Movatterモバイル変換

Ontology alignment in the biomedical domain using entity definitions and context

Lucy Lu Wang,Chandra Bhagavatula,Mark Neumann,Kyle Lo,Chris Wilhelm,Waleed Ammar

Correct Metadata for

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. Seeour corrections guidelines if you need to change the PDF.

TitleAdjust the title. Retain tags such as <fixed-case>.

AuthorsAdjust author names and order to match the PDF.

AbstractCorrect abstract if needed. Retain XML formatting tags such as <tex-math>.

Verification against PDFEnsure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consultthe PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, resulting in a need to de-duplicate entities when merging ontologies. We propose a method for enriching entities in an ontology with external definition and context information, and use this additional information for ontology alignment. We develop a neural architecture capable of encoding the additional information when available, and show that the addition of external data results in an F1-score of 0.69 on the Ontology Alignment Evaluation Initiative (OAEI) largebio SNOMED-NCI subtask, comparable with the entity-level matchers in a SOTA system.

Anthology ID:: W18-2306
Volume:: Proceedings of the BioNLP 2018 workshop
Month:: July
Year:: 2018
Address:: Melbourne, Australia
Editors:: Dina Demner-Fushman,Kevin Bretonnel Cohen,Sophia Ananiadou,Junichi Tsujii
Venue:: BioNLP
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 47–55
Language:
URL:: https://aclanthology.org/W18-2306/
DOI:: 10.18653/v1/W18-2306
Bibkey:
Cite (ACL):: Lucy Lu Wang, Chandra Bhagavatula, Mark Neumann, Kyle Lo, Chris Wilhelm, and Waleed Ammar. 2018.Ontology alignment in the biomedical domain using entity definitions and context. InProceedings of the BioNLP 2018 workshop, pages 47–55, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):: Ontology alignment in the biomedical domain using entity definitions and context (Wang et al., BioNLP 2018)
Copy Citation:
PDF:: https://aclanthology.org/W18-2306.pdf
Code: allenai/ontoemma
Data: Semantic Scholar

PDF Cite Search Code Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{wang-etal-2018-ontology,    title = "Ontology alignment in the biomedical domain using entity definitions and context",    author = "Wang, Lucy Lu  and      Bhagavatula, Chandra  and      Neumann, Mark  and      Lo, Kyle  and      Wilhelm, Chris  and      Ammar, Waleed",    editor = "Demner-Fushman, Dina  and      Cohen, Kevin Bretonnel  and      Ananiadou, Sophia  and      Tsujii, Junichi",    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",    month = jul,    year = "2018",    address = "Melbourne, Australia",    publisher = "Association for Computational Linguistics",    url = "https://aclanthology.org/W18-2306/",    doi = "10.18653/v1/W18-2306",    pages = "47--55",    abstract = "Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, resulting in a need to de-duplicate entities when merging ontologies. We propose a method for enriching entities in an ontology with external definition and context information, and use this additional information for ontology alignment. We develop a neural architecture capable of encoding the additional information when available, and show that the addition of external data results in an F1-score of 0.69 on the Ontology Alignment Evaluation Initiative (OAEI) largebio SNOMED-NCI subtask, comparable with the entity-level matchers in a SOTA system."}

