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Incorporating Literals into Knowledge Graph Embeddings [article]

Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer
2019 arXiv   pre-print
Knowledge graphs, on top of entities and their relationships, contain other important elements: literals.  ...  In this work, we study the effect of incorporating literal information into existing link prediction methods.  ...  In this paper, we introduced LiteralE: a simple method to incorporate literals into latent feature methods for knowledge graphs analysis.  ... 
arXiv:1802.00934v3 fatcat:tn6yelj64ffmbfornmrqmu37ai

A First Experiment on Including Text Literals in KGloVe [article]

Michael Cochez and Martina Garofalo and Jérôme Lenßen and Maria Angela Pellegrino
2018 arXiv   pre-print
Graph embedding models produce embedding vectors for entities and relations in Knowledge Graphs, often without taking literal properties into account.  ...  We show an initial idea based on the combination of global graph structure with additional information provided by textual information in properties.  ...  In this paper we present a preliminary experiment on our investigation with a new model to incorporate the information contained in literals in a Knowledge Graph into the embedding.  ... 
arXiv:1807.11761v1 fatcat:lh7cnswtwzef7jphshxpzgsgjm

Joint Embedding Learning of Educational Knowledge Graphs [article]

Siyu Yao, Ruijie Wang, Shen Sun, Derui Bu, Jun Liu
2019 arXiv   pre-print
Instead, rich literals of the graphs are more valuable. In this paper, we focus on this problem and propose a novel model for embedding learning of educational knowledge graphs.  ...  And there is a steady trend of learning embedding representations of knowledge graphs to facilitate knowledge graph construction and downstream tasks.  ...  SimplE [23] incorporates certain types of background knowledge into the model by weight tying.  ... 
arXiv:1911.08776v2 fatcat:23gvecmwf5gzhdxmui4ry5tbzu

Synonym Knowledge Enhanced Reader for Chinese Idiom Reading Comprehension [article]

Siyu Long and Ran Wang and Kun Tao and Jiali Zeng and Xin-Yu Dai
2020 arXiv   pre-print
then incorporate the graph attention network and gate mechanism to encode the graph.  ...  Specifically, for each idiom, we first construct a synonym graph according to the annotations from a high-quality synonym dictionary or the cosine similarity between the pre-trained idiom embeddings and  ...  ., 2013) , integrating combination rules , or incorporating the general linguistic knowledge (Qi et al., 2019) . By combining the word embeddings, we can alleviate the sparsity of idioms.  ... 
arXiv:2011.04499v1 fatcat:3err7otdkjh45n7bzn3lbfyani

Embedding Knowledge for Document Summarization: A Survey [article]

Yutong Qu, Wei Emma Zhang, Jian Yang, Lingfei Wu, Jia Wu, Xindong Wu
2022 arXiv   pre-print
Particularly, we propose novel taxonomies to recapitulate knowledge and knowledge embeddings under the document summarization view.  ...  With the gathered momentum, knowledge recently has been pumped into enormous attention in document summarization research.  ...  embeddings when incorporating the knowledge into models.  ... 
arXiv:2204.11190v1 fatcat:cyt5sdspcbeq3h3gfoi5xfdbem

End-to-End Entity Classification on Multimodal Knowledge Graphs [article]

W.X. Wilcke , F.A.H. van Harmelen Department of Computer Science Vrije Universiteit Amsterdam The Netherlands
2020 arXiv   pre-print
End-to-end multimodal learning on knowledge graphs has been left largely unaddressed.  ...  Our model uses dedicated (neural) encoders to naturally learn embeddings for node features belonging to five different types of modalities, including images and geometries, which are projected into a joint  ...  Acknowledgments We express our gratitude to Wouter Beek from Triply 9 for helping us obtain two of the three knowledge graphs that make up the Dutch Monument Graph.  ... 
arXiv:2003.12383v1 fatcat:z5qqcjqaurftzakgf3lagie25y

Aligning Cross-Lingual Entities with Multi-Aspect Information

Hsiu-Wei Yang, Yanyan Zou, Peng Shi, Wei Lu, Jimmy Lin, Xu SUN
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages.  ...  In this work, we investigate embedding-based approaches to encode entities from multilingual KGs into the same vector space, where equivalent entities are close to each other.  ...  Integration Strategy Sections 3.1 and 3.2 introduce two modules that separately collect evidence from knowledge graph structures and the literal descriptions of entities, namely graph and textual embeddings  ... 
doi:10.18653/v1/d19-1451 dblp:conf/emnlp/YangZSLLS19 fatcat:qxl4mjkxcnchfpoo7wy43hx6cm

Aligning Cross-Lingual Entities with Multi-Aspect Information [article]

