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

The rapid proliferation of knowledge graphs (KGs) has changed the paradigm for various AI-related applications. Despite their large sizes, modern KGs are far from complete and comprehensive. This has motivated the research in knowledge graph completion (KGC), which aims to infer missing values in incomplete knowledge triples. However, most existing KGC models treat the triples in KGs independently without leveraging the inherent and valuable information from the local neighborhood surrounding an entity. To this end, we propose a Relational Graph neural network with Hierarchical ATtention (RGHAT) for the KGC task. The proposed model is equipped with a two-level attention mechanism: (i) the first level is the relation-level attention, which is inspired by the intuition that different relations have different weights for indicating an entity; (ii) the second level is the entity-level attention, which enables our model to highlight the importance of different neighboring entities under the same relation. The hierarchical attention mechanism makes our model more effective to utilize the neighborhood information of an entity. Finally, we extensively validate the superiority of RGHAT against various state-of-the-art baselines.

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Commonsense Knowledge Aware Conversation Generation with Graph Attention

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/643 ◽

2018 ◽

Cited By ~ 35

Author(s):

Hao Zhou ◽

Tom Young ◽

Minlie Huang ◽

Haizhou Zhao ◽

Jingfang Xu ◽

...

Keyword(s):

Language Processing ◽

Large Scale ◽

Semantic Information ◽

Attention Mechanism ◽

Generation Model ◽

Dynamic Graph ◽

Commonsense Knowledge ◽

Word Generation ◽

Proposed Model ◽

Knowledge Graphs

Commonsense knowledge is vital to many natural language processing tasks. In this paper, we present a novel open-domain conversation generation model to demonstrate how large-scale commonsense knowledge can facilitate language understanding and generation. Given a user post, the model retrieves relevant knowledge graphs from a knowledge base and then encodes the graphs with a static graph attention mechanism, which augments the semantic information of the post and thus supports better understanding of the post. Then, during word generation, the model attentively reads the retrieved knowledge graphs and the knowledge triples within each graph to facilitate better generation through a dynamic graph attention mechanism. This is the first attempt that uses large-scale commonsense knowledge in conversation generation. Furthermore, unlike existing models that use knowledge triples (entities) separately and independently, our model treats each knowledge graph as a whole, which encodes more structured, connected semantic information in the graphs. Experiments show that the proposed model can generate more appropriate and informative responses than state-of-the-art baselines.

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Knowledge Graph Alignment Network with Gated Multi-Hop Neighborhood Aggregation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5354 ◽

2020 ◽

Vol 34 (01) ◽

pp. 222-229

Author(s):

Zequn Sun ◽

Chengming Wang ◽

Wei Hu ◽

Muhao Chen ◽

Jian Dai ◽

...

Keyword(s):

Neural Networks ◽

Attention Mechanism ◽

Knowledge Graph ◽

Neighborhood Information ◽

Graph Alignment ◽

Gating Mechanism ◽

Neighborhood Structures ◽

Knowledge Graphs ◽

End To End ◽

Graph Neural Networks

Graph neural networks (GNNs) have emerged as a powerful paradigm for embedding-based entity alignment due to their capability of identifying isomorphic subgraphs. However, in real knowledge graphs (KGs), the counterpart entities usually have non-isomorphic neighborhood structures, which easily causes GNNs to yield different representations for them. To tackle this problem, we propose a new KG alignment network, namely AliNet, aiming at mitigating the non-isomorphism of neighborhood structures in an end-to-end manner. As the direct neighbors of counterpart entities are usually dissimilar due to the schema heterogeneity, AliNet introduces distant neighbors to expand the overlap between their neighborhood structures. It employs an attention mechanism to highlight helpful distant neighbors and reduce noises. Then, it controls the aggregation of both direct and distant neighborhood information using a gating mechanism. We further propose a relation loss to refine entity representations. We perform thorough experiments with detailed ablation studies and analyses on five entity alignment datasets, demonstrating the effectiveness of AliNet.

