Knowledge graph has made impressive progress and is an important resource in artificial intelligence domain. I am researching on knowledge graph embedding, which is representing the entities and relations in the knowledge graph as vectors. Now, I want to introduce knowledge map as a resource to other natural language processing tasks. What interesting areas do you think I can try? Such as text semantic matching, text classification, and so on.
Dear Zhengang Zhang ,
Look the link, maybe useful.
https://towardsdatascience.com/knowledge-graphs-in-natural-language-processing-acl-2021-6cac04f39761
Regards,
Shafagat
Hi, Zhengang Zhang
I think that work on knowledge graph embedding should be done along with semantic or ontology based work. Extraction of relations, classification of relations is also important challenge in any language.
I suggest you to review these papers for your study.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9165716
https://arxiv.org/pdf/2011.01103.pdf
https://aclanthology.org/2022.aacl-main.46.pdf
https://aclanthology.org/2021.tacl-1.11.pdf
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