Hsiu-Wei Yang, Yanyan Zou, Peng Shi, Wei Lu, Jimmy Lin, Xu Sun
2019 arXiv   pre-print
Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages.  ...  In this work, we investigate embedding-based approaches to encode entities from multilingual KGs into the same vector space, where equivalent entities are close to each other.  ...  Integration Strategy Sections 3.1 and 3.2 introduce two modules that separately collect evidence from knowledge graph structures and the literal descriptions of entities, namely graph and textual embeddings  ... 
arXiv:1910.06575v1 fatcat:f4lhfutlgfcajihhsoqasaatlq

Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding [article]

Wenqiang Liu, Hongyun Cai, Xu Cheng, Sifa Xie, Yipeng Yu, Hanyu Zhang
2019 arXiv   pre-print
The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces.  ...  Many recent works have demonstrated the benefits of knowledge graph embedding on knowledge graph completion task, such as relation extraction.  ...  Recent attempts can be divided into two categories: (i) those which tries to incorporate additional information to further improve the performance of knowledge graph embedding, e.g., entity types or concepts  ... 
arXiv:1910.03891v2 fatcat:xwojhphmubhr7ntmn6zvj6im6a

Incorporating Phrases in Latent Query Reformulation for Multi-Hop Question Answering

Jiuyang Tang, Shengze Hu, Ziyang Chen, Hao Xu, Zhen Tan
2022 Mathematics  
In summary, by incorporating phrases in the latent query reformulation and employing semantic-augmented embedding fusion, our proposed model can lead to better performance on MH-QA.  ...  The process might add an entity irrelevant to the answer to the graph and then lead to incorrect predictions.  ...  ., knowledge graph) of the question.  ... 
doi:10.3390/math10040646 fatcat:3rnfmvdnvvdyzbw7x47qq2aodm

A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly? [article]

Genet Asefa Gesese, Russa Biswas, Mehwish Alam, Harald Sack
2020 arXiv   pre-print
Knowledge Graphs (KGs) are composed of structured information about a particular domain in the form of entities and relations.  ...  Hence, there arises the necessity for a representation able to map the high dimensional KGs into low dimensional spaces, i.e., embedding space, preserving structural as well as relational information.  ...  Knowledge Graph Embeddings with Literals This section investigates KG embedding models with literals divided into the following different categories based on the types of literals utilized: (i) Text, (  ... 
arXiv:1910.12507v2 fatcat:j3roq6x4jfahnikk3yc4zclmlu

MMKG: Multi-Modal Knowledge Graphs [article]

Ye Liu, Hui Li, Alberto Garcia-Duran, Mathias Niepert, Daniel Onoro-Rubio, David S. Rosenblum
2019 arXiv   pre-print
We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs.  ...  We believe this data set has the potential to facilitate the development of novel multi-modal learning approaches for knowledge graphs.We validate the utility ofMMKG in the sameAs link prediction task  ...  Furthermore, all three KGs are enriched with numeric literals and image information, as well as sameAs predicates linking entities from pairs of knowledge graphs. sameAs predicates, numerical literals  ... 
arXiv:1903.05485v1 fatcat:mapil3zlcjbipnkzgskufclcaa

Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment [article]

Zhiyuan Liu, Yixin Cao, Liangming Pan, Juanzi Li, Zhiyuan Liu, Tat-Seng Chua
2021 arXiv   pre-print
Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs.  ...  In this paper, we propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently.  ...  Related Work Recent entity alignment methods can be classified into embedding-based methods and Graph Neural Network-based (GNN-based) methods.  ... 
arXiv:2010.03249v2 fatcat:mq4xitmoevc5rnszdimxkvqxmm

Embedding based Link Prediction for Knowledge Graph Completion

Russa Biswas
2020 Proceedings of the 29th ACM International Conference on Information & Knowledge Management  
Knowledge Graphs (KGs) have recently gained attention for representing knowledge about a particular domain.  ...  The main focus of this thesis is to do Knowledge Graph Completion by tackling the link prediction tasks within a KG as well as across different KGs.  ...  paths, models using logical rules, models with temporal information, models using graph structures, and models incorporating information represented in literals.  ... 
doi:10.1145/3340531.3418512 dblp:conf/cikm/Biswas20 fatcat:bgsrwzlh6bcbzeydijfc6ek7bu

pyRDF2Vec: A Python Implementation and Extension of RDF2Vec [article]

Gilles Vandewiele, Bram Steenwinckel, Terencio Agozzino, Femke Ongenae
2022 arXiv   pre-print
The package is released under a MIT license and structured in such a way to foster further research into sampling, walking, and embedding strategies, which are vital components of the RDF2Vec algorithm  ...  By making the algorithm available in the most popular data science language, and by bundling all extensions into a single place, the use of RDF2Vec is simplified for data scientists.  ...  Introduction Knowledge Graphs (KGs) are an ideal candidate to perform hybrid Machine Learning (ML) where both background and observational knowledge are taken into account to construct predictive models  ... 
arXiv:2205.02283v1 fatcat:77arw2y5qzd33a4y7xsd6batfa
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