@inproceedings{chandrahas-etal-2020-inducing,
title = "Inducing Interpretability in Knowledge Graph Embeddings",
author = "Chandrahas, . and
Sengupta, Tathagata and
Pragadeesh, Cibi 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.9",
pages = "70--75",
abstract = "We study the problem of inducing interpretability in Knowledge Graph (KG) embeddings. Learning KG embeddings has been an active area of research in the past few years, resulting in many different models. However, most of these methods do not address the interpretability (semantics) of individual dimensions of the learned embeddings. In this work, we study this problem and propose a method for inducing interpretability in KG embeddings using entity co-occurrence statistics. The proposed method significantly improves the interpretability, while maintaining comparable performance in other KG tasks.",
}
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%0 Conference Proceedings
%T Inducing Interpretability in Knowledge Graph Embeddings
%A Chandrahas, ..
%A Sengupta, Tathagata
%A Pragadeesh, Cibi
%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-inducing
%X We study the problem of inducing interpretability in Knowledge Graph (KG) embeddings. Learning KG embeddings has been an active area of research in the past few years, resulting in many different models. However, most of these methods do not address the interpretability (semantics) of individual dimensions of the learned embeddings. In this work, we study this problem and propose a method for inducing interpretability in KG embeddings using entity co-occurrence statistics. The proposed method significantly improves the interpretability, while maintaining comparable performance in other KG tasks.
%U https://aclanthology.org/2020.icon-main.9
%P 70-75
Markdown (Informal)
[Inducing Interpretability in Knowledge Graph Embeddings](https://aclanthology.org/2020.icon-main.9) (Chandrahas et al., ICON 2020)
ACL
- . Chandrahas, Tathagata Sengupta, Cibi Pragadeesh, and Partha Talukdar. 2020. Inducing Interpretability in Knowledge Graph Embeddings. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 70–75, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).