Results 71 to 80 of about 244,866 (345)

CIST: Differentiating Concepts and Instances Based on Spatial Transformation for Knowledge Graph Embedding

Mathematics, 2022
Knowledge representation learning is representing entities and relations in a knowledge graph as dense low-dimensional vectors in the continuous space, which explores the features and properties of the graph.
Pengfei Zhang, Dong Chen, Yang Fang, Xiang Zhao, Weidong Xiao   +4 more
doaj   +1 more source

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   +2 more sources

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

From wide to deep: dimension lifting network for parameter-efficient knowledge graph embedding [PDF]

Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph ...
Zhang, He, Gao, Longxiang, Jin, Jiong, Xiang, Yong, Wu, Di, Cai, Borui, Luan, Tom   +6 more
core   +1 more source

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   +3 more sources

Bringing Light Into the Dark: A Large-Scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
The heterogeneity in recently published knowledge graph embedding models’ implementations, training, and evaluation has made fair and thorough comparisons difficult.
Mehdi Ali, M. Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Mikhail Galkin, Sahand Sharifzadeh, Asja Fischer, Volker Tresp, Jens Lehmann   +8 more
semanticscholar   +1 more source

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

, 2017
A knowledge graph is a graph with entities of different types as nodes and various relations among them as edges. The construction of knowledge graphs in the past decades facilitates many applications, such as link prediction, web search analysis ...
Hailun Lin, Yantao Jia, Xueqi Cheng, Yuanzhuo Wang, Xiaolong Jin   +4 more
core   +1 more source

Knowledge Graph Embedding for Ecotoxicological Effect Prediction [PDF]

, 2019
Exploring the effects a chemical compound has on a species takes a considerable experimental effort. Appropriate methods for estimating and suggesting new effects can dramatically reduce the work needed to be done by a laboratory.
Jimenez-Ruiz, E., Hartig, Olaf, Tollefsen, Knut-Erik, Gandon, Fabien, Maleshkova, Maria,, Lefrançois, Maxime,, Svátek, Vojtěch,, On,, Myklebust, Erik Bryhn, Cruz, Isabel,, Svatek, Vojtech, Hartig, Olaf,, Wolf, R., Cruz, Isabel F., Gandon, Fabien,, Myklebust, E., Jimenez-Ruiz, Ernesto, Tollefsen, K. E., Song, Jie, Hogan, Aidan, Maleshkova, Maria, Cruz, Isabel, G, Fabien, Lefrançois, Maxime, Aidan, Hogan, Ghidini, Chiara,, Chen, J., Song, Jie,, Ghidini, Chiara, Wolf, Raoul, Hogan, Aidan,, Chen, Jiaoyan   +31 more
core   +1 more source