Relational Knowledge Prediction via Dynamic Bi-Mode Embedding
Knowledge graphs are a crucial concept in artificial intelligence with a wide spectrum of real-life applications. Nonetheless, they are currently suffering from the incompleteness issue, i.e., relational knowledge in the graphs may not yet meet the practical needs. To address this issue, mainstream...
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8353191/ |
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| author | Yang Fang Xiang Zhao Zhen Tan Weidong Xiao |
| author_facet | Yang Fang Xiang Zhao Zhen Tan Weidong Xiao |
| author_sort | Yang Fang |
| collection | DOAJ |
| description | Knowledge graphs are a crucial concept in artificial intelligence with a wide spectrum of real-life applications. Nonetheless, they are currently suffering from the incompleteness issue, i.e., relational knowledge in the graphs may not yet meet the practical needs. To address this issue, mainstream solutions propose to predict links by using compositional models or translation models. However, the prediction accuracy is still of particular concern. In this paper, we propose a new method, namely, <italic>Bi-Mult</italic>, which combines the advantages of compositional models and translation models. <italic>Bi-Mult</italic> is based on the compositional model, such that an entity (resp. relation) embedding is decomposed into two parts, one is to represent intra-entity (resp. relation) state and the other is for inter-entity (resp. relation) state, and we call such embedding as bi-mode embedding. In addition, the bi-mode relation embedding enhances relation’s interaction with entities, resulting its improvement on handling antisymmetric relations. Moreover, we incorporate mapping matrices in translation models through bi-mode entity embedding to construct dynamic embeddings for expressing complex relations, such as “1-to-N”, “N-to-1,” and “N-to-N” relations. In experiments, we evaluate our method on the benchmark data sets and the task of link prediction, and our method is demonstrated to outperform state-of-the-art methods consistently and significantly. |
| format | Article |
| id | doaj-art-a07c29d7ddb1441b95a59d21a9f811e9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a07c29d7ddb1441b95a59d21a9f811e92025-08-20T04:02:18ZengIEEEIEEE Access2169-35362018-01-016257152572310.1109/ACCESS.2018.28321658353191Relational Knowledge Prediction via Dynamic Bi-Mode EmbeddingYang Fang0Xiang Zhao1https://orcid.org/0000-0001-6339-0219Zhen Tan2Weidong Xiao3Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, ChinaScience and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, ChinaScience and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, ChinaScience and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, ChinaKnowledge graphs are a crucial concept in artificial intelligence with a wide spectrum of real-life applications. Nonetheless, they are currently suffering from the incompleteness issue, i.e., relational knowledge in the graphs may not yet meet the practical needs. To address this issue, mainstream solutions propose to predict links by using compositional models or translation models. However, the prediction accuracy is still of particular concern. In this paper, we propose a new method, namely, <italic>Bi-Mult</italic>, which combines the advantages of compositional models and translation models. <italic>Bi-Mult</italic> is based on the compositional model, such that an entity (resp. relation) embedding is decomposed into two parts, one is to represent intra-entity (resp. relation) state and the other is for inter-entity (resp. relation) state, and we call such embedding as bi-mode embedding. In addition, the bi-mode relation embedding enhances relation’s interaction with entities, resulting its improvement on handling antisymmetric relations. Moreover, we incorporate mapping matrices in translation models through bi-mode entity embedding to construct dynamic embeddings for expressing complex relations, such as “1-to-N”, “N-to-1,” and “N-to-N” relations. In experiments, we evaluate our method on the benchmark data sets and the task of link prediction, and our method is demonstrated to outperform state-of-the-art methods consistently and significantly.https://ieeexplore.ieee.org/document/8353191/Knowledge embeddingrepresentation learning |
| spellingShingle | Yang Fang Xiang Zhao Zhen Tan Weidong Xiao Relational Knowledge Prediction via Dynamic Bi-Mode Embedding IEEE Access Knowledge embedding representation learning |
| title | Relational Knowledge Prediction via Dynamic Bi-Mode Embedding |
| title_full | Relational Knowledge Prediction via Dynamic Bi-Mode Embedding |
| title_fullStr | Relational Knowledge Prediction via Dynamic Bi-Mode Embedding |
| title_full_unstemmed | Relational Knowledge Prediction via Dynamic Bi-Mode Embedding |
| title_short | Relational Knowledge Prediction via Dynamic Bi-Mode Embedding |
| title_sort | relational knowledge prediction via dynamic bi mode embedding |
| topic | Knowledge embedding representation learning |
| url | https://ieeexplore.ieee.org/document/8353191/ |
| work_keys_str_mv | AT yangfang relationalknowledgepredictionviadynamicbimodeembedding AT xiangzhao relationalknowledgepredictionviadynamicbimodeembedding AT zhentan relationalknowledgepredictionviadynamicbimodeembedding AT weidongxiao relationalknowledgepredictionviadynamicbimodeembedding |