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
Note: Technical Report
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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