Explanation Ontology in Action: A Clinical Use-Case

Explanation Ontology in Action: A Clinical Use-Case

We addressed the problem of a lack of semantic representation for user-centric explanations and different explanation types in our Explanation Ontology (https://purl.org/heals/eo). Such a representation is increasingly necessary as explainability has become an important problem in Artificial Intelli...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org 2020-10
Hauptverfasser: Chari, Shruthi, Seneviratne, Oshani, Gruen, Daniel M, eman, Morgan A, Das, Amar K, McGuinness, Deborah L
Format: Artikel
Sprache:eng
Schlagworte:
Artificial intelligence
Knowledge representation
Ontology
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Chari, Shruthi
Seneviratne, Oshani
Gruen, Daniel M
eman, Morgan A
Das, Amar K
McGuinness, Deborah L
description We addressed the problem of a lack of semantic representation for user-centric explanations and different explanation types in our Explanation Ontology (https://purl.org/heals/eo). Such a representation is increasingly necessary as explainability has become an important problem in Artificial Intelligence with the emergence of complex methods and an uptake in high-precision and user-facing settings. In this submission, we provide step-by-step guidance for system designers to utilize our ontology, introduced in our resource track paper, to plan and model for explanations during the design of their Artificial Intelligence systems. We also provide a detailed example with our utilization of this guidance in a clinical setting.
format Article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2448765204</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2448765204</sourcerecordid><originalsourceid>FETCH-proquest_journals_24487652043</originalsourceid><addsrcrecordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mQwdq0oyEnMSyzJzM9T8M8ryc_JT69UyMxTcEwGCVkpOCo452TmZSYn5iiEFqfqOicWp_IwsKYl5hSn8kJpbgZlN9cQZw_dgqL8wtLU4pL4rPzSojygVLyRiYmFuZmpkYGJMXGqAL85M08</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2448765204</pqid></control><display><type>article</type><title>Explanation Ontology in Action: A Clinical Use-Case</title><source>Free E- Journals</source><creator>Chari, Shruthi ; Seneviratne, Oshani ; Gruen, Daniel M ; eman, Morgan A ; Das, Amar K ; McGuinness, Deborah L</creator><creatorcontrib>Chari, Shruthi ; Seneviratne, Oshani ; Gruen, Daniel M ; eman, Morgan A ; Das, Amar K ; McGuinness, Deborah L</creatorcontrib><description>We addressed the problem of a lack of semantic representation for user-centric explanations and different explanation types in our Explanation Ontology (https://purl.org/heals/eo). Such a representation is increasingly necessary as explainability has become an important problem in Artificial Intelligence with the emergence of complex methods and an uptake in high-precision and user-facing settings. In this submission, we provide step-by-step guidance for system designers to utilize our ontology, introduced in our resource track paper, to plan and model for explanations during the design of their Artificial Intelligence systems. We also provide a detailed example with our utilization of this guidance in a clinical setting.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Artificial intelligence ; Knowledge representation ; Ontology</subject><ispartof>arXiv.org, 2020-10</ispartof><rights>2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>777,781</link.rule.ids></links><search><creatorcontrib>Chari, Shruthi</creatorcontrib><creatorcontrib>Seneviratne, Oshani</creatorcontrib><creatorcontrib>Gruen, Daniel M</creatorcontrib><creatorcontrib>eman, Morgan A</creatorcontrib><creatorcontrib>Das, Amar K</creatorcontrib><creatorcontrib>McGuinness, Deborah L</creatorcontrib><title>Explanation Ontology in Action: A Clinical Use-Case</title><title>arXiv.org</title><description>We addressed the problem of a lack of semantic representation for user-centric explanations and different explanation types in our Explanation Ontology (https://purl.org/heals/eo). Such a representation is increasingly necessary as explainability has become an important problem in Artificial Intelligence with the emergence of complex methods and an uptake in high-precision and user-facing settings. In this submission, we provide step-by-step guidance for system designers to utilize our ontology, introduced in our resource track paper, to plan and model for explanations during the design of their Artificial Intelligence systems. We also provide a detailed example with our utilization of this guidance in a clinical setting.</description><subject>Artificial intelligence</subject><subject>Knowledge representation</subject><subject>Ontology</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mQwdq0oyEnMSyzJzM9T8M8ryc_JT69UyMxTcEwGCVkpOCo452TmZSYn5iiEFqfqOicWp_IwsKYl5hSn8kJpbgZlN9cQZw_dgqL8wtLU4pL4rPzSojygVLyRiYmFuZmpkYGJMXGqAL85M08</recordid><startdate>20201004</startdate><enddate>20201004</enddate><creator>Chari, Shruthi</creator><creator>Seneviratne, Oshani</creator><creator>Gruen, Daniel M</creator><creator>eman, Morgan A</creator><creator>Das, Amar K</creator><creator>McGuinness, Deborah L</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20201004</creationdate><title>Explanation Ontology in Action: A Clinical Use-Case</title><author>Chari, Shruthi ; Seneviratne, Oshani ; Gruen, Daniel M ; eman, Morgan A ; Das, Amar K ; McGuinness, Deborah L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_24487652043</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial intelligence</topic><topic>Knowledge representation</topic><topic>Ontology</topic><toplevel>online_resources</toplevel><creatorcontrib>Chari, Shruthi</creatorcontrib><creatorcontrib>Seneviratne, Oshani</creatorcontrib><creatorcontrib>Gruen, Daniel M</creatorcontrib><creatorcontrib>eman, Morgan A</creatorcontrib><creatorcontrib>Das, Amar K</creatorcontrib><creatorcontrib>McGuinness, Deborah L</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chari, Shruthi</au><au>Seneviratne, Oshani</au><au>Gruen, Daniel M</au><au>eman, Morgan A</au><au>Das, Amar K</au><au>McGuinness, Deborah L</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Explanation Ontology in Action: A Clinical Use-Case</atitle><jtitle>arXiv.org</jtitle><date>2020-10-04</date><risdate>2020</risdate><eissn>2331-8422</eissn><abstract>We addressed the problem of a lack of semantic representation for user-centric explanations and different explanation types in our Explanation Ontology (https://purl.org/heals/eo). Such a representation is increasingly necessary as explainability has become an important problem in Artificial Intelligence with the emergence of complex methods and an uptake in high-precision and user-facing settings. In this submission, we provide step-by-step guidance for system designers to utilize our ontology, introduced in our resource track paper, to plan and model for explanations during the design of their Artificial Intelligence systems. We also provide a detailed example with our utilization of this guidance in a clinical setting.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2020-10
issn 2331-8422
language eng
recordid cdi_proquest_journals_2448765204
source Free E- Journals
subjects Artificial intelligence
Knowledge representation
Ontology
title Explanation Ontology in Action: A Clinical Use-Case
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T22%3A29%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Explanation%20Ontology%20in%20Action:%20A%20Clinical%20Use-Case&rft.jtitle=arXiv.org&rft.au=Chari,%20Shruthi&rft.date=2020-10-04&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2448765204%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2448765204&rft_id=info:pmid/&rfr_iscdi=true