Language embeddings are a promising approach for handling natural language expressions. Current embeddings encompass a large language corpus, and need to be retrained to deal with specific sub-domains. On the other hand, these embeddings often disregard even basic domain knowledge, making them specially fragile when handling technical, specific, knowledge domains, and requiring costly retraining. To alleviate this issue, we propose a combined approach where the embedding is seen as a model of a logical knowledge base. Through a continuous learning approach, the embedding improves its satisfaction of the knowledge base, and in turn produces better training examples by labelling previously unseen text. In this position paper we describe the general framework for this continuous learning, along with its main features.

Tenti, P., Pasi, G., Penaloza, R. (2021). Complementing language embeddings with knowledge bases for specific domains. In Proceedings of the Workshop on Data meets Applied Ontologies in Explainable AI (DAO-XAI 2021) part of Bratislava Knowledge September (BAKS 2021) (pp.1-9). CEUR-WS.

Complementing language embeddings with knowledge bases for specific domains

Tenti P.;Pasi G.;Penaloza R.
2021

Abstract

Language embeddings are a promising approach for handling natural language expressions. Current embeddings encompass a large language corpus, and need to be retrained to deal with specific sub-domains. On the other hand, these embeddings often disregard even basic domain knowledge, making them specially fragile when handling technical, specific, knowledge domains, and requiring costly retraining. To alleviate this issue, we propose a combined approach where the embedding is seen as a model of a logical knowledge base. Through a continuous learning approach, the embedding improves its satisfaction of the knowledge base, and in turn produces better training examples by labelling previously unseen text. In this position paper we describe the general framework for this continuous learning, along with its main features.
paper
Knowledge Bases; Language embedding; Natural Language Understanding; Neuro-Symbolic Learning;
English
3rd International Workshop on Data Meets Applied Ontologies in Explainable AI, DAO-XAI 2021 - 18 September 2021 through 19 September 2021
2021
Proceedings of the Workshop on Data meets Applied Ontologies in Explainable AI (DAO-XAI 2021) part of Bratislava Knowledge September (BAKS 2021)
2021
2998
1
9
https://ceur-ws.org/Vol-2998/
open
Tenti, P., Pasi, G., Penaloza, R. (2021). Complementing language embeddings with knowledge bases for specific domains. In Proceedings of the Workshop on Data meets Applied Ontologies in Explainable AI (DAO-XAI 2021) part of Bratislava Knowledge September (BAKS 2021) (pp.1-9). CEUR-WS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/414317
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