@inproceedings{kochsiek-etal-2023-friendly,
title = "Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction",
author = "Kochsiek, Adrian and
Saxena, Apoorv and
Nair, Inderjeet and
Gemulla, Rainer",
editor = "Can, Burcu and
Mozes, Maximilian and
Cahyawijaya, Samuel and
Saphra, Naomi and
Kassner, Nora and
Ravfogel, Shauli and
Ravichander, Abhilasha and
Zhao, Chen and
Augenstein, Isabelle and
Rogers, Anna and
Cho, Kyunghyun and
Grefenstette, Edward and
Voita, Lena",
booktitle = "Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.repl4nlp-1.11",
doi = "10.18653/v1/2023.repl4nlp-1.11",
pages = "131--138",
abstract = "We propose KGT5-context, a simple sequence-to-sequence model for link prediction (LP) in knowledge graphs (KG). Our work expands on KGT5, a recent LP model that exploits textual features of the KG, has small model size, and is scalable. To reach good predictive performance, however, KGT5 relies on an ensemble with a knowledge graph embedding model, which itself is excessively large and costly to use. In this short paper, we show empirically that adding contextual information {---} i.e., information about the direct neighborhood of the query entity {---} alleviates the need for a separate KGE model to obtain good performance. The resulting KGT5-context model is simple, reduces model size significantly, and obtains state-of-the-art performance in our experimental study.",
}
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%0 Conference Proceedings
%T Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction
%A Kochsiek, Adrian
%A Saxena, Apoorv
%A Nair, Inderjeet
%A Gemulla, Rainer
%Y Can, Burcu
%Y Mozes, Maximilian
%Y Cahyawijaya, Samuel
%Y Saphra, Naomi
%Y Kassner, Nora
%Y Ravfogel, Shauli
%Y Ravichander, Abhilasha
%Y Zhao, Chen
%Y Augenstein, Isabelle
%Y Rogers, Anna
%Y Cho, Kyunghyun
%Y Grefenstette, Edward
%Y Voita, Lena
%S Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kochsiek-etal-2023-friendly
%X We propose KGT5-context, a simple sequence-to-sequence model for link prediction (LP) in knowledge graphs (KG). Our work expands on KGT5, a recent LP model that exploits textual features of the KG, has small model size, and is scalable. To reach good predictive performance, however, KGT5 relies on an ensemble with a knowledge graph embedding model, which itself is excessively large and costly to use. In this short paper, we show empirically that adding contextual information — i.e., information about the direct neighborhood of the query entity — alleviates the need for a separate KGE model to obtain good performance. The resulting KGT5-context model is simple, reduces model size significantly, and obtains state-of-the-art performance in our experimental study.
%R 10.18653/v1/2023.repl4nlp-1.11
%U https://aclanthology.org/2023.repl4nlp-1.11
%U https://doi.org/10.18653/v1/2023.repl4nlp-1.11
%P 131-138
Markdown (Informal)
[Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction](https://aclanthology.org/2023.repl4nlp-1.11) (Kochsiek et al., RepL4NLP 2023)
ACL