The framework uses a combination of superficial similarities to kickstart the machine learning process that produces aligned entity pairs. Credit: Tingting Jiang et al.
A team of researchers led by professor Xindong Wu in Hefei, China has developed an unsupervised entity alignment framework to improve the process of searching for related information in multiple knowledge graphs for artificial intelligence applications. The framework brings together the advantages of multiple approaches and avoids relying on human labor to kickstart the alignment process.
They tested their framework on several cross-lingual datasets and measured the results, comparing them against the results of 14 other machine …
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