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MADLINK: Attentive Multihop and Entity Descriptions for Link Prediction in Knowledge Graphs


MADLINK: Attentive Multihop and Entity Descriptions for Link Prediction in Knowledge Graphs



Veröffentlicht: 2022

Journal: Semantic Web Journal




Referierte Veröffentlichung

BibTeX




Kurzfassung
Knowledge Graphs (KGs) comprise interlinked information in the form of entities and relations between them in a particular domain and provide the backbone for many applications. However, the KGs are often incomplete as the links between the entities are missing. Link Prediction is the task of predicting these missing links in a KG based on the existing links. Recent years have witnessed many studies on link prediction using KG embeddings which is one of the mainstream tasks in KG completion. To do so, most of the existing methods learn the latent representation of the entities and relations, whereas only a few of them consider contextual information as well as the textual descriptions of the entities. This paper introduces an attentive encoder-decoder based link prediction approach considering both structural information of the KG and the textual entity descriptions. A path selection method is used to encapsulate the contextual information of an entity in a KG. The model explores a bidirectional Gated Recurrent Unit (GRU) based encoder-decoder to learn the representation of the paths whereas SBERT is used to generate the representation of the entity descriptions. The proposed approach outperforms most of the state-of-the-art models and achieves comparable results with the rest when evaluated with FB15K, FB15K-237, WN18, WN18RR, and YAGO3-10 datasets.

Download: Media:MADLINK_ Attentive Multihop and Entity Descriptions for Link Prediction in Knowledge Graphs_BISWAS_SWJ.pdf
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Forschungsgruppe

Information Service Engineering


Forschungsgebiet