A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Relation path embedding in knowledge graphs
2018
Neural computing & applications (Print)
Preserved Fulltext
paths and propose a novel relation path embedding model named as RPE. ...
They indicate that our model is capable of catching the semantics of relation paths, which is significant for knowledge representation learning. ...
In this paper, we propose a novel relation path embedding model (RPE) to explicitly model knowledge graph by taking full advantage of the semantics of relation paths. ...
doi:10.1007/s00521-018-3384-6
fatcat:tebn6t3vobg2loyxxv2a3rpx6u
Knowledge Graph Embeddings
[chapter]
2018
Encyclopedia of Big Data Technologies
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Learning knowledge graph embeddings Learning KG embeddings consists in two key steps in general: 1. ...
In a typical KG such as Freebase Bollacker et al (2008) or Google's Knowledge Graph Google (2014), entities are connected via relations. For example, Bern is capital of Switzerland. ...
doi:10.1007/978-3-319-63962-8_284-1
fatcat:xwmtv26vyrayvk3uwudy32xaz4
Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Methods for retrofitting pre-trained entity representations to the structure of a knowledge graph typically assume that entities are embedded in a connected space and that relations imply similarity. ...
Knowledge graphs are a versatile framework to encode richly structured data relationships, but it can be challenging to combine these graphs with unstructured data. ...
while the relation functions are learned. ...
arXiv:1708.00112v3
fatcat:z6bkghqgjbdaberikwyqvorf3u
PairRE: Knowledge Graph Embeddings via Paired Relation Vectors
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2020 pre-print arXiv:2011.03798v1 fatcat:inx6gwosovaurgogbouhunlenuOther Versions
Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to ...
Moreover, We set a new state-of-the-art on two knowledge graph datasets of the challenging Open Graph Benchmark. ...
The latter is learning and inferring relation patterns according to observed triples, as the success of knowledge graph completion heavily relies on this ability [3, 26] . ...
arXiv:2011.03798v3
fatcat:stgf3t2akjfx7p2c32ljr7fwea
Knowledge Graph Embedding with Hierarchical Relation Structure
2018
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Most existing researches are focusing on knowledge graph embedding (KGE) models. ...
To this end, in this paper, we extend existing KGE models TransE, TransH and Dist-Mult, to learn knowledge representations by leveraging the information from the HRS. ...
Introduction Knowledge Graphs (KGs) are extremely useful resources for many AI-related applications, such as question answering, information retrieval and query expansion. ...
doi:10.18653/v1/d18-1358
dblp:conf/emnlp/ZhangZQLH18
fatcat:u7m2hkpi5na7fcpwwmonlln5ru
Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Translational distance-based knowledge graph embedding has shown progressive improvements on the link prediction task, from TransE to the latest state-of-the-art RotatE. ...
The proposed method includes two-folds, first we extend the RotatE from 2D complex domain to high dimension space with orthogonal transforms to model relations for better modeling capacity. ...
On WN18RR our results achieve the new state-of-the-art performance. 2 Related work
Knowledge Graph Embedding Knowledge graph embedding could be roughly categorized into two classes : distance-based models ...
arXiv:1911.04910v3
fatcat:iuhb7wxduzbqlkiil3r6w3lpci
Knowledge Graph Embedding with Multiple Relation Projections
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Knowledge graphs contain rich relational structures of the world, and thus complement data-driven machine learning in heterogeneous data. ...
However, state-of-the-art relation projection methods such as TransR, TransD or TransSparse do not model the correlation between relations, and thus are not scalable to complex knowledge graphs with thousands ...
Holographic Embedding (HolE) [19] , a novel method leveraging the holographic models of associative memory to learn the compositional representations of knowledge graphs. ...
arXiv:1801.08641v1
fatcat:tksyzqblknbifn4nmyoyb6etwq
Interpreting Knowledge Graph Relation Representation from Word Embeddings
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be inferred. ...
We show that empirical properties of relation representations and the relative performance of leading knowledge graph representation methods are justified by our analysis. ...
of different knowledge graph models for each relation type. ...
arXiv:1909.11611v2
fatcat:ddgefluk3zfchoz5t4joxr754y
TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Knowledge graph embedding (KGE) aims to learn continuous vectors of relations and entities in knowledge graph. ...
In this paper, we propose a novel transition-based method, TranS, for knowledge graph embedding. ...
Conclusion In this paper, we propose a novel transition-based knowledge graph embedding model, TranS, to solve the issues of complex relations, especially the situation of same entity pair with different ...
arXiv:2204.08401v2
fatcat:n6zeh4dwtjcfvl6cmqt5mmeshe
Knowledge Embedding Based Graph Convolutional Network
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
propagation and the strengths of advanced knowledge embedding (a.k.a. knowledge graph embedding) methods, and goes beyond. ...
Most GCN methods are either restricted to graphs with a homogeneous type of edges (e.g., citation links only), or focusing on representation learning for nodes only instead of jointly propagating and updating ...
Especially, in order to capture the rich semantics of heterogeneous relations in knowledge graphs, both entity embeddings and relation embeddings in our model are used to enforce optimization of each other ...
arXiv:2006.07331v2
fatcat:lbbure7zsvhljdecmz6fssq6wi
QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We propose a simple yet effective embedding model to learn quaternion embeddings for entities and relations in knowledge graphs. ...
The model achieves this goal by further associating each relation with two relation-aware rotations, which are used to rotate quaternion embeddings of the head and tail entities, respectively. ...
CONCLUSION In this paper, we propose QuatRE -a simple yet effective knowledge graph embedding model -to learn the embeddings of entities and relations within the Quaternion space with the Hamilton product ...
arXiv:2009.12517v2
fatcat:alpth2t7cvhepibmqnrgddlpme
Embedding Uncertain Knowledge Graphs
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents ...
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 ...
Uncertain Knowledge Graph Embedding Problem. ...
arXiv:1811.10667v2
fatcat:joig6esu7jgqvfgnys3grohxt4
Embedding Uncertain Knowledge Graphs
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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 ...
However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents ...
Uncertain Knowledge Graph Embedding Problem. ...
doi:10.1609/aaai.v33i01.33013363
fatcat:vqxhhyj3e5cfli5thjehdld5ga
Federated Knowledge Graphs Embedding
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In this paper, we propose a novel decentralized scalable learning framework, Federated Knowledge Graphs Embedding (FKGE), where embeddings from different knowledge graphs can be learnt in an asynchronous ...
FKGE exploits adversarial generation between pairs of knowledge graphs to translate identical entities and relations of different domains into near embedding spaces. ...
RELATED WORK As our work is closely related to federated learning and knowledge graph embedding, the related works are two-fold. ...
arXiv:2105.07615v1
fatcat:il5oopbv65cuzhtyk4ciyqr74u
Relation Embedding with Dihedral Group in Knowledge Graph
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
To fulfill this gap, we propose a new model called DihEdral, named after dihedral symmetry group. This new model learns knowledge graph embeddings that can capture relation compositions by nature. ...
Link prediction is critical for the application of incomplete knowledge graph (KG) in the downstream tasks. ...
composition are coherent with the relation embeddings learned from DihEdral. ...
doi:10.18653/v1/p19-1026
dblp:conf/acl/XuL19
fatcat:ekc67addibfkrliycybjsuqk24
« Previous
Showing results 1 — 15 out of 121,473 results