@inproceedings{chandrahas-etal-2020-learning,
title = "Learning to Interact: An Adaptive Interaction Framework for Knowledge Graph Embeddings",
author = "Chandrahas, . and
Agrawal, Nilesh and
Talukdar, Partha",
editor = "Bhattacharyya, Pushpak and
Sharma, Dipti Misra and
Sangal, Rajeev",
booktitle = "Proceedings of the 17th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2020",
address = "Indian Institute of Technology Patna, Patna, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2020.icon-main.8",
pages = "60--69",
abstract = "Knowledge Graph (KG) Embedding methods have been widely studied in the past few years and many methods have been proposed. These methods represent entities and relations in the KG as vectors in a vector space, trained to distinguish correct edges from the incorrect ones. For this distinction, simple functions of vectors{'} dimensions, called interactions, are used. These interactions are used to calculate the candidate tail entity vector which is matched against all entities in the KG. However, for most of the existing methods, these interactions are fixed and manually specified. In this work, we propose an automated framework for discovering the interactions while training the KG Embeddings. The proposed method learns relevant interactions along with other parameters during training, allowing it to adapt to different datasets. Many of the existing methods can be seen as special cases of the proposed framework. We demonstrate the effectiveness of the proposed method on link prediction task by extensive experiments on multiple benchmark datasets.",
}
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<abstract>Knowledge Graph (KG) Embedding methods have been widely studied in the past few years and many methods have been proposed. These methods represent entities and relations in the KG as vectors in a vector space, trained to distinguish correct edges from the incorrect ones. For this distinction, simple functions of vectors’ dimensions, called interactions, are used. These interactions are used to calculate the candidate tail entity vector which is matched against all entities in the KG. However, for most of the existing methods, these interactions are fixed and manually specified. In this work, we propose an automated framework for discovering the interactions while training the KG Embeddings. The proposed method learns relevant interactions along with other parameters during training, allowing it to adapt to different datasets. Many of the existing methods can be seen as special cases of the proposed framework. We demonstrate the effectiveness of the proposed method on link prediction task by extensive experiments on multiple benchmark datasets.</abstract>
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%0 Conference Proceedings
%T Learning to Interact: An Adaptive Interaction Framework for Knowledge Graph Embeddings
%A Chandrahas, ..
%A Agrawal, Nilesh
%A Talukdar, Partha
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F chandrahas-etal-2020-learning
%X Knowledge Graph (KG) Embedding methods have been widely studied in the past few years and many methods have been proposed. These methods represent entities and relations in the KG as vectors in a vector space, trained to distinguish correct edges from the incorrect ones. For this distinction, simple functions of vectors’ dimensions, called interactions, are used. These interactions are used to calculate the candidate tail entity vector which is matched against all entities in the KG. However, for most of the existing methods, these interactions are fixed and manually specified. In this work, we propose an automated framework for discovering the interactions while training the KG Embeddings. The proposed method learns relevant interactions along with other parameters during training, allowing it to adapt to different datasets. Many of the existing methods can be seen as special cases of the proposed framework. We demonstrate the effectiveness of the proposed method on link prediction task by extensive experiments on multiple benchmark datasets.
%U https://aclanthology.org/2020.icon-main.8
%P 60-69
Markdown (Informal)
[Learning to Interact: An Adaptive Interaction Framework for Knowledge Graph Embeddings](https://aclanthology.org/2020.icon-main.8) (Chandrahas et al., ICON 2020)
ACL