Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Mehwish Alam
- sl:arxiv_num : 2308.00081
- sl:arxiv_published : 2023-07-31T18:53:47Z
- sl:arxiv_summary : Embedding based Knowledge Graph (KG) Completion has gained much attention
over the past few years. Most of the current algorithms consider a KG as a
multidirectional labeled graph and lack the ability to capture the semantics
underlying the schematic information. In a separate development, a vast amount
of information has been captured within the Large Language Models (LLMs) which
has revolutionized the field of Artificial Intelligence. KGs could benefit from
these LLMs and vice versa. This vision paper discusses the existing algorithms
for KG completion based on the variations for generating KG embeddings. It
starts with discussing various KG completion algorithms such as transductive
and inductive link prediction and entity type prediction algorithms. It then
moves on to the algorithms utilizing type information within the KGs, LLMs, and
finally to algorithms capturing the semantics represented in different
description logic axioms. We conclude the paper with a critical reflection on
the current state of work in the community and give recommendations for future
directions.@en
- sl:arxiv_title : Towards Semantically Enriched Embeddings for Knowledge Graph Completion@en
- sl:arxiv_updated : 2023-07-31T18:53:47Z
- sl:bookmarkOf : https://arxiv.org/abs/2308.00081
- sl:creationDate : 2023-08-02
- sl:creationTime : 2023-08-02T16:10:37Z
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