Filters








59,767 Hits in 3.7 sec

Path-Enhanced Multi-Relational Question Answering with Knowledge Graph Embeddings [article]

Guanglin Niu, Yang Li, Chengguang Tang, Zhongkai Hu, Shibin Yang, Peng Li, Chengyu Wang, Hao Wang, Jian Sun
2021 arXiv   pre-print
Recent approaches attempt to introduce the knowledge graph embedding (KGE) technique to handle the KG incompleteness but only consider the triple facts and neglect the significant semantic correlation  ...  In this paper, we propose a Path and Knowledge Embedding-Enhanced multi-relational Question Answering model (PKEEQA), which leverages multi-hop paths between entities in the KG to evaluate the ambipolar  ...  Inspired by the KG embedding technique for KG completion, EmbedKGQA (Saxena et al., 2020) employs the pre-trained entity embeddings by KGE and the question embedding to calculate the score of each candidate  ... 
arXiv:2110.15622v1 fatcat:vghuvwptlnfntoyb7zpu56ia2i

RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion [article]

Youri Xu, E Haihong, Meina Song, Wenyu Song, Xiaodong Lv, Wang Haotian, Yang Jinrui
2021 arXiv   pre-print
Static knowledge graph (SKG) embedding (SKGE) has been studied intensively in the past years. Recently, temporal knowledge graph (TKG) embedding (TKGE) has emerged.  ...  In this paper, we propose a Recursive Temporal Fact Embedding (RTFE) framework to transplant SKGE models to TKGs and to enhance the performance of existing TKGE models for TKG completion.  ...  Acknowledgements This work was supported in part by the National Science Foundation of China (Grant No. 61902034); Engineering Research Center of Information Networks, Ministry of Education.  ... 
arXiv:2009.14653v4 fatcat:63torcfukrhibif2w3fvbftece

Unifying Task-oriented Knowledge Graph Learning and Recommendation

Qianyu Li, Xiaoli Tang, Tengyun Wang, Haizhi Yang, Hengjie Song
2019 IEEE Access  
The RCoLM aims at transferring knowledge between recommendation task and knowledge graph completion (KG completion) task by utilizing a transfer learning model.  ...  from recommendations for KG completion, and unifies the two tasks in a joint model for mutual enhancements.  ...  Due to negative sampling triples of relation, RCoLM can utilize and maintain the structural information in the knowledge graph more comprehensively, and it further enhances the performance of the embedding  ... 
doi:10.1109/access.2019.2932466 fatcat:wvlchwdaynblbfymecyt5p3m7y

Association Rules Enhanced Knowledge Graph Attention Network [article]

Zhenghao Zhang, Jianbin Huang, Qinglin Tan
2020 arXiv   pre-print
Most existing knowledge graphs suffer from incompleteness. Embedding knowledge graphs into continuous vector spaces has recently attracted increasing interest in knowledge base completion.  ...  To overcome the problem, we propose an association rules enhanced knowledge graph attention network (AR-KGAT).  ...  ACKNOWLEDGMENTS The work was supported by the National Natural Science Foundation of China [grant numbers: 61876138, 61602354].  ... 
arXiv:2011.08431v1 fatcat:l6sfja6ycng7bkszyclgih5qiu

Leveraging Concept-Enhanced Pre-Training Model and Masked-Entity Language Model for Named Entity Disambiguation

Zizheng Ji, Lin Dai, Jin Pang, Tingting Shen
2020 IEEE Access  
Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in an input-text sequence to their correct references in a knowledge graph.  ...  Therefore, the proposed pre-training NED model could merge the advantage of pre-training mechanism for generating contextualized embedding with the superiority of the lexical knowledge (e.g., concept knowledge  ...  graph completion [13] , [14] , [23] , [45] .  ... 
doi:10.1109/access.2020.2994247 fatcat:m5e2h3w5gnat3imgre6e5swx5e

Scientific and Technological Resource Sharing Model Based on Few-Shot Relational Learning

Yangshengyan LIU, Fu GU, Yangjian JI, Yijie WU, Jianfeng GUO, Xinjian GU, Jin ZHANG
2021 IEICE transactions on information and systems  
Here we propose a novel method to share scientific and technological resources by storing resources as nodes and correlations as links to form a complex network.  ...  We also show two practical applications, a resource graph and a resource map, to demonstrate how the complex network helps resource sharing.  ...  the original knowledge is kept and enhanced.  ... 
doi:10.1587/transinf.2020bdp0021 fatcat:caoeiwdfmjawfb6din5zurhao4

Learning Rare Word Representations using Semantic Bridging [article]

Victor Prokhorov, Mohammad Taher Pilehvar, Dimitri Kartsaklis, Pietro Lió, Nigel Collier
2017 arXiv   pre-print
Squares and Canonical Correlation Analysis) in order to merge the corpus and ontological sources of lexical knowledge.  ...  We propose a methodology that adapts graph embedding techniques (DeepWalk (Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016)) as well as cross-lingual vector space mapping approaches (Least  ...  Acknowledgments This research was supported by EPSRC Experienced Researcher Fellowship (Nigel Collier, Dimitri Kartsaklis, (EP/M005089/1)), MRC grant (Mohammad Taher Pilehvar, (MR/M025160/1)) We gratefully  ... 
arXiv:1707.07554v1 fatcat:pwhyz3z6kzfutjavty7auszc3u

Semantically Enhanced Models for Commonsense Knowledge Acquisition [article]

