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|Author=Mehwish Alam
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|Author=Harald Sack
 
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|Year=2020
 
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|Month=April
 
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|Note=Technical Report
 
|Booktitle=Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG2020) co-located with the 17th Extended Semantic Web Conference 2020 (ESWC 2020)
 
|Booktitle=Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG2020) co-located with the 17th Extended Semantic Web Conference 2020 (ESWC 2020)
 
|Publisher=CEUR Workshop Proceedings
 
|Publisher=CEUR Workshop Proceedings
 
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{{Publikation Details
 
{{Publikation Details
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|Abstract=Most Knowledge Graphs (KGs) contain textual descriptions of entities in various natural languages. These descriptions of entities provide valuable information that may not be explicitly represented in the structured part of the KG. Based on this fact, some link prediction methods which make use of the information presented in the textual descriptions of entities have been proposed to learn representations of (monolingual) KGs. However, these methods use entity descriptions in only one language and ignore the fact that descriptions given in differ- ent languages may provide complementary information and thereby also additional semantics. In this position paper, the problem of effectively leveraging multilingual entity descriptions for the purpose of link pre- diction in KGs will be discussed along with potential solutions to the problem.
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|Download=Semantic Entity Enrichment by Leveraging Multilingual Descriptions for Link Prediction.pdf
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|Link=https://ceur-ws.org/Vol-2635/paper7.pdf, https://arxiv.org/abs/2004.10640
 
|Forschungsgruppe=Information Service Engineering
 
|Forschungsgruppe=Information Service Engineering
 
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Aktuelle Version vom 17. November 2022, 12:40 Uhr


Semantic Entity Enrichment by Leveraging Multilingual Descriptions for Link Prediction


Semantic Entity Enrichment by Leveraging Multilingual Descriptions for Link Prediction



Published: 2020 April

Buchtitel: Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG2020) co-located with the 17th Extended Semantic Web Conference 2020 (ESWC 2020)
Verlag: CEUR Workshop Proceedings

Referierte Veröffentlichung
Note: Technical Report

BibTeX

Kurzfassung
Most Knowledge Graphs (KGs) contain textual descriptions of entities in various natural languages. These descriptions of entities provide valuable information that may not be explicitly represented in the structured part of the KG. Based on this fact, some link prediction methods which make use of the information presented in the textual descriptions of entities have been proposed to learn representations of (monolingual) KGs. However, these methods use entity descriptions in only one language and ignore the fact that descriptions given in differ- ent languages may provide complementary information and thereby also additional semantics. In this position paper, the problem of effectively leveraging multilingual entity descriptions for the purpose of link pre- diction in KGs will be discussed along with potential solutions to the problem.

Download: Media:Semantic Entity Enrichment by Leveraging Multilingual Descriptions for Link Prediction.pdf
Weitere Informationen unter: LinkLink



Forschungsgruppe

Information Service Engineering


Forschungsgebiet