTitle | Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00760%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00760%2F2020/PT | |
| oaire.citation.issue | 2 | pt_PT |
| oaire.citation.startPage | 43 | pt_PT |
| oaire.citation.title | Systems | pt_PT |
| oaire.citation.volume | 10 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Gomes Lopes de Sousa | |
| person.familyName | Oliveira | |
| person.familyName | Praça | |
| person.givenName | Norberto João | |
| person.givenName | Nuno | |
| person.givenName | Isabel | |
| person.identifier | 299522 | |
| person.identifier.ciencia-id | 091D-1166-0B2E | |
| person.identifier.ciencia-id | 3E1B-B728-9524 | |
| person.identifier.ciencia-id | C710-4218-1BFF | |
| person.identifier.orcid | 0000-0003-2919-4817 | |
| person.identifier.orcid | 0000-0002-5030-7751 | |
| person.identifier.orcid | 0000-0002-2519-9859 | |
| person.identifier.rid | K-8430-2014 | |
| person.identifier.scopus-author-id | 22734900800 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | 0e10d289-1e87-4729-9e31-b2c274b3f6c4 | |
| relation.isAuthorOfPublication | e49f38bc-accb-44eb-8f49-7e7e555f34a5 | |
| relation.isAuthorOfPublication | ee4ecacd-c6c6-41e8-bca1-21a60ff05f50 | |
| relation.isAuthorOfPublication.latestForDiscovery | e49f38bc-accb-44eb-8f49-7e7e555f34a5 | |
| relation.isProjectOfPublication | db3e2edb-b8af-487a-b76a-f6790ac2d86e | |
| relation.isProjectOfPublication | 6eb94c83-adf9-4d9d-a75c-be95f44e3ca5 | |
| relation.isProjectOfPublication.latestForDiscovery | db3e2edb-b8af-487a-b76a-f6790ac2d86e |