Ikhlas Alhussien, Erik Cambria, Zhang NengSheng
2018 arXiv   pre-print
The proposed models enhance in a knowledge graph embedding (KGE) framework for knowledge base completion.  ...  To address these challenges, this paper presents semantically enhanced models to enable reasoning through resolving part of commonsense ambiguity.  ...  We use semantically enhanced embeddings to perform knowledge base completion (KBC), a technique that perform reasoning over existing knowledge in supervised manner to predict missing assertions by filling  ... 
arXiv:1809.04708v2 fatcat:mzvmg56c5bchtltayiwsgk3lki

A Model of Text-Enhanced Knowledge Graph Representation Learning with Mutual Attention

Yashen Wang, Huanhuan Zhang, Ge Shi, Zhirun Liu, Qiang Zhou
2020 IEEE Access  
Recently, it has gained lots of interests to jointly learn the embeddings of knowledge graph (KG) and text information.  ...  This paper proposes a novel text-enhanced knowledge graph representation model, which can utilize textual information to enhance the knowledge representations.  ...  EXPERIMENTS We evaluate our proposed text-enhanced knowledge graph representation model with mutual attention based on Knowledge Graph Completion (KGC) task, mainly consists of: Link Prediction (Section  ... 
doi:10.1109/access.2020.2981212 fatcat:6uhn2qkedjcqrln3hd6xdy2zbq

Enhancing Semantic Word Representations by Embedding Deeper Word Relationships [article]

Anupiya Nugaliyadde, Kok Wai Wong, Ferdous Sohel, Hong Xie
2019 arXiv   pre-print
Embedding deeper word relationships which are not represented in the context enhances the word representation.  ...  The proposed word representation shows a Spearman correlation score of 0.886 and provided the best results when compared to the current state-of-the-art methods, and exceed the human performance of 0.78  ...  The semantic and knowledge-graph embedding are combined to create one-word embedding, representing both a semantic and general word representation.  ... 
arXiv:1901.07176v1 fatcat:l2vcvimvu5ctrasqtxncfd3nre

FoodKG: A Tool to Enrich Knowledge Graphs Using Machine Learning Techniques

Mohamed Gharibi, Arun Zachariah, Praveen Rao
2020 Frontiers in Big Data  
FoodKG employs an existing graph embedding technique trained on a controlled vocabulary called AGROVOC, which is published by the Food and Agriculture Organization of the United Nations.  ...  The resulting model obtained after training on AGROVOC was evaluated against the state-of-the-art word embedding and knowledge graph embedding models that were trained on the same dataset.  ...  This work was supported in part by the National Science Foundation under grant No. 1747751. Part of this work was done when the second and third authors were at the University of Missouri-Kansas City.  ... 
doi:10.3389/fdata.2020.00012 pmid:33693387 pmcid:PMC7931944 fatcat:3vaonvpuabhinhgdn5ehmvvt5u

A Survey of Knowledge Enhanced Pre-trained Models [article]

Jian Yang, Gang Xiao, Yulong Shen, Wei Jiang, Xinyu Hu, Ying Zhang, Jinghui Peng
2021 arXiv   pre-print
Pre-trained models with knowledge injection, which we call knowledge enhanced pre-trained models (KEPTMs), possess deep understanding and logical reasoning and introduce interpretability to some extent  ...  We first introduce the progress of pre-trained models and knowledge representation learning. Then we systematically categorize existing KEPTMs from three different perspectives.  ...  completion.  ... 
arXiv:2110.00269v1 fatcat:6y4gi4bmb5fnrogi7nx44jdxie

Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition

Tianshui Chen, Liang Lin, Riquan Chen, Yang Wu, Xiaonan Luo
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
Compared with existing methods of fine-grained image classification, our KERL framework has several appealing properties: i) The embedded high-level knowledge enhances the feature representation, thus  ...  Specifically, we organize the rich visual concepts in the form of knowledge graph and employ a Gated Graph Neural Network to propagate node message through the graph for generating the knowledge representation  ...  To validate the benefit of our knowledge embedding method, we further conduct an experiment that incorporates knowledge by simply concatenating the image and graph feature vectors, followed by a fully-connected  ... 
doi:10.24963/ijcai.2018/87 dblp:conf/ijcai/ChenLCWL18 fatcat:iek67dt6mzftld5cll247t6mfa

Towards Holistic Concept Representations: Embedding Relational Knowledge, Visual Attributes, and Distributional Word Semantics [chapter]

Steffen Thoma, Achim Rettinger, Fabian Both
2017 Lecture Notes in Computer Science  
Knowledge Graphs (KGs) effectively capture explicit relational knowledge about individual entities.  ...  We investigate the potential of complementing the relational knowledge captured in KG embeddings with knowledge from text documents and images by learning a shared latent representation that integrates  ...  from the knowledge graph.  ... 
doi:10.1007/978-3-319-68288-4_41 fatcat:sq3qn2qusfej5dzxgwvklpw2ku

Link-Intensive Alignment for Incomplete Knowledge Graphs [article]

Vinh Van Tong, Thanh Trung Huynh, Thanh Tam Nguyen, Hongzhi Yin, Quoc Viet Hung Nguyen, Quyet Thang Huynh
2021 arXiv   pre-print
graph completion techniques.  ...  We also demonstrate that the knowledge exchanging between the KGs helps reveal the unseen facts from knowledge graphs (a.k.a. knowledge completion), with the result being 3.5\% higher than the SOTA knowledge  ...  Zhou, “Context-enhanced entity and representation learning,” in IEEE 35th International Conference on Data relation embedding for knowledge graph completion (student abstract),” Engineering  ... 
arXiv:2112.09266v1 fatcat:5xb2b23w3bed7km4opfm6usgzi
« Previous Showing results 1 — 15 out of 59,767 results