Unsupervised entity alignment using attribute triples and relation triples
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Unsupervised Entity Alignment using Attribute Triples and Relation Triples.pdf
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Type
Conference PaperAuthors
He, FuzhenLi, Zhixu
Qiang, Yang
Liu, An
Liu, Guanfeng
Zhao, Pengpeng
Zhao, Lei
Zhang, Min
Chen, Zhigang
Date
2019-04-24Online Publication Date
2019-04-24Print Publication Date
2019Permanent link to this record
http://hdl.handle.net/10754/656860
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Entity alignment aims to find entities referring to the same real-world object across different knowledge graphs (KGs). Most existing works utilize the relations between entities contained in the relation triples with embedding-based approaches, but require a large number of training data. Some recent attempt works on using types of their attributes in attribute triples for measuring the similarity between entities across KGs. However, due to diverse expressions of attribute names and non-standard attribute values across different KGs, the information contained in attribute triples can not be fully used. To tackle the drawbacks of the existing efforts, we novelly propose an unsupervised entity alignment approach using both attribute triples and relation triples of KGs. Initially, we propose an interactive model to use attribute triples by performing entity alignment and attribute alignment alternately, which will generate a lot of high-quality aligned entity pairs. We then use these aligned entity pairs to train a relation embedding model such that we could use relation triples to further align the remaining entities. Lastly, we utilize a bivariate regression model to learn the respective weights of similarities measuring from the two aspects for a result combination. Our empirical study performed on several real-world datasets shows that our proposed method achieves significant improvements on entity alignment compared with state-of-the-art methods.Citation
He, F., Li, Z., Qiang, Y., Liu, A., Liu, G., Zhao, P., … Chen, Z. (2019). Unsupervised Entity Alignment Using Attribute Triples and Relation Triples. Lecture Notes in Computer Science, 367–382. doi:10.1007/978-3-030-18576-3_22Sponsors
This research is partially supported by National Natural Science Foundation of China (Grant No. 61632016, 61572336, 61572335, 61772356), the Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003, 18KJA520010), and the Open Program of Neusoft Corporation (No. SKLSAOP1801).Publisher
Springer NatureConference/Event name
24th International Conference on Database Systems for Advanced Applications, DASFAA 2019Additional Links
http://link.springer.com/10.1007/978-3-030-18576-3_22ae974a485f413a2113503eed53cd6c53
10.1007/978-3-030-18576-3_22