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Accurate Text-Enhanced Knowledge Graph Representation Learning
2018
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
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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
2020
IEEE Access
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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
2019
Asian Conference on Machine Learning
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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]
2020
arXiv
pre-print
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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
2020
IEICE transactions on information and systems
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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
2020
IEEE Internet Computing
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2020 pre-print arXiv:2005.04726v1 fatcat:526vm2b53bff3ejlnrkdai7jpa
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]
2022
arXiv
pre-print
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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
2020
Applied Sciences
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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
2021
International Semantic Web Conference
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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
2021
Extended Semantic Web Conference
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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]
2021
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2022
arXiv
pre-print
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2021 pre-print arXiv:2010.04389v2 fatcat:h2kprdm4knbglljkqarxetwknyOther Versions
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
2022
Computational Intelligence and Neuroscience
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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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