Linguistic Frames as Support for Entity Alignment in Knowledge Graphs

Costa, Gustavo de Assis; Oliveira, José M. Parente de

doi:10.1145/3282373.3282415

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…With the development of representation learning, scholars begin to use the information of relational and the text description of entities in multilingual knowledge graphs for cross-lingual representation learning. [77] jointly trained the embedding model of cross-lingual knowledge graphs and the embedding model of cross-lingual description. MTransE [78] solved the problem of representation learning and matching of crosslingual knowledge graphs through a transfer-based approach.…”

Section: Cross-lingual Knowledge Graph Fusionmentioning

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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Section: Cross-lingual Knowledge Graph Fusionmentioning

confidence: 99%

Multi-source knowledge fusion: a survey

Zhao

Jia

et al. 2020

World Wide Web

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A survey: knowledge graph entity alignment research based on graph embedding

Zhu,

Wang,

Wang

et al. 2024

Artif Intell Rev

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Entity alignment (EA) aims to automatically match entities in different knowledge graphs, which is beneficial to the development of knowledge-driven applications. Representation learning has powerful feature capture capability and it is widely used in the field of natural language processing. Compared with traditional EA methods, EA methods based on representation learning have better performance and efficiency. Hence, we summarize and analyze the representative EA approaches based on representation learning in this paper. We present the problem description and data preprocessing for EA and other related fundamental knowledge. We propose a new EA framework for the latest models, which includes information aggregation module, entity alignment module, and post-alignment module. Based on these three modules, the various technologies are described in detail. In the experimental part, we first explore the effect of EA direction on model performance. Then, we classify the models into different categories in terms of alignment inference strategy, noise filtering strategy, and whether additional information is utilized. To ensure fairness, we perform the comparative analysis of the performance of the models within the categories separately on different datasets. We investigate both unimodal and multimodal EA. Finally, we present future research perspectives based on the shortcomings of existing EA methods.

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Multi-source Knowledge Fusion: A Survey

Zhao

Jia

et al. 2019

2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC)

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Linguistic Frames as Support for Entity Alignment in Knowledge Graphs

Cited by 4 publications

References 15 publications

Multi-source knowledge fusion: a survey

Multi-source knowledge fusion: a survey

A survey: knowledge graph entity alignment research based on graph embedding

Multi-source Knowledge Fusion: A Survey

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