Download as File

<?xml version="1.0" encoding="UTF-8"?><modsCollection xmlns="http://www.loc.gov/mods/v3"><mods ID="wang-etal-2018-ontology">    <titleInfo>        <title>Ontology alignment in the biomedical domain using entity definitions and context</title>    </titleInfo>    <name type="personal">        <namePart type="given">Lucy</namePart>        <namePart type="given">Lu</namePart>        <namePart type="family">Wang</namePart>        <role>            <roleTerm authority="marcrelator" type="text">author</roleTerm>        </role>    </name>    <name type="personal">        <namePart type="given">Chandra</namePart>        <namePart type="family">Bhagavatula</namePart>        <role>            <roleTerm authority="marcrelator" type="text">author</roleTerm>        </role>    </name>    <name type="personal">        <namePart type="given">Mark</namePart>        <namePart type="family">Neumann</namePart>        <role>            <roleTerm authority="marcrelator" type="text">author</roleTerm>        </role>    </name>    <name type="personal">        <namePart type="given">Kyle</namePart>        <namePart type="family">Lo</namePart>        <role>            <roleTerm authority="marcrelator" type="text">author</roleTerm>        </role>    </name>    <name type="personal">        <namePart type="given">Chris</namePart>        <namePart type="family">Wilhelm</namePart>        <role>            <roleTerm authority="marcrelator" type="text">author</roleTerm>        </role>    </name>    <name type="personal">        <namePart type="given">Waleed</namePart>        <namePart type="family">Ammar</namePart>        <role>            <roleTerm authority="marcrelator" type="text">author</roleTerm>        </role>    </name>    <originInfo>        <dateIssued>2018-07</dateIssued>    </originInfo>    <typeOfResource>text</typeOfResource>    <relatedItem type="host">        <titleInfo>            <title>Proceedings of the BioNLP 2018 workshop</title>        </titleInfo>        <name type="personal">            <namePart type="given">Dina</namePart>            <namePart type="family">Demner-Fushman</namePart>            <role>                <roleTerm authority="marcrelator" type="text">editor</roleTerm>            </role>        </name>        <name type="personal">            <namePart type="given">Kevin</namePart>            <namePart type="given">Bretonnel</namePart>            <namePart type="family">Cohen</namePart>            <role>                <roleTerm authority="marcrelator" type="text">editor</roleTerm>            </role>        </name>        <name type="personal">            <namePart type="given">Sophia</namePart>            <namePart type="family">Ananiadou</namePart>            <role>                <roleTerm authority="marcrelator" type="text">editor</roleTerm>            </role>        </name>        <name type="personal">            <namePart type="given">Junichi</namePart>            <namePart type="family">Tsujii</namePart>            <role>                <roleTerm authority="marcrelator" type="text">editor</roleTerm>            </role>        </name>        <originInfo>            <publisher>Association for Computational Linguistics</publisher>            <place>                <placeTerm type="text">Melbourne, Australia</placeTerm>            </place>        </originInfo>        <genre authority="marcgt">conference publication</genre>    </relatedItem>    <abstract>Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, resulting in a need to de-duplicate entities when merging ontologies. We propose a method for enriching entities in an ontology with external definition and context information, and use this additional information for ontology alignment. We develop a neural architecture capable of encoding the additional information when available, and show that the addition of external data results in an F1-score of 0.69 on the Ontology Alignment Evaluation Initiative (OAEI) largebio SNOMED-NCI subtask, comparable with the entity-level matchers in a SOTA system.</abstract>    <identifier type="citekey">wang-etal-2018-ontology</identifier>    <identifier type="doi">10.18653/v1/W18-2306</identifier>    <location>        <url>https://aclanthology.org/W18-2306/</url>    </location>    <part>        <date>2018-07</date>        <extent unit="page">            <start>47</start>            <end>55</end>        </extent>    </part></mods></modsCollection>

Download as File

%0 Conference Proceedings%T Ontology alignment in the biomedical domain using entity definitions and context%A Wang, Lucy Lu%A Bhagavatula, Chandra%A Neumann, Mark%A Lo, Kyle%A Wilhelm, Chris%A Ammar, Waleed%Y Demner-Fushman, Dina%Y Cohen, Kevin Bretonnel%Y Ananiadou, Sophia%Y Tsujii, Junichi%S Proceedings of the BioNLP 2018 workshop%D 2018%8 July%I Association for Computational Linguistics%C Melbourne, Australia%F wang-etal-2018-ontology%X Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, resulting in a need to de-duplicate entities when merging ontologies. We propose a method for enriching entities in an ontology with external definition and context information, and use this additional information for ontology alignment. We develop a neural architecture capable of encoding the additional information when available, and show that the addition of external data results in an F1-score of 0.69 on the Ontology Alignment Evaluation Initiative (OAEI) largebio SNOMED-NCI subtask, comparable with the entity-level matchers in a SOTA system.%R 10.18653/v1/W18-2306%U https://aclanthology.org/W18-2306/%U https://doi.org/10.18653/v1/W18-2306%P 47-55

Download as File

Markdown (Informal)

[Ontology alignment in the biomedical domain using entity definitions and context](https://aclanthology.org/W18-2306/) (Wang et al., BioNLP 2018)

Ontology alignment in the biomedical domain using entity definitions and context (Wang et al., BioNLP 2018)

ACL

Lucy Lu Wang, Chandra Bhagavatula, Mark Neumann, Kyle Lo, Chris Wilhelm, and Waleed Ammar. 2018.Ontology alignment in the biomedical domain using entity definitions and context. InProceedings of the BioNLP 2018 workshop, pages 47–55, Melbourne, Australia. Association for Computational Linguistics.