TY - GEN

T1 - AEVis: A Visualization Method to Facilitate Understanding Data and Entity Alignment Results

AU - Apanovich, Zinaida

AU - Kolganova, Arina

PY - 2024/11/26

Y1 - 2024/11/26

N2 - The cross-lingual Entity Alignment algorithms are designed to look for identical real-world objects in multilingual knowledge graphs. This problem occurs, for example, when searching for drugs, loT and other devices manufactured in different countries under different names. Embedding-based Entity Alignment algorithms have recently become extremely popular. The idea behind these algorithms is to obtain the descriptions of entities and relationships in the form of low-dimensional vectors so that the similarity or equivalence of entities and relationships is represented by the distance between their vectors. The advantage of the embedding-based EA approach is high scalability and low effort in preparing a test data set. Its main disadvantage is poor comprehensibility. This paper presents a visualization algorithm combining two visualizations: the conventional t-sne and graph-based. The set of options for the visualization tool developed makes it easier to understand not only the results of various entity alignment algorithms, but also the structure of the test data set.

AB - The cross-lingual Entity Alignment algorithms are designed to look for identical real-world objects in multilingual knowledge graphs. This problem occurs, for example, when searching for drugs, loT and other devices manufactured in different countries under different names. Embedding-based Entity Alignment algorithms have recently become extremely popular. The idea behind these algorithms is to obtain the descriptions of entities and relationships in the form of low-dimensional vectors so that the similarity or equivalence of entities and relationships is represented by the distance between their vectors. The advantage of the embedding-based EA approach is high scalability and low effort in preparing a test data set. Its main disadvantage is poor comprehensibility. This paper presents a visualization algorithm combining two visualizations: the conventional t-sne and graph-based. The set of options for the visualization tool developed makes it easier to understand not only the results of various entity alignment algorithms, but also the structure of the test data set.

KW - embedding

KW - entity alignment algorithms

KW - multilingual knowledge graphs

KW - visualization

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85212062203&origin=inward&txGid=1b54350dc1463b2014749d54e836615f

UR - https://www.mendeley.com/catalogue/f49f6035-193e-30e4-a38a-c8c17f3151c7/

U2 - 10.1109/SIBIRCON63777.2024.10758503

DO - 10.1109/SIBIRCON63777.2024.10758503

M3 - Conference contribution

SN - 9798331532024

T3 - 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2024

SP - 391

EP - 395

BT - 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2024

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences

Y2 - 30 September 2024 through 2 November 2024

ER -