Knowledge Graph Embedding via Graph Attenuated Attention Networks

Wang, Rui; Li, Bicheng; Hu, Shengwei; Du, Wei; Zhang, Min

doi:10.1109/access.2019.2963367

Cited by 71 publications

(37 citation statements)

References 27 publications

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“…Contrary to node classification, link-prediction is the task of predicting label(s) or strength associated to unknown or non-existent connections, given the information on constituent nodes. For more details see [18,19].…”

Section: • Link Predictionmentioning

confidence: 99%

Graph Neural Networks and Application for Cosmic-Ray Analysis

Koundal¹

2021

Proceedings of the 5th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2021)

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Deep Learning has emerged as one of the most promising areas of computational research for pattern learning, inference drawing, and decision-making, with wide-ranging applications across various scientific disciplines. This has also made it possible for faster and more precise analysis in astroparticle physics, enabling new insights from massive volumes of input data. Graph Neural Networks have materialized as a salient implementation method among the numerous deeplearning architectures over the last few years because of the unique ability to represent complex input data from a wide range of problems in its most natural form. Described using nodes and edges, graphs allow us to efficiently represent relational data and learn hidden representations of input data to obtain better model accuracy. At IceCube Neutrino Observatory, a complex multicomponent detector, traditional likelihood-based analysis on a per-event basis, to reconstruct cosmic-ray air shower parameters is time-consuming and computationally costly. Using advanced and flexible models based on Graph Neural Networks naturally emerges as a possible solution, reducing the time and computing cost for performing such analysis while boosting sensitivity. This paper will outline Graph Neural Networks and discuss a possible application of using such methods at the IceCube Neutrino Observatory.

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Section: • Link Predictionmentioning

confidence: 99%

Graph Neural Networks and Application for Cosmic-Ray Analysis

Koundal¹

2021

Proceedings of the 5th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2021)

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“…We argue that the accuracy of the inference results is not only affected by the explicit characteristics of the entity itself, but also by the implicit characteristics [9, 10]. For example, the implicit characteristics of the entity's neighbours can cascade to affect the overall characteristics of the entities [11, 12].…”

Section: Introductionmentioning

confidence: 99%

Hybrid attention mechanism for few‐shot relational learning of knowledge graphs

Guo

et al. 2021

IET Computer Vision

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Few-shot knowledge graph (KG) reasoning is the main focus in the field of knowledge graph reasoning. In order to expand the application fields of the knowledge graph, a large number of studies are based on a large number of training samples. However, we have learnt that there are actually many missing relationships or entities in the knowledge graph, and in most cases, there are not many training instances when implementing new relationships. To tackle it, in this study, the authors aim to predict a new entity given few reference instances, even only one training instance. A few-shot learning framework based on a hybrid attention mechanism is proposed. The framework employs traditional embedding models to extract knowledge, and uses an attenuated attention network and a self-attention mechanism to obtain the hidden attributes of entities. Thus, it can learn a matching metric by considering both the learnt embeddings and one-hop graph structures. The experimental results present that the model has achieved significant performance improvements on the NELL-One and Wiki-One datasets.

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“…Attributed to its reasoning ability, KGC models are crucial in alleviating the KG's incompleteness issue and benefiting many downstream applications, such as recommendation (Cao et al, 2019b) and information extraction (Hu et al, 2021;Cao et al, 2020a). However, the KGC performance on existing benchmarks are still unsatisfactory -0.51 Hit Ratio@1 and 187 Mean Rank of the top-ranked model (Wang et al, 2019) on the widely used FB15k237 . Do we have a slow progress of models (Akrami et al, 2020)?…”

Section: Introductionmentioning

confidence: 99%

Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion

Cao¹,

Xiang²,

Lv³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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We present InferWiki, a Knowledge Graph Completion (KGC) dataset that improves upon existing benchmarks in inferential ability, assumptions, and patterns. First, each testing sample is predictable with supportive data in the training set. To ensure it, we propose to utilize rule-guided train/test generation, instead of conventional random split. Second, InferWiki initiates the evaluation following the open-world assumption and improves the inferential difficulty of the closed-world assumption, by providing manually annotated negative and unknown triples. Third, we include various inference patterns (e.g., reasoning path length and types) for comprehensive evaluation. In experiments, we curate two settings of InferWiki varying in sizes and structures, and apply the construction process on CoDEx as comparative datasets. The results and empirical analyses demonstrate the necessity and high-quality of InferWiki. Nevertheless, the performance gap among various inferential assumptions and patterns presents the difficulty and inspires future research direction. Our datasets can be found in https://github. com/TaoMiner/inferwiki.

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Knowledge Graph Embedding via Graph Attenuated Attention Networks

Cited by 71 publications

References 27 publications

Graph Neural Networks and Application for Cosmic-Ray Analysis

Graph Neural Networks and Application for Cosmic-Ray Analysis

Hybrid attention mechanism for few‐shot relational learning of knowledge graphs

Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion

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