Abstract
Despite their large sizes, modern Knowledge Graphs (KGs) are still highly incomplete. Statistical relational learning methods can detect missing links by "embedding" the nodes and relations into latent feature tensors. Unfortunately, these methods are unable to learn good embeddings if the nodes are not well-connected. Our proposal is to learn embeddings for correlations between subgraphs and add a post-prediction phase to counter the lack of training data. This technique, applied on top of methods like TransE or HolE, can significantly increase the predictions on realistic KGs.
Original language | English |
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Title of host publication | CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management |
Publisher | Association for Computing Machinery |
Pages | 2247-2250 |
Number of pages | 4 |
Volume | Part F131841 |
ISBN (Electronic) | 9781450349185 |
DOIs | |
Publication status | Published - 6 Nov 2017 |
Event | 26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore Duration: 6 Nov 2017 → 10 Nov 2017 |
Conference
Conference | 26th ACM International Conference on Information and Knowledge Management, CIKM 2017 |
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Country/Territory | Singapore |
City | Singapore |
Period | 6/11/17 → 10/11/17 |