Results 1 to 10 of about 7,645,086 (295)

KGvec2go -- Knowledge Graph Embeddings as a Service

CoRR, 2020
In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we serve pre-trained embeddings for four knowledge graphs. We introduce the service and its usage, and we show further that the trained models have semantic value by evaluating them on multiple ...
Portisch, Jan, Hladik, Michael, Paulheim, Heiko   +2 more
openaire   +4 more sources

Knowledge Graph Embedding by Normalizing Flows

Proceedings of the AAAI Conference on Artificial Intelligence, 2023
A key to knowledge graph embedding (KGE) is to choose a proper representation space, e.g., point-wise Euclidean space and complex vector space. In this paper, we propose a unified perspective of embedding and introduce uncertainty into KGE from the view of group theory.
XIAO, Changyi, HE, Xiangnan, CAO, Yixin
openaire   +3 more sources

Convolutional Complex Knowledge Graph Embeddings [PDF]

, 2021
In this paper, we study the problem of learning continuous vector representations of knowledge graphs for predicting missing links. We present a new approach called ConEx, which infers missing links by leveraging the composition of a 2D convolution with a Hermitian inner product of complex-valued embedding vectors.
Caglar Demir, Axel-Cyrille Ngonga Ngomo
openaire   +3 more sources

Embedding Uncertain Knowledge Graphs

Proceedings of the AAAI Conference on Artificial Intelligence, 2019
Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating the structured knowledge they contain into machine learning. However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of ...
Xuelu Chen, Muhao Chen 0001, Weijia Shi, Yizhou Sun, Carlo Zaniolo   +4 more
openaire   +4 more sources

ModulE: Module Embedding for Knowledge Graphs

CoRR, 2022
Knowledge graph embedding (KGE) has been shown to be a powerful tool for predicting missing links of a knowledge graph. However, existing methods mainly focus on modeling relation patterns, while simply embed entities to vector spaces, such as real field, complex field and quaternion space.
Jingxuan Chai, Guangming Shi
openaire   +3 more sources

Resilience in Knowledge Graph Embeddings

Trans. Graph Data Knowl.
In recent years, knowledge graphs have gained interest and witnessed widespread applications in various domains, such as information retrieval, question-answering, recommendation systems, amongst others. Large-scale knowledge graphs to this end have demonstrated their utility in effectively representing structured knowledge.
Sharma, Arnab, Kouagou, N'Dah Jean, Ngomo, Axel-Cyrille Ngonga   +2 more
openaire   +5 more sources

Semantically Smooth Knowledge Graph Embedding [PDF]

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2015
This paper considers the problem of embedding Knowledge Graphs (KGs) consisting of entities and relations into lowdimensional vector spaces. Most of the existing methods perform this task based solely on observed facts. The only requirement is that the learned embeddings should be compatible within each individual fact. In this paper, aiming at further
Shu Guo, Quan Wang 0002, Bin Wang 0004, Lihong Wang, Li Guo 0001   +4 more
openaire   +1 more source

Distance Based Korean WordNet(alias. KorLex) Embedding Model

Applied Artificial Intelligence
The objective of this study was to create graph embedding vectors using Korean WordNet (KorLex) and apply them to neural network word-embedding models.
SeongReol Park, JoongMin Shin, Sanghyun Cho, Hyuk-Chul Kwon, Jung-Hun Lee   +4 more
doaj   +1 more source

Knowledge graph embedding based on semantic hierarchy

Cognitive Robotics, 2022
In view of the current knowledge graph embedding, it mainly focuses on symmetry/opposition, inversion and combination of relationship patterns, and does not fully consider the structure of the knowledge graph. We propose a Knowledge Graph Embedding Based
Fan Linjuan, Sun Yongyong, Xu Fei, Zhou Hnghang   +3 more
doaj   +1 more source

Improving FMEA Comprehensibility via Common-Sense Knowledge Graph Completion Techniques

IEEE Access, 2023
The Failure Mode Effect Analysis process (FMEA) is widely used in industry for risk assessment, as it effectively captures and documents domain-specific knowledge. This process is mainly concerned with causal domain knowledge.
Houssam Razouk, Xing Lan Liu, Roman Kern
doaj   +1 more source