# A shallow neural model for relation prediction

@article{Demir2021ASN,
title={A shallow neural model for relation prediction},
author={Caglar Demir and Diego Moussallem and A. N. Ngomo},
journal={2021 IEEE 15th International Conference on Semantic Computing (ICSC)},
year={2021},
pages={179-182}
}
• Published 2021
• Computer Science
• 2021 IEEE 15th International Conference on Semantic Computing (ICSC)
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… Expand
4 Citations

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#### References

SHOWING 1-10 OF 33 REFERENCES
Relation prediction in knowledge graph by Multi-Label Deep Neural Network
A simple architecture model with emphasis on relation prediction by using a Multi-Label Deep Neural Network (DNN), and developed KGML, which is more accurate than TransE and TransR and faster than PTransE. Expand
Convolutional 2D Knowledge Graph Embeddings
• Computer Science, Mathematics
• AAAI
• 2018
ConvE, a multi-layer convolutional network model for link prediction, is introduced and it is found that ConvE achieves state-of-the-art Mean Reciprocal Rank across most datasets. Expand
Predicting relations of embedded RDF entities by Deep Neural Network
Experimental results showed that predictions by RDFDNN are more accurate than those by TransE and TransR, and its accuracy is comparable to that of DKRL which uses both RDF triples and entity descriptions for learning. Expand
Representation Learning of Knowledge Graphs with Entity Descriptions
• Computer Science
• AAAI
• 2016
Experimental results on real-world datasets show that, the proposed novel RL method for knowledge graphs outperforms other baselines on the two tasks, especially under the zero-shot setting, which indicates that the method is capable of building representations for novel entities according to their descriptions. Expand
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
• Computer Science
• ICLR
• 2015
It is found that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. Expand
SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions
• Computer Science
• AAAI
• 2017
A semantic representation method for knowledge graph (KSR) is proposed, which imposes a two-level hierarchical generative process that globally extracts many aspects and then locally assigns a specific category in each aspect for every triple. Expand
Translating Embeddings for Modeling Multi-relational Data
• Computer Science, Mathematics
• NIPS
• 2013
TransE is proposed, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities, which proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Expand
Type-Constrained Representation Learning in Knowledge Graphs
• Computer Science
• International Semantic Web Conference
• 2015
This work integrated prior knowledge in form of type-constraints in various state of the art latent variable approaches and shows that prior knowledge on relation-types significantly improves these models up to 77% in link-prediction tasks. Expand
You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings
• Computer Science
• ICLR
• 2020
It is found that when trained appropriately, the relative performance differences between various model architectures often shrinks and sometimes even reverses when compared to prior results, and many of the more advanced architectures and techniques proposed in the literature should be revisited to reassess their individual benefits. Expand
ProjE: Embedding Projection for Knowledge Graph Completion
• Computer Science, Mathematics
• AAAI
• 2017
This work presents a shared variable neural network model called ProjE that fills-in missing information in a knowledge graph by learning joint embeddings of the knowledge graph's entities and edges, and through subtle, but important, changes to the standard loss function. Expand