Searching for Embeddings in a Haystack: Link Prediction on Knowledge Graphs with Subgraph Pruning

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Abstract

Embedding-based models of Knowledge Graphs (KGs) can be used to predict the existence of missing links by ranking the entities according to some likelihood scores. An exhaustive computation of all likelihood scores is very expensive if the KG is large. To counter this problem, we propose a technique to reduce the search space by identifying smaller subsets of promising entities. Our technique first creates embeddings of subgraphs using the embeddings from the model. Then, it ranks the subgraphs with some proposed ranking functions and considers only the entities in the top k subgraphs. Our experiments show that our technique is able to reduce the search space significantly while maintaining a good recall.

Original languageEnglish
Title of host publicationWWW '20
Subtitle of host publicationProceedings of The Web Conference 2020
PublisherAssociation for Computing Machinery, Inc
Pages2817-2823
Number of pages7
ISBN (Electronic)9781450370233
DOIs
Publication statusPublished - Apr 2020
Event29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China
Duration: 20 Apr 202024 Apr 2020

Conference

Conference29th International World Wide Web Conference, WWW 2020
CountryTaiwan, Province of China
CityTaipei
Period20/04/2024/04/20

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