A framework for semi-automatic identification, disambiguation and storage of protein-related abbreviations in scientific literature

Daniele Toti; Paolo Atzeni; Fabio Polticelli

doi:10.1109/ICDEW.2011.5767646

A framework for semi-automatic identification, disambiguation and storage of protein-related abbreviations in scientific literature

Daniele Toti, Paolo Atzeni, Fabio Polticelli

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Citations (Scopus)

Abstract

We propose a framework for identifying, disambiguating and storing protein-related abbreviations as found in the full texts of scientific papers, in order to build and maintain a publicly available abbreviation repository via a semi-automatic process. This process involves information extraction methods and techniques for acronym identification and resolution, based on lexical clues and syntactical, largely domain-independent criteria. A dictionary and an ontology for proteins provide the means for matching and disambiguating the biological entities. User feedback is gathered at the end of the process and the confirmed entries are then stored and made available to the scientific community for further reviewing. © 2011 IEEE.

Original language	English
Title of host publication	Proceedings - International Conference on Data Engineering
Pages	59-61
Number of pages	3
DOIs	https://doi.org/10.1109/ICDEW.2011.5767646
Publication status	Published - 2011
Event	2011 IEEE 27th International Conference on Data Engineering Workshops, ICDE 2011 - Hannover, deu Duration: 11 Apr 2011 → 16 Apr 2011

Conference

Conference	2011 IEEE 27th International Conference on Data Engineering Workshops, ICDE 2011
City	Hannover, deu
Period	11/4/11 → 16/4/11

Keywords

abbreviations

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10.1109/ICDEW.2011.5767646

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@inproceedings{b9287d172c194d7e9389192d5af2b51a,

title = "A framework for semi-automatic identification, disambiguation and storage of protein-related abbreviations in scientific literature",

abstract = "We propose a framework for identifying, disambiguating and storing protein-related abbreviations as found in the full texts of scientific papers, in order to build and maintain a publicly available abbreviation repository via a semi-automatic process. This process involves information extraction methods and techniques for acronym identification and resolution, based on lexical clues and syntactical, largely domain-independent criteria. A dictionary and an ontology for proteins provide the means for matching and disambiguating the biological entities. User feedback is gathered at the end of the process and the confirmed entries are then stored and made available to the scientific community for further reviewing. {\textcopyright} 2011 IEEE.",

keywords = "abbreviations, abbreviations",

author = "Daniele Toti and Paolo Atzeni and Fabio Polticelli",

year = "2011",

doi = "10.1109/ICDEW.2011.5767646",

language = "English",

isbn = "978-1-4244-9195-7",

pages = "59--61",

booktitle = "Proceedings - International Conference on Data Engineering",

note = "2011 IEEE 27th International Conference on Data Engineering Workshops, ICDE 2011 ; Conference date: 11-04-2011 Through 16-04-2011",

}

Toti, D, Atzeni, P & Polticelli, F 2011, A framework for semi-automatic identification, disambiguation and storage of protein-related abbreviations in scientific literature. in Proceedings - International Conference on Data Engineering. pp. 59-61, 2011 IEEE 27th International Conference on Data Engineering Workshops, ICDE 2011, Hannover, deu, 11/4/11. https://doi.org/10.1109/ICDEW.2011.5767646

TY - GEN

T1 - A framework for semi-automatic identification, disambiguation and storage of protein-related abbreviations in scientific literature

AU - Toti, Daniele

AU - Atzeni, Paolo

AU - Polticelli, Fabio

PY - 2011

Y1 - 2011

N2 - We propose a framework for identifying, disambiguating and storing protein-related abbreviations as found in the full texts of scientific papers, in order to build and maintain a publicly available abbreviation repository via a semi-automatic process. This process involves information extraction methods and techniques for acronym identification and resolution, based on lexical clues and syntactical, largely domain-independent criteria. A dictionary and an ontology for proteins provide the means for matching and disambiguating the biological entities. User feedback is gathered at the end of the process and the confirmed entries are then stored and made available to the scientific community for further reviewing. © 2011 IEEE.

AB - We propose a framework for identifying, disambiguating and storing protein-related abbreviations as found in the full texts of scientific papers, in order to build and maintain a publicly available abbreviation repository via a semi-automatic process. This process involves information extraction methods and techniques for acronym identification and resolution, based on lexical clues and syntactical, largely domain-independent criteria. A dictionary and an ontology for proteins provide the means for matching and disambiguating the biological entities. User feedback is gathered at the end of the process and the confirmed entries are then stored and made available to the scientific community for further reviewing. © 2011 IEEE.

KW - abbreviations

UR - http://hdl.handle.net/10807/163320

U2 - 10.1109/ICDEW.2011.5767646

DO - 10.1109/ICDEW.2011.5767646

M3 - Conference contribution

SN - 978-1-4244-9195-7

SP - 59

EP - 61

BT - Proceedings - International Conference on Data Engineering

T2 - 2011 IEEE 27th International Conference on Data Engineering Workshops, ICDE 2011

Y2 - 11 April 2011 through 16 April 2011

ER -

A framework for semi-automatic identification, disambiguation and storage of protein-related abbreviations in scientific literature

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