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        <title>Multi-Hop Relation Aware Representations for Inductive Knowledge Graphs</title>
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    <abstract>Recent knowledge graph (KG) embedding methods explore parameter-efficient representations for large-scale KGs. These techniques learn entity representation using a fixed size vocabulary. Such a vocabulary consists of all the relations and a small subset of the full entity set, referred to as anchors. An entity is hence expressed as a function of reachable anchors and immediate relations. The performance of these methods is, therefore, largely dependent on the entity tokenization strategy. Especially in inductive settings, the representation capacity of these embeddings is limited due to the absence of anchor entities, as unseen entities have no connection with training graph entities. In this work, we propose a novel entity tokenization strategy that tokenizes an entity into a set of anchors based on relation similarity and relational paths. Our model MH-RARe overcomes the challenge of unseen entities not being directly connected to the anchors by selecting informative anchors from the training graph using relation similarity. Experiment results show that our model outperforms the baselines on multiple datasets for inductive knowledge graph completion task, attaining upto 5% improvement, while maintaining parameter efficiency.</abstract>
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%0 Conference Proceedings
%T Multi-Hop Relation Aware Representations for Inductive Knowledge Graphs
%A Aniruddha, Bala
%A Ankit, Sharma
%A Shlok, Sharma
%A Pinaki, Bhaskar
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F aniruddha-etal-2023-multi
%X Recent knowledge graph (KG) embedding methods explore parameter-efficient representations for large-scale KGs. These techniques learn entity representation using a fixed size vocabulary. Such a vocabulary consists of all the relations and a small subset of the full entity set, referred to as anchors. An entity is hence expressed as a function of reachable anchors and immediate relations. The performance of these methods is, therefore, largely dependent on the entity tokenization strategy. Especially in inductive settings, the representation capacity of these embeddings is limited due to the absence of anchor entities, as unseen entities have no connection with training graph entities. In this work, we propose a novel entity tokenization strategy that tokenizes an entity into a set of anchors based on relation similarity and relational paths. Our model MH-RARe overcomes the challenge of unseen entities not being directly connected to the anchors by selecting informative anchors from the training graph using relation similarity. Experiment results show that our model outperforms the baselines on multiple datasets for inductive knowledge graph completion task, attaining upto 5% improvement, while maintaining parameter efficiency.
%U https://aclanthology.org/2023.icon-1.3
%P 27-35

Markdown (Informal)

[Multi-Hop Relation Aware Representations for Inductive Knowledge Graphs](https://aclanthology.org/2023.icon-1.3) (Aniruddha et al., ICON 2023)

• Multi-Hop Relation Aware Representations for Inductive Knowledge Graphs (Aniruddha et al., ICON 2023)

ACL

• Bala Aniruddha, Sharma Ankit, Sharma Shlok, and Bhaskar Pinaki. 2023. Multi-Hop Relation Aware Representations for Inductive Knowledge Graphs. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 27–35, Goa University, Goa, India. NLP Association of India (NLPAI).