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An Approach to Automatically Selecting Tolerance Types Using Relational Learning for Knowledge Graphs

10.14733/cadconfp.2020.358-362 ◽

2020 ◽

Author(s):

Yingzhong Zhang ◽

Jia Jia ◽

Mohamed Saad

Keyword(s):

Relational Learning ◽

Knowledge Graphs

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One-Shot Relational Learning for Knowledge Graphs

10.18653/v1/d18-1223 ◽

2018 ◽

Cited By ~ 10

Author(s):

Wenhan Xiong ◽

Mo Yu ◽

Shiyu Chang ◽

Xiaoxiao Guo ◽

William Yang Wang

Keyword(s):

Relational Learning ◽

Knowledge Graphs

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

10.18653/v1/d19-1431 ◽

2019 ◽

Cited By ~ 1

Author(s):

Mingyang Chen ◽

Wen Zhang ◽

Wei Zhang ◽

Qiang Chen ◽

Huajun Chen

Keyword(s):

Link Prediction ◽

Relational Learning ◽

Knowledge Graphs

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Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6392 ◽

2020 ◽

Vol 34 (05) ◽

pp. 8673-8680

Author(s):

Pengda Qin ◽

Xin Wang ◽

Wenhu Chen ◽

Chunyun Zhang ◽

Weiran Xu ◽

...

Keyword(s):

Supervised Classification ◽

Large Scale ◽

Relational Learning ◽

Generative Adversarial Networks ◽

Knowledge Graph ◽

Semantic Features ◽

Performance Improvements ◽

Adversarial Networks ◽

Knowledge Graphs ◽

Noisy Text

Large-scale knowledge graphs (KGs) are shown to become more important in current information systems. To expand the coverage of KGs, previous studies on knowledge graph completion need to collect adequate training instances for newly-added relations. In this paper, we consider a novel formulation, zero-shot learning, to free this cumbersome curation. For newly-added relations, we attempt to learn their semantic features from their text descriptions and hence recognize the facts of unseen relations with no examples being seen. For this purpose, we leverage Generative Adversarial Networks (GANs) to establish the connection between text and knowledge graph domain: The generator learns to generate the reasonable relation embeddings merely with noisy text descriptions. Under this setting, zero-shot learning is naturally converted to a traditional supervised classification task. Empirically, our method is model-agnostic that could be potentially applied to any version of KG embeddings, and consistently yields performance improvements on NELL and Wiki dataset.

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An approach to capturing and reusing tacit design knowledge using relational learning for knowledge graphs

Advanced Engineering Informatics ◽

10.1016/j.aei.2021.101505 ◽

2022 ◽

Vol 51 ◽

pp. 101505

Author(s):

Jia Jia ◽

Yingzhong Zhang ◽

Mohamed Saad

Keyword(s):

Relational Learning ◽

Design Knowledge ◽

Knowledge Graphs

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A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/843 ◽

2019 ◽

Cited By ~ 1

Author(s):

Sebastijan Dumancic ◽

Alberto Garcia-Duran ◽

Mathias Niepert

Keyword(s):

Euclidean Space ◽

Knowledge Base ◽

Relational Learning ◽

Statistical Relational Learning ◽

Representation Learning ◽

The Other ◽

Learning Approaches ◽

Relational Knowledge ◽

Knowledge Graphs ◽

Relational Classification

Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally been addressed by symbolic approaches such as Statistical relational learning, recent methods in (deep) representation learning have shown promising results for specialised tasks such as knowledge base completion. These approaches, also known as distributional, abandon the traditional symbolic paradigm by replacing symbols with vectors in Euclidean space. With few exceptions, symbolic and distributional approaches are explored in different communities and little is known about their respective strengths and weaknesses. In this work, we compare distributional and symbolic relational learning approaches on various standard relational classification and knowledge base completion tasks. Furthermore, we analyse the properties of the datasets and relate them to the performance of the methods in the comparison. The results reveal possible indicators that could help in choosing one approach over the other for particular knowledge graphs.

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