Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs

Chen, Mingyang; Zhang, Wen; Zhang, Wei; Chen, Qiang; Chen, Huajun

doi:10.18653/v1/d19-1431

Cited by 157 publications

(125 citation statements)

References 16 publications

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“…Additionally, some models exploit the metadata as a source of prior knowledge of knowledge graph entities. Such models learn from the information available such as relation or entity types, which could improve inference about missing triples (Lv et al, 2019;Wang et al, 2019a;Chen et al, 2019). Furthermore, multi-source Knowledge Representation Learning model (Tang et al, 2019) is multimodal in terms of combining features and combining models.…”

Section: Multimodal Embedding Methodsmentioning

confidence: 99%

Application and evaluation of knowledge graph embeddings in biomedical data

Alshahrani

Thafar

Essack

2021

PeerJ Computer Science

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Linked data and bio-ontologies enabling knowledge representation, standardization, and dissemination are an integral part of developing biological and biomedical databases. That is, linked data and bio-ontologies are employed in databases to maintain data integrity, data organization, and to empower search capabilities. However, linked data and bio-ontologies are more recently being used to represent information as multi-relational heterogeneous graphs, “knowledge graphs”. The reason being, entities and relations in the knowledge graph can be represented as embedding vectors in semantic space, and these embedding vectors have been used to predict relationships between entities. Such knowledge graph embedding methods provide a practical approach to data analytics and increase chances of building machine learning models with high prediction accuracy that can enhance decision support systems. Here, we present a comparative assessment and a standard benchmark for knowledge graph-based representation learning methods focused on the link prediction task for biological relations. We systematically investigated and compared state-of-the-art embedding methods based on the design settings used for training and evaluation. We further tested various strategies aimed at controlling the amount of information related to each relation in the knowledge graph and its effects on the final performance. We also assessed the quality of the knowledge graph features through clustering and visualization and employed several evaluation metrics to examine their uses and differences. Based on this systematic comparison and assessments, we identify and discuss the limitations of knowledge graph-based representation learning methods and suggest some guidelines for the development of more improved methods.

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Section: Multimodal Embedding Methodsmentioning

confidence: 99%

Application and evaluation of knowledge graph embeddings in biomedical data

Alshahrani

Thafar

Essack

2021

PeerJ Computer Science

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“…For the low resource setting, [44] proposed a one-shot relational learning framework, which learns a matching metric by considering both the learned embeddings and one-hop graph structures. [6] proposed a Meta Relational Learning (MetaR) framework to do few-shot link prediction in KGs. [59] propose a novel framework IterE iteratively learning embeddings and rules which can improve the quality of sparse entity embeddings and their link prediction results.…”

Section: Related Workmentioning

confidence: 99%

Relation Adversarial Network for Low Resource Knowledge Graph Completion

Zhang

Deng

Sun

et al. 2020

Proceedings of the Web Conference 2020

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Knowledge Graph Completion (KGC) has been proposed to improve Knowledge Graphs by filling in missing connections via link prediction or relation extraction. One of the main difficulties for KGC is a low resource problem. Previous approaches assume sufficient training triples to learn versatile vectors for entities and relations, or a satisfactory number of labeled sentences to train a competent relation extraction model. However, low resource relations are very common in KGs, and those newly added relations often do not have many known samples for training. In this work, we aim at predicting new facts under a challenging setting where only limited training instances are available. We propose a general framework called Weighted Relation Adversarial Network, which utilizes an adversarial procedure to help adapt knowledge/features learned from high resource relations to different but related low resource relations. Specifically, the framework takes advantage of a relation discriminator to distinguish between samples from different relations, and help learn relation-invariant features more transferable from source relations to target relations. Experimental results show that the proposed approach outperforms previous methods regarding low resource settings for both link prediction and relation extraction.

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“…Some other methods utilize extra textual information to represent entities and relations, for example, DKRL [17], Open-world KGC [12]. And MetaR [4] concentrate on few-shot link prediction in knowledge graph. In this problem, few triplets are given at training time, while in OOKG entities and relations problems, auxiliary triplets are given at testing time.…”

Section: Related Workmentioning

confidence: 99%

Attention-Based Aggregation Graph Networks for Knowledge Graph Information Transfer

Zhao

Jia

Huang

2020

Advances in Knowledge Discovery and Data Mining

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Knowledge graph completion (KGC) aims to predict missing information in a knowledge graph. Many existing embedding-based KGC models solve the Out-of-knowledge-graph (OOKG) entity problem (also known as zero-shot entity problem) by utilizing textual information resources such as descriptions and types. However, few works utilize the extra structural information to generate embeddings. In this paper, we propose a new zero-shot scenario: how to acquire the embedding vector of a relation that is not observed at training time. Our work uses a convolutional transition and attention-based aggregation graph neural network to solve both the OOKG entity problem and the new OOKG relation problem without retraining, regarding the structural neighbors as the auxiliary information. The experimental results show the effectiveness of our proposed models in solving the OOKG relation problem. For the OOKG entity problem, our model performs better than the previous GNN-based model by 23.9% in NELL-995-Tail dataset.

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Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs

Cited by 157 publications

References 16 publications

Application and evaluation of knowledge graph embeddings in biomedical data

Application and evaluation of knowledge graph embeddings in biomedical data

Relation Adversarial Network for Low Resource Knowledge Graph Completion

Attention-Based Aggregation Graph Networks for Knowledge Graph Information Transfer

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