Locally Adaptive Translation for Knowledge Graph Embedding

Jia, Yunyi; Wang, Yuanzhuo; Lin, Hailun; Jin, Xiaolong; Cheng, Xueqi

doi:10.1609/aaai.v30i1.10091

Cited by 63 publications

(9 citation statements)

References 18 publications

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“…Following the translational principles, TransG [91] is proposed to deal with multiple relation semantic problems through mining the multiple potential meanings of pairs of entities whose relations may be associated with corresponding triples in the knowledge graph. By improving the loss function of the previous translational method, TransA [92] can adaptively learn the embedding of entities and relations in a knowledge graph. Besides, the previous approaches ignored the hierarchical routine of human cognition, so TransAt [93] adopts an attention mechanism to handle the problems above.…”

Section: Translational Modelsmentioning

confidence: 99%

Deep Learning Meets Knowledge Graphs: A Comprehensive Survey

Bai

et al. 2022

Preprint

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Knowledge Graphs (KGs) which can encode structural relations connecting two objects with one or multiple related attributes have become an increasingly popular research direction. Given the superiority of deep learning in representing complex data in continuous space, it is handy to represent KGs data, thus promoting KGs construction, representation, and application. This survey article provides a comprehensive overview of deep learning technologies and KGs by exploring research topics from diverse phases of the KGs lifecycle, such as construction, representation, and knowledge-aware application. We propose new taxonomies on these research topics for motivating cross-understanding between deep learning and KGs. Based on the above three phases, we classify the different tasks of KGs and task-related methods. Afterwards, we explain the principles of combing deep learning in various KGs steps like KGs embedding. We further discuss the contribution and advantages of deep learning applied to the different application scenarios. Finally, we summarize some critical challenges and open issues deep learning approaches face in KGs.

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Section: Translational Modelsmentioning

confidence: 99%

Deep Learning Meets Knowledge Graphs: A Comprehensive Survey

Bai

et al. 2022

Preprint

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“…Elon Musk is an entrepreneur, investor, and engineer. For tackling these challenge, knowledge graph embedding has been provided and attracted much attention, as it has the capability of knowledge graph to a dense and low dimensional, feature space [19][20][21][22][23][24][25] and it can efficiently calculate the semantic relation between entities in low dimensional space and effectively solve the problems of computational complexity and data sparsity. This method can further be used to explore new knowledge from existing facts (link prediction [19,23]), disambiguate entities (entity resolution [22,24]), extract relations (relation classification [26,27]), etc.…”

Section: Elon Musk Founderof Locatedinmentioning

confidence: 99%

A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks

et al. 2020

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A knowledge graph (KG), also known as a knowledge base, is a particular kind of network structure in which the node indicates entity and the edge represent relation. However, with the explosion of network volume, the problem of data sparsity that causes large-scale KG systems to calculate and manage difficultly has become more significant. For alleviating the issue, knowledge graph embedding is proposed to embed entities and relations in a KG to a low-, dense and continuous feature space, and endow the yield model with abilities of knowledge inference and fusion. In recent years, many researchers have poured much attention in this approach, and we will systematically introduce the existing state-of-the-art approaches and a variety of applications that benefit from these methods in this paper. In addition, we discuss future prospects for the development of techniques and application trends. Specifically, we first introduce the embedding models that only leverage the information of observed triplets in the KG. We illustrate the overall framework and specific idea and compare the advantages and disadvantages of such approaches. Next, we introduce the advanced models that utilize additional semantic information to improve the performance of the original methods. We divide the additional information into two categories, including textual descriptions and relation paths. The extension approaches in each category are described, following the same classification criteria as those defined for the triplet fact-based models. We then describe two experiments for comparing the performance of listed methods and mention some broader domain tasks such as question answering, recommender systems, and so forth. Finally, we collect several hurdles that need to be overcome and provide a few future research directions for knowledge graph embedding.

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“…where E(h, r, t) is the energy function of each model, γ is the margin, and (h , r , t ) denotes some "corrupted" triple which does not exist in S. Unlike aforementioned models that focus on different E(h, r, t), TransA (Jia et al 2016) introduces an adaptive local margin approach that determines γ by a closed set of entity candidates. Other similar models include RESCAL (Nickel, Tresp, and Kriegel 2011), Semantic Matching Energy (SME) (Bordes et al 2012), and the Latent Factor Model (LFM) (Jenatton et al 2012).…”

Section: Related Workmentioning

confidence: 99%

ProjE: Embedding Projection for Knowledge Graph Completion

Shi

Weninger

2017

AAAI

121

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With the large volume of new information created every day, determining the validity of information in a knowledge graph and filling in its missing parts are crucial tasks for many researchers and practitioners. To address this challenge, a number of knowledge graph completion methods have been developed using low-dimensional graph embeddings. Although researchers continue to improve these models using an increasingly complex feature space, we show that simple changes in the architecture of the underlying model can outperform state-of-the-art models without the need for complex feature engineering. In this work, we present a shared variable neural network model called ProjE that fills-in missing information in a knowledge graph by learning joint embeddings of the knowledge graph’s entities and edges, and through subtle, but important, changes to the standard loss function. In doing so, ProjE has a parameter size that is smaller than 11 out of 15 existing methods while performing 37% better than the current-best method on standard datasets. We also show, via a new fact checking task, that ProjE is capable of accurately determining the veracity of many declarative statements.

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Locally Adaptive Translation for Knowledge Graph Embedding

Cited by 63 publications

References 18 publications

Deep Learning Meets Knowledge Graphs: A Comprehensive Survey

Deep Learning Meets Knowledge Graphs: A Comprehensive Survey

A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks

ProjE: Embedding Projection for Knowledge Graph Completion

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