Knowledge base enrichment by relation learning from social tagging data

Dong, Hang; Wang, Wei; Coenen, Frans; Huang, Kaizhu

doi:10.1016/j.ins.2020.04.002

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…These methods learn semantic models by using bi-directional training samples, which combine the advantages of both directions of retrieval, thus improving generation performance. Lastly, to exploit label information, some algorithm [5,6,[25][26][27][28] directly minimize the cross-entropy loss between the predictions and the class labels. [29,30]are combined with fine-grained [31] calculate the semantic similarity between the instances by both the label information and feature information of instances.…”

Section: Related Workmentioning

confidence: 99%

Cross‐modal semantic correlation learning by Bi‐CNN network

Wang

Yan

et al. 2021

IET Image Processing

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Cross modal retrieval can retrieve images through a text query and vice versa. In recent years, cross modal retrieval has attracted extensive attention. The purpose of most now available cross modal retrieval methods is to find a common subspace and maximize the different modal correlation. To generate specific representations consistent with cross modal tasks, this paper proposes a novel cross modal retrieval framework, which integrates feature learning and latent space embedding. In detail, we proposed a deep CNN and a shallow CNN to extract the feature of the samples. The deep CNN is used to extract the representation of images, and the shallow CNN uses a multi-dimensional kernel to extract multi-level semantic representation of text. Meanwhile, we enhance the semantic manifold by constructing cross modal ranking and within-modal discriminant loss to improve the division of semantic representation. Moreover, the most representative samples are selected by using online sampling strategy, so that the approach can be implemented on a large-scale data. This approach not only increases the discriminative ability among different categories, but also maximizes the relativity between different modalities. Experiments on three real word datasets show that the proposed method is superior to the popular methods. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%

Cross‐modal semantic correlation learning by Bi‐CNN network

Wang

Yan

et al. 2021

IET Image Processing

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“…As a subtask, Relation classification(RC) aims to identify the correct relationship between two entities for a given instance (i.e., a sentence in natural language including a pair of entities, head and tail entities) and a set of relations. It is essential for many applied researches in natural language processing (NLP) and artificial intelligence, such as knowledge base completion [1], dialogue systems [2], entity resolution [3] and drug discovery [4].…”

Section: Introductionmentioning

confidence: 99%

Phrase-level attention network for few-shot inverse relation classification in knowledge graph

Dou²,

Wang

et al. 2023

World Wide Web

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Relation classification is to recognize semantic relation between two given entities mentioned in the given text in Knowledge Graph. Existing models have performed well on the inverse relation classification with large-scale datasets, but their performance drops significantly for few-shot learning. In this paper, we propose a novel method, function words adaptively enhanced attention framework (FAEA+), to capture class-related function words by the designed hybrid attention for fewshot inverse relation classification. Then, an instance-aware prototype network is present to adaptively capture relation information associated with query instances and eliminate intra-class redundancy due to function words introduced. We theoretically prove that the introduction of function words will increase intra-class differences, and the designed instance-aware prototype network is competent for reducing redundancy. Experimental results show that FAEA+ significantly improved over strong baselines on two datasets. Moreover, our model Article Title has a distinct advantage in solving inverse relations, which outperforms state-of-the-art results by 16.82% under a 1-shot setting in FewRel1.0.

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