The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
언어 간 엔터티 정렬의 목표는 현실 세계에서 동일한 개체를 나타내는 다양한 언어의 지식 그래프에서 엔터티를 일치시키는 것입니다. 다양한 언어의 지식 그래프는 엔터티 정렬에 유용할 수 있는 동일한 온톨로지를 공유할 수 있습니다. 이 아이디어를 검증하기 위해 TransC 기반의 새로운 임베딩 모델을 제안합니다. 이 모델은 먼저 TransC 및 매개변수 공유 모델을 채택하여 지식 그래프의 모든 엔터티와 관계를 정렬된 엔터티 집합을 기반으로 공유된 저차원 의미 공간에 매핑합니다. 그런 다음 모델은 다시 초기화 및 소프트 정렬 전략을 반복적으로 사용하여 엔터티 정렬을 수행합니다. 실험 결과를 통해 제안한 모델이 벤치마크 알고리즘과 비교하여 온톨로지 정보를 효과적으로 융합하고 상대적으로 더 나은 결과를 얻을 수 있음을 보여주었다.
Shize KANG
Information Engineering University
Lixin JI
Information Engineering University
Zhenglian LI
Beijing Information and Communication Research Center
Xindi HAO
Beijing Information and Communication Research Center
Yuehang DING
Information Engineering University
다국어 엔터티 정렬, 존재론, 지식 임베딩, 반복 정렬
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Shize KANG, Lixin JI, Zhenglian LI, Xindi HAO, Yuehang DING, "Iterative Cross-Lingual Entity Alignment Based on TransC" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 5, pp. 1002-1005, May 2020, doi: 10.1587/transinf.2019DAL0001.
Abstract: The goal of cross-lingual entity alignment is to match entities from knowledge graph of different languages that represent the same object in the real world. Knowledge graphs of different languages can share the same ontology which we guess may be useful for entity alignment. To verify this idea, we propose a novel embedding model based on TransC. This model first adopts TransC and parameter sharing model to map all the entities and relations in knowledge graphs to a shared low-dimensional semantic space based on a set of aligned entities. Then, the model iteratively uses reinitialization and soft alignment strategy to perform entity alignment. The experimental results show that, compared with the benchmark algorithms, the proposed model can effectively fuse ontology information and achieve relatively better results.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019DAL0001/_p
부
@ARTICLE{e103-d_5_1002,
author={Shize KANG, Lixin JI, Zhenglian LI, Xindi HAO, Yuehang DING, },
journal={IEICE TRANSACTIONS on Information},
title={Iterative Cross-Lingual Entity Alignment Based on TransC},
year={2020},
volume={E103-D},
number={5},
pages={1002-1005},
abstract={The goal of cross-lingual entity alignment is to match entities from knowledge graph of different languages that represent the same object in the real world. Knowledge graphs of different languages can share the same ontology which we guess may be useful for entity alignment. To verify this idea, we propose a novel embedding model based on TransC. This model first adopts TransC and parameter sharing model to map all the entities and relations in knowledge graphs to a shared low-dimensional semantic space based on a set of aligned entities. Then, the model iteratively uses reinitialization and soft alignment strategy to perform entity alignment. The experimental results show that, compared with the benchmark algorithms, the proposed model can effectively fuse ontology information and achieve relatively better results.},
keywords={},
doi={10.1587/transinf.2019DAL0001},
ISSN={1745-1361},
month={May},}
부
TY - JOUR
TI - Iterative Cross-Lingual Entity Alignment Based on TransC
T2 - IEICE TRANSACTIONS on Information
SP - 1002
EP - 1005
AU - Shize KANG
AU - Lixin JI
AU - Zhenglian LI
AU - Xindi HAO
AU - Yuehang DING
PY - 2020
DO - 10.1587/transinf.2019DAL0001
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2020
AB - The goal of cross-lingual entity alignment is to match entities from knowledge graph of different languages that represent the same object in the real world. Knowledge graphs of different languages can share the same ontology which we guess may be useful for entity alignment. To verify this idea, we propose a novel embedding model based on TransC. This model first adopts TransC and parameter sharing model to map all the entities and relations in knowledge graphs to a shared low-dimensional semantic space based on a set of aligned entities. Then, the model iteratively uses reinitialization and soft alignment strategy to perform entity alignment. The experimental results show that, compared with the benchmark algorithms, the proposed model can effectively fuse ontology information and achieve relatively better results.
ER -