Russian-English Dataset and Entity Alignment in Knowledge Graphs with Unmatchable Entities
Main Article Content
Abstract
In recent years, interest in knowledge graphs (KGs) has increased exponentially in both the scientific and industrial communities. Integration of various KGs is a pressing problem and is used, for example, to develop complex digital twins of industrial systems. Knowledge graph integration is also necessary when combining KGs extracted from natural language texts using large language models. One component of solving the KG integration problem is entity alignment (EA), which attempts to identify entities in different KGs that describe the same real-world object. In reality, many entities in real KGs have no equivalents in other KGs. In particular, each knowledge graph fragment extracted from a single publication may have its own structure of entity names and identifiers, which significantly complicates the task of identifying entities. This paper describes experiments on entity alignment in the presence of unmatchable entities using a Russian-English dataset as an example.
Article Details
References
2. Gusev D., Apanovich Z. Methods of processing textual information in entity alignment algorithms // Bulletin of the Novosibirsk Computing Center. Series: Computer Science. 2021. No. 45. P. 49–58. https://doi.org/10.31144/bncc.cs.2542-1972.2021.n45.p49-58
3. Lample G., Conneau A., Ranzato M., Denoyer L., Jégou H. Word translation without parallel data // ICLR. OpenReview.net. 2018. https://doi.org/10.48550/arXiv.1710.04087
4. W. Zeng, X. Zhao, X. Li, J. Tang, W. Wang. On entity alignment at scale // The VLDB Journal. 2022. Vol. 31. Issue 5. P. 1009–1033. https://doi.org/10.1007/s00778-021-00703-3
5. Kuhn H.W. The hungarian method for the assignment problem. URL: https://web.eecs.umich.edu/~pettie/matching/Kuhn-hungarian-assignment.pdf. https://doi.org/10.1002/nav.3800020109
6. W. Zeng, X. Zhao, J. Tang, X. Lin. Collective entity alignment via adaptive features // ICDE. 2020. P. 1870–1873. https://doi.org/10.48550/arXiv.1912.08404
7. R. Zhu, M. Ma, P. Wang. RAGA: relation-aware graph attention networks for global entity alignment // PAKDD. 2021. Vol. 12712. P. 501–513. https://doi.org/10.48550/arXiv.2103.00791
8. Xin M., Wenting W., Huimin X. et al. Relational Reflection Entity Alignment. // arXiv.org. 2020. https://doi.org/10.48550/arXiv.2008.07962
9. Zhao X., Zeng W., Tang J., Recent Advance of Alignment Inference Stage // Entity Alignment. Springer Nature, Singapore, 2023. P. 207–227. https://doi.org/10.1007/978-981-99-4250-3
10. Zhao X., Zeng W., Tang J. et al. Toward Entity Alignment in the Open World: An Unsupervised Approach with Confidence Modeling // Data Sci. Eng. 2022. No. 7. P. 16–29. https://doi.org/10.1007/s41019-022-00178-4
11. Feng F., Yang Y., Cer D., Arivazhagan N., Wang W. Language-agnostic BERT Sentence Embedding // ArXiv. 2020. https://doi.org/10.48550/arXiv.2007.01852
This work is licensed under a Creative Commons Attribution 4.0 International License.
Presenting an article for publication in the Russian Digital Libraries Journal (RDLJ), the authors automatically give consent to grant a limited license to use the materials of the Kazan (Volga) Federal University (KFU) (of course, only if the article is accepted for publication). This means that KFU has the right to publish an article in the next issue of the journal (on the website or in printed form), as well as to reprint this article in the archives of RDLJ CDs or to include in a particular information system or database, produced by KFU.
All copyrighted materials are placed in RDLJ with the consent of the authors. In the event that any of the authors have objected to its publication of materials on this site, the material can be removed, subject to notification to the Editor in writing.
Documents published in RDLJ are protected by copyright and all rights are reserved by the authors. Authors independently monitor compliance with their rights to reproduce or translate their papers published in the journal. If the material is published in RDLJ, reprinted with permission by another publisher or translated into another language, a reference to the original publication.
By submitting an article for publication in RDLJ, authors should take into account that the publication on the Internet, on the one hand, provide unique opportunities for access to their content, but on the other hand, are a new form of information exchange in the global information society where authors and publishers is not always provided with protection against unauthorized copying or other use of materials protected by copyright.
RDLJ is copyrighted. When using materials from the log must indicate the URL: index.phtml page = elbib / rus / journal?. Any change, addition or editing of the author's text are not allowed. Copying individual fragments of articles from the journal is allowed for distribute, remix, adapt, and build upon article, even commercially, as long as they credit that article for the original creation.
Request for the right to reproduce or use any of the materials published in RDLJ should be addressed to the Editor-in-Chief A.M. Elizarov at the following address: amelizarov@gmail.com.
The publishers of RDLJ is not responsible for the view, set out in the published opinion articles.
We suggest the authors of articles downloaded from this page, sign it and send it to the journal publisher's address by e-mail scan copyright agreements on the transfer of non-exclusive rights to use the work.