Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | The Semantic Web |
| Untertitel | ESWC 2020 Satellite Events - ESWC 2020, Revised Selected Papers |
| Herausgeber/-innen | Andreas Harth, Valentina Presutti, Raphaël Troncy, Maribel Acosta, Axel Polleres, Javier D. Fernández, Josiane Xavier Parreira, Olaf Hartig, Katja Hose, Michael Cochez |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 152-157 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 978-3-030-62327-2 |
| ISBN (Print) | 9783030623265 |
| Publikationsstatus | Veröffentlicht - 11 Nov. 2020 |
| Veranstaltung | 17th Extended Semantic Web Conference, ESWC 2020 - Heraklion, Griechenland Dauer: 31 Mai 2020 → 4 Juni 2020 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 12124 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Diseases and their symptoms are a frequent information need for Web users. Diseases often are categorized into sub-types, manifested through different symptoms. Extracting such information from textual corpora is inherently difficult. Yet, this can be easily extracted from semi-structured resources like tables. We propose an approach for identifying tables that contain information about sub-type classifications and their attributes. Often tables have diverse and redundant schemas, hence, we align equivalent columns in disparate schemas s.t. information about diseases are accessible through a unified and a common schema. Experimental evaluation shows that we can accurately identify tables containing disease sub-type classifications and additionally align equivalent columns.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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- BibTex
- RIS
The Semantic Web: ESWC 2020 Satellite Events - ESWC 2020, Revised Selected Papers. Hrsg. / Andreas Harth; Valentina Presutti; Raphaël Troncy; Maribel Acosta; Axel Polleres; Javier D. Fernández; Josiane Xavier Parreira; Olaf Hartig; Katja Hose; Michael Cochez. Springer Science and Business Media Deutschland GmbH, 2020. S. 152-157 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12124 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
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TY - GEN
T1 - MedTable
T2 - 17th Extended Semantic Web Conference, ESWC 2020
AU - Koutraki, Maria
AU - Fetahu, Besnik
PY - 2020/11/11
Y1 - 2020/11/11
N2 - Diseases and their symptoms are a frequent information need for Web users. Diseases often are categorized into sub-types, manifested through different symptoms. Extracting such information from textual corpora is inherently difficult. Yet, this can be easily extracted from semi-structured resources like tables. We propose an approach for identifying tables that contain information about sub-type classifications and their attributes. Often tables have diverse and redundant schemas, hence, we align equivalent columns in disparate schemas s.t. information about diseases are accessible through a unified and a common schema. Experimental evaluation shows that we can accurately identify tables containing disease sub-type classifications and additionally align equivalent columns.
AB - Diseases and their symptoms are a frequent information need for Web users. Diseases often are categorized into sub-types, manifested through different symptoms. Extracting such information from textual corpora is inherently difficult. Yet, this can be easily extracted from semi-structured resources like tables. We propose an approach for identifying tables that contain information about sub-type classifications and their attributes. Often tables have diverse and redundant schemas, hence, we align equivalent columns in disparate schemas s.t. information about diseases are accessible through a unified and a common schema. Experimental evaluation shows that we can accurately identify tables containing disease sub-type classifications and additionally align equivalent columns.
UR - http://www.scopus.com/inward/record.url?scp=85097277511&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-62327-2_26
DO - 10.1007/978-3-030-62327-2_26
M3 - Conference contribution
AN - SCOPUS:85097277511
SN - 9783030623265
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 152
EP - 157
BT - The Semantic Web
A2 - Harth, Andreas
A2 - Presutti, Valentina
A2 - Troncy, Raphaël
A2 - Acosta, Maribel
A2 - Polleres, Axel
A2 - Fernández, Javier D.
A2 - Xavier Parreira, Josiane
A2 - Hartig, Olaf
A2 - Hose, Katja
A2 - Cochez, Michael
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 31 May 2020 through 4 June 2020
ER -