Differentiating Concepts and Instances for Knowledge Graph Embedding

Lv, Xin; Hou, Lei; Li, Juanzi; Liu, Zhiyuan

doi:10.18653/v1/d18-1222

Cited by 82 publications

(46 citation statements)

References 17 publications

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“…A survey on KG embeddings Wang et al (2017a) considers translation-based models, such as TransE (Bordes et al, 2013), TransH (Wang et al, 2014), TransM (Fan et al, 2014), TransR/CTransR (Lin et al, 2015), TransC (Lv et al, 2018), TransD (Ji et al, 2015), TranSparse (Ji et al, 2016), KG2E (He et al, 2015), and semantic matching models, based on RESCAL (Nickel, Tresp & Kriegel, 2011) tensor factorization framework, such as DistMult (Yang et al, 2014), HolE (Nickel, Rosasco & Poggio, 2015) and ComplEx with comparison paper for the latter two in Trouillon & Nickel (2017).…”

Section: Knowledge Graph Completionmentioning

confidence: 99%

Survey on graph embeddings and their applications to machine learning problems on graphs

Makarov

Kiselev

Nikitinsky

et al. 2021

PeerJ Computer Science

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Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

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Section: Knowledge Graph Completionmentioning

confidence: 99%

Survey on graph embeddings and their applications to machine learning problems on graphs

Makarov

Kiselev

Nikitinsky

et al. 2021

PeerJ Computer Science

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“…Note that incorporating more external information (Jin et al, 2018;Neelakantan et al, 2015) is not the main focus in this paper, as we only consider the internal structural information in KGs instead, which correspondingly makes our work much more challenging but also more universal and flexible due to the limited information. Recently, (Lv et al, 2018;Hao et al, 2019) also attempt to embedding structural information in KG. However, the goals and models are very different from ours.…”

Section: Related Workmentioning

confidence: 99%

Connecting Embeddings for Knowledge Graph Entity Typing

Yu¹,

zhang²,

Xie³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG, which is a very significant but still under-explored subtask of knowledge graph completion. In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge from KGs. Specifically, we present two distinct knowledge-driven effective mechanisms of entity type inference. Accordingly, we build two novel embedding models to realize the mechanisms. Afterward, a joint model with them is used to infer missing entity type instances, which favors inferences that agree with both entity type instances and triple knowledge in KGs. Experimental results on two real-world datasets (Freebase and YAGO) demonstrate the effectiveness of our proposed mechanisms and models for improving KG entity typing.

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“…The former does not consider the word order information of the text, while the latter considers the word order of the text. TransC [42] is a knowledge graph embedding model which distinguishes concepts from instances. It encodes each concept in knowledge graphs as a sphere and each instance as a vector in the same semantic space.…”

Section: Information Fusion Within Knowledge Graphmentioning

confidence: 99%

Multi-source knowledge fusion: a survey

Zhao

Jia

et al. 2020

World Wide Web

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Multi-source knowledge fusion is one of the important research topics in the fields of artificial intelligence, natural language processing, and so on. The research results of multi-source knowledge fusion can help computer to better understand human intelligence, human language and human thinking, effectively promote the Big Search in Cyberspace, effectively promote the construction of domain knowledge graphs (KGs), and bring enormous social and economic benefits. Due to the uncertainty of knowledge acquisition, the reliability and confidence of KG based on entity recognition and relationship extraction technology need to be evaluated. On the one hand, the process of multi-source knowledge reasoning can detect conflicts and provide help for knowledge evaluation and verification; on the other hand, the new knowledge acquired by knowledge reasoning is also uncertain and needs to be evaluated and verified. Collaborative reasoning of multi-source knowledge includes not only inferring new knowledge from multi-source knowledge, but also conflict detection, i.e. identifying erroneous knowledge or conflicts between knowledges. Starting from several related concepts of multi-source knowledge fusion, this paper comprehensively introduces the latest research progress of opensource knowledge fusion, multi-knowledge graphs fusion, information fusion within KGs, multi-modal knowledge fusion and multi-source knowledge collaborative reasoning. On this basis, the challenges and future research directions of multisource knowledge fusion in a large-scale knowledge base environment are discussed.

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Differentiating Concepts and Instances for Knowledge Graph Embedding

Cited by 82 publications

References 17 publications

Survey on graph embeddings and their applications to machine learning problems on graphs

Survey on graph embeddings and their applications to machine learning problems on graphs

Connecting Embeddings for Knowledge Graph Entity Typing

Multi-source knowledge fusion: a survey

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