LinkNBed: Multi-Graph Representation Learning with Entity Linkage

Trivedi, Rakshit; Sisman, Bunyamin; Dong, Xin Luna; Faloutsos, Christos; Ma, Jun; Zha, Hongyuan

doi:10.18653/v1/p18-1024

Cited by 38 publications

(24 citation statements)

References 28 publications

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“…It combines three different transformation methods to learn embedding for supporting multi-lingual learning. LinkNbed [21] learns latent representations of nodes and edges in multiple graphs where a unified graph embedding is constructed, avoiding the bias caused by the transformation between different HIN embeddings.…”

Section: Learning Across Multiple Hinsmentioning

confidence: 99%

Link Trustworthiness Evaluation over Multiple Heterogeneous Information Networks

Wang

Jiang³

et al. 2021

Complexity

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Link trustworthiness evaluation is a crucial task for information networks to evaluate the probability of a link being true in a heterogeneous information network (HIN). This task can significantly influence the effectiveness of downstream analysis. However, the performance of existing evaluation methods is limited, as they can only utilize incomplete or one-sided information from a single HIN. To address this problem, we propose a novel multi-HIN link trustworthiness evaluation model that leverages information across multiple related HINs to accomplish link trustworthiness evaluation tasks inherently and efficiently. We present an effective method to evaluate and select informative pairs across HINs and an integrated training procedure to balance inner-HIN and inter-HIN trustworthiness. Experiments on a real-world dataset demonstrate that our proposed model outperforms baseline methods and achieves the best accuracy and F1-score in downstream tasks of HINs.

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Section: Learning Across Multiple Hinsmentioning

confidence: 99%

Link Trustworthiness Evaluation over Multiple Heterogeneous Information Networks

Wang

Jiang³

et al. 2021

Complexity

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“…Das et al [3] show that training Random Forest on around 1,000 labels can obtain ∼ 95% F-measure for easy data sets, and ∼ 80% F-measure for harder data sets. Deep learning allows comparing long text values by their embedding representations, and starts to show promise when matching texts and dirty data [32,48]. Finally, logic-based learning methods (e.g., probabilistic soft logic) enable linking entities of multiple types at the same time, called collective linkage [38].…”

Section: For Entity Resolutionmentioning

confidence: 99%

Data integration and machine learning

Dong

Ρεκατσίνας

2018

Proc. VLDB Endow.

Self Cite

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As data volume and variety have increased, so have the ties between machine learning and data integration become stronger. For machine learning to be effective, one must utilize data from the greatest possible variety of sources; and this is why data integration plays a key role. At the same time machine learning is driving automation in data integration, resulting in overall reduction of integration costs and improved accuracy. This tutorial focuses on three aspects of the synergistic relationship between data integration and machine learning: (1) we survey how state-of-the-art data integration solutions rely on machine learning-based approaches for accurate results and effective human-in-the-loop pipelines, (2) we review how end-to-end machine learning applications rely on data integration to identify accurate, clean, and relevant data for their analytics exercises, and (3) we discuss open research challenges and opportunities that span across data integration and machine learning.

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“…Challenged by the diverse schemata, relational structures and granularities of knowledge representations in different KGs (Nikolov et al, 2009), traditional symbolic methods usually fall short of supporting heterogeneous knowledge association (Suchanek et al, 2011;Lacoste-Julien et al, 2013;Paulheim and Bizer, 2013). Recently, increasing efforts have been put into exploring embeddingbased methods (Chen et al, 2017;Trivedi et al, 2018;Jin et al, 2019). Such methods capture the associations of entities or concepts in a vector space, which can help overcome the symbolic and schematic heterogeneity .…”

Section: Introductionmentioning

confidence: 99%

Knowledge Association with Hyperbolic Knowledge Graph Embeddings

Sun

Chen

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Capturing associations for knowledge graphs (KGs) through entity alignment, entity type inference and other related tasks benefits NLP applications with comprehensive knowledge representations. Recent related methods built on Euclidean embeddings are challenged by the hierarchical structures and different scales of KGs. They also depend on high embedding dimensions to realize enough expressiveness. Differently, we explore with low-dimensional hyperbolic embeddings for knowledge association. We propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a hyperbolic transformation. Extensive experiments on entity alignment and type inference demonstrate the effectiveness and efficiency of our method.

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LinkNBed: Multi-Graph Representation Learning with Entity Linkage

Cited by 38 publications

References 28 publications

Link Trustworthiness Evaluation over Multiple Heterogeneous Information Networks

Link Trustworthiness Evaluation over Multiple Heterogeneous Information Networks

Data integration and machine learning

Knowledge Association with Hyperbolic Knowledge Graph Embeddings

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