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Accurate Text-Enhanced Knowledge Graph Representation Learning

Bo An, Bo Chen, Xianpei Han, Le Sun
2018 Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)  
To appropriately handle the semantic variety of entities/relations in distinct triples, we propose an accurate text-enhanced knowledge graph representation learning method, which can represent a relation  ...  And a mutual attention mechanism between relation mention and entity description is proposed to learn more accurate textual representations for further improving knowledge graph representation.  ...  Accurate Text-enhanced Knowledge Graph Representation This section presents our accurate text-enhanced knowledge graph representation learning framework.  ... 
doi:10.18653/v1/n18-1068 dblp:conf/naacl/AnCHS18 fatcat:i6iwxf67snhxloyc4v26wlupyq

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  
This paper proposes a novel text-enhanced knowledge graph representation model, which can utilize textual information to enhance the knowledge representations.  ...  Especially, a mutual attention mechanism between KG and text is proposed to learn more accurate textual representations for further improving knowledge graph representation, within a unified parameter  ...  CONCLUSION In this paper, we propose an accurate text-enhanced knowledge graph representation framework, which can utilize accurate textual information enhance the knowledge representations of a triple  ... 
doi:10.1109/access.2020.2981212 fatcat:6uhn2qkedjcqrln3hd6xdy2zbq

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

Yashen Wang, Huanhuan Zhang, Haiyong Xie
2019 Asian Conference on Machine Learning  
Based on this collaborative attention mechanism, a text-enhanced knowledge graph (KG) representation model is proposed, which could utilize textual information to enhance the knowledge representations  ...  ) and textual relation representation learning procedure (i.e., text representation).  ...  We argue that, this phenomenon is rooted in the methodology that, the proposed text-enhanced knowledge graph representation model with collaborative attention could utilize accurate textual information  ... 
dblp:conf/acml/WangZ019 fatcat:3fh6abkdbza33gkzchmz6ceb7u

Towards information-rich, logical text generation with knowledge-enhanced neural models [article]

Hao Wang, Bin Guo, Wei Wu, Zhiwen Yu
2020 arXiv   pre-print
In order to solve this problem, many researchers begin to consider combining external knowledge in text generation systems, namely knowledge-enhanced text generation.  ...  The challenges of knowledge enhanced text generation including how to select the appropriate knowledge from large-scale knowledge bases, how to read and understand extracted knowledge, and how to integrate  ...  Graph attention-based knowledge representation To obtain more accurate vector representations, the graph attention algorithm [Zhou et al., 2018] is proposed, which uses relation information to aggregate  ... 
arXiv:2003.00814v1 fatcat:5fllyakwqzf4vnmar3a6zjoewe

Joint Representations of Knowledge Graphs and Textual Information via Reference Sentences

Zizheng JI, Zhengchao LEI, Tingting SHEN, Jing ZHANG
2020 IEICE transactions on information and systems  
The proposed framework consists of knowledge graph representation learning module, textual relation representation learning module, and textual entity representation learning module.  ...  learning, knowledge graph, reference sentence, attention mechanism Recent years have witnessed the great advance of representation learning (RL) models for knowledge graph, which represents entities and  ...  The three modules mentioned above enhance the ability to represent the potential interaction between the knowledge graph representation learning process and the textual information representation learning  ... 
doi:10.1587/transinf.2019edp7229 fatcat:wyzem5xnazg53a6x2mzjcpvx6a

Knowledge Graph Semantic Enhancement of Input Data for Improving AI

Shreyansh Bhatt, Amit Sheth, Valerie Shalin, Jinjin Zhao, Amit Sheth
2020 IEEE Internet Computing  
The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph.  ...  Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph.  ...  Figure 1 : 1 Iterative optimization for knowledge enhanced machine learning. Training data is linked/augmented with the KG.  ... 
doi:10.1109/mic.2020.2979620 fatcat:q4xrsmddnfbvzjhev4xqknfo64

Retrieval of Scientific and Technological Resources for Experts and Scholars [article]

Suyu Ouyang and Yingxia Shao and Ang Li
2022 arXiv   pre-print
This paper sorts out the related research work in this field from four aspects: text relation extraction, text knowledge representation learning, text vector retrieval and visualization system.  ...  due to information asymmetry and other reasons, the scientific and technological resources of experts and scholars cannot be connected with the society in a timely manner, and social needs cannot be accurately  ...  Text knowledge representation learning In recent years, large-scale knowledge graphs such as WordNet and Freebase have provided an effective foundation for important areas of artificial intelligence such  ... 
arXiv:2204.06142v1 fatcat:qspkjuqrhbc6he3pdoyrheukua

Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge

Wenfeng Hou, Qing Liu, Longbing Cao
2020 Applied Sciences  
One important solution is to enrich short text representation by involving cognitive aspects of text, including semantic concept, knowledge, and category.  ...  In this paper, we propose a named Entity-based Concept Knowledge-Aware (ECKA) representation model which incorporates semantic information into short text representation.  ...  In addition, knowledge graph is another effective way to enhance the text semantic representation.  ... 
doi:10.3390/app10144893 fatcat:uotbuk4tajcghmmwrlnkbkrfze

Graphs and Commonsense Knowledge improve the Dialogue Reasoning Ability

Minglei Gao, Sai Zhang, Xiaowang Zhang, Zhiyong Feng, Wenhuan Lu
2021 International Semantic Web Conference  
In this paper, we propose a new approach based on commonsense knowledge combined with graph features.  ...  Experiments show good performance through the effective combination of commonsense knowledge and graph structure.  ...  The success of the retrieval can make the dialogue proceed more accurately and smoothly and can better enhance the user experience.  ... 
dblp:conf/semweb/GaoZZFL21 fatcat:wv2omfbwyrawbey7nfgn2tsfoa

Deep Learning Enhanced with Graph Knowledge for Sentiment Analysis

Fernando Lovera, Yudith Cardinale, Davide Buscaldi, Thierry Charnois, Masun Nabhan Homsi
2021 Extended Semantic Web Conference  
Knowledge Graphs give a way to extract structured knowledge from images and texts, in order to facilitate their semantic analysis.  ...  In this work, we propose a new hybrid approach for Sentiment Analysis based on Knowledge Graphs and Deep Learning techniques, to identify the sentiment polarity (positive or negative) in short documents  ...  to the representation of text as graphs.  ... 
dblp:conf/esws/LoveraCBCH21 fatcat:qp4bcvm5ibdynn67k65543jdii

ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration [article]

Yuhao Cui, Zhou Yu, Chunqi Wang, Zhongzhou Zhao, Ji Zhang, Meng Wang, Jun Yu
2021 arXiv   pre-print
Vision-and-language pretraining (VLP) aims to learn generic multimodal representations from massive image-text pairs.  ...  To this end, we introduce a new VLP method called ROSITA, which integrates the cross- and intra-modal knowledge in a unified scene graph to enhance the semantic alignments.  ...  Figure 2 : 2 The flowchart of knowledge extraction given an image-text pair. It consists of two main stages, namely the unified scene graph construction and knowledge representation.  ... 
arXiv:2108.07073v1 fatcat:2ergckq7e5dxlkm53hp5fsvyj4

C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System [article]

Yuanhang Zhou, Kun Zhou, Wayne Xin Zhao, Cheng Wang, Peng Jiang, He Hu
2022 arXiv   pre-print
For developing effective CRSs, a major technical issue is how to accurately infer user preference from very limited conversation context.  ...  To effectively leverage multi-type external data, we propose a novel coarse-to-fine contrastive learning framework to improve data semantic fusion for CRS.  ...  Finally, we produce the review-based user representation 𝒆 𝑅 . After the above encoding, we can obtain the corresponding representations for conversation history, knowledge graph and review text.  ... 
arXiv:2201.02732v2 fatcat:egdaemxp6jcxxeeaejy7vodife

ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph [article]

Fei Yu, Jiji Tang, Weichong Yin, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang
2021 arXiv   pre-print
We propose a knowledge-enhanced approach, ERNIE-ViL, which incorporates structured knowledge obtained from scene graphs to learn joint representations of vision-language.  ...  Thus, ERNIE-ViL can learn the joint representations characterizing the alignments of the detailed semantics across vision and language.  ...  Thus integrating the knowledge of scene graphs can benefit learning more fine-grained joint representations for the vision-language.  ... 
arXiv:2006.16934v3 fatcat:q7iucmyxfrf4bkiusdujbym6ye

A Survey of Knowledge-Enhanced Text Generation [article]

Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, Meng Jiang
2022 arXiv   pre-print
This research direction is known as knowledge-enhanced text generation.  ...  In this survey, we present a comprehensive review of the research on knowledge enhanced text generation over the past five years.  ...  Graph neural networks (GNNs) [134] and graph-to-sequence (Graph2Seq) [6] potentiate to bridge up the gap between graph representation learning and text generation.  ... 
arXiv:2010.04389v3 fatcat:vzdtlz4j65g2va7gwkbmzyxkhq

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

Majid Ramezani, Mohammad-Reza Feizi-Derakhshi, Mohammad-Ali Balafar, Tongguang Ni
2022 Computational Intelligence and Neuroscience  
Afterwards, the knowledge graph, which is now a knowledgeable alternative for the input text, was embedded to yield an embedding matrix.  ...  This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits.  ...  best of our knowledge) calls into question the application of knowledge representation and thereby knowledge graph in text-based APP.  ... 
doi:10.1155/2022/3732351 pmid:35769270 pmcid:PMC9236841 fatcat:5sjjjq2yynalnmsjhoc3ieflh4
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