A shallow neural model for relation prediction

• URL: http://arxiv.org/abs/2101.09090v1
• Date: Fri, 22 Jan 2021 13:10:11 GMT
• Title: A shallow neural model for relation prediction
• Authors: Caglar Demir and Diego Moussallem and Axel-Cyrille Ngonga Ngomo
• Abstract summary: We propose a shallow neural model (SHALLOM) that accurately infers missing relations from entities. Our experiments indicate that SHALLOM outperforms state-of-the-art approaches on the FB15K-237 and WN18RR datasets.
• Score: 2.2559617939136505
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Knowledge graph completion refers to predicting missing triples. Most approaches achieve this goal by predicting entities, given an entity and a relation. We predict missing triples via the relation prediction. To this end, we frame the relation prediction problem as a multi-label classification problem and propose a shallow neural model (SHALLOM) that accurately infers missing relations from entities. SHALLOM is analogous to C-BOW as both approaches predict a central token (p) given surrounding tokens ((s,o)). Our experiments indicate that SHALLOM outperforms state-of-the-art approaches on the FB15K-237 and WN18RR with margins of up to $3\%$ and $8\%$ (absolute), respectively, while requiring a maximum training time of 8 minutes on these datasets. We ensure the reproducibility of our results by providing an open-source implementation including training and evaluation scripts at {\url{https://github.com/dice-group/Shallom}.}

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