Encoding Category Correlations into Bilingual Topic Modeling for Cross-Lingual Taxonomy Alignment

Wu, Tianxing; Zhang, Lei; Qi, Guilin; Cui, Xuan

doi:10.1007/978-3-319-68288-4_43

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Wu et al [82] used the bilingual topic model to solve the cross-lingual ontology matching problem, proposed the modeling of disordered word pairs (called BiBTM) in bilingual documents. On this basis, the word co-occurrence relationship and hierarchical structure relationship between classes are further added, that is, the subsequent C-BiBTM [83]. In order to solve the problem of cross-lingual attribute matching, Zhang et al [84] proposed the EAFG model, which not only considers the characteristics of attributes to itself, but also considers the correlation between attributes.…”

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%