| 1. Identity statement | ||||
| Reference Type | Conference Paper (Conference Proceedings) | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | QABCDSTQQW/46B5A55 | |||
| Repository | urlib.net/www/2022/02.08.11.29 (restricted access) | |||
| Last Update | 2022:02.08.11.29.49 (UTC) simone | |||
| Metadata Repository | urlib.net/www/2022/02.08.11.29.49 | |||
| Metadata Last Update | 2022:04.03.23.14.43 (UTC) administrator | |||
| Secondary Key | INPE--PRE/ | |||
| DOI | 10.1109/CLEI53233.2021.9639968 | |||
| ISBN | 978-166549503-5 | |||
| Citation Key | SantosSSFWCMAV:2021:ExSyMa | |||
| Title | Using Open Information Extraction to Extract Relations: An Extended Systematic Mapping  | |||
| Year | 2021 | |||
| Access Date | 2024, May 09 | |||
| Secondary Type | PRE CI | |||
| Number of Files | 1 | |||
| Size | 1558 KiB | |||
| 2. Context | ||||
| Author | 1 Santos, Vinícius dos 2 Silva, Patrick R. 3 Souza, Érica F. de 4 Felizardo, Katia R. 5 Watanabe, Willian M. 6 Cândido Júnior, Arnaldo 7 Meinerz, Giovani V. 8 Aluísio, Sandra M. 9 Vijaykumar, Nandamudi Lankalapalli | |||
| Resume Identifier | 1 2 3 4 5 6 7 8 9 8JMKD3MGP5W/3C9JHTU | |||
| Group | 1 2 3 4 5 6 7 8 9 COPDT-CGIP-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 Universidade de São Paulo (USP) 2 Universidade Tecnológica Federal do Paraná (UTFPR) 3 Universidade Tecnológica Federal do Paraná (UTFPR) 4 Universidade Tecnológica Federal do Paraná (UTFPR) 5 Universidade Tecnológica Federal do Paraná (UTFPR) 6 Universidade Tecnológica Federal do Paraná (UTFPR) 7 Universidade Tecnológica Federal do Paraná (UTFPR) 8 Universidade de São Paulo (USP) 9 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Author e-Mail Address | 1 vinicius.dos.santos@usp.br 2 patrick.1985@alunos.utfpr.edu.br 3 ericasouza@utfpr.edu.br 4 katiascannavino@utfpr.edu.br 5 wwatanabe@utfpr.edu.br 6 arnaldoc@utfpr.edu.br 7 giovanimeinerz@utfpr.edu.br 8 sandra@icmc.usp.br 9 vijaykumar.nandamudi@gmail.com | |||
| Conference Name | Latin American Computing Conference, 47 | |||
| Conference Location | Online | |||
| Date | 25-29 Oct. 2021 | |||
| Publisher | IEEE | |||
| Book Title | Proceedings | |||
| History (UTC) | 2022-02-08 11:30:43 :: simone -> administrator :: 2021 2022-04-03 23:14:43 :: administrator -> simone :: 2021 | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Content Type | External Contribution | |||
| Version Type | publisher | |||
| Abstract | Context: For thousands of years humans have been using natural language to register their knowledge on important information to enable its access to future generations. With internet, a large amount of textual data is produced and shared on a daily basis. So, scientists started to research techniques for efficiently process knowledge stored in textual format. In this context, Natural Language Processing (NLP) became a popular area studying linguistic phenomena and using computational methods to process texts in natural language. In particular, Open Information Extraction (Open IE) was proposed to gather information from plain text. Despite the advances in this area, it is still necessary to map details about how these approaches were proposed to support the community while creating more efficient Open IE systems. Objective: In this paper, we identify, in the literature, the main characteristics of proposed Open IE approaches. Method: First, we extended the search performed in a systematic mapping previously published by using backward snowballing and a manual search. Next, we updated the electronic database search including ACL Anthology. Finally, 159 studies proposing Open IE approaches were considered for data extraction. Results: Data analysis showed a significant increase in the number of studies published about Open IE in the last years. In addition, we provide important details about how these techniques were proposed (e.g., data sets used and output evaluation techniques). Results indicate that researchers started to adopt neural networks to perform Open IE instead of using conventional supervised learning techniques. Conclusion: Recent advances in Artificial Intelligence and neural networks techniques allowed scientists to have a new perspective on how to perform efficient textual data management. Therefore, Open IE approaches gained much attention as they can help in many contexts, especially in knowledge management tasks. | |||
| Area | COMP | |||
| Arrangement | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > Using Open Information... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| Language | en | |||
| Target File | Santos_2021_using.pdf | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Read Permission | deny from all | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Mirror Repository | urlib.net/www/2021/06.04.03.40.25 | |||
| Next Higher Units | 8JMKD3MGPCW/46KUES5 | |||
| Citing Item List | sid.inpe.br/bibdigital/2022/04.03.23.11 1 sid.inpe.br/mtc-m21/2012/07.13.14.56.50 1 | |||
| Host Collection | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notes | ||||
| Empty Fields | archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination e-mailaddress edition editor format issn keywords label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisheraddress rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url volume | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||