DUSKG: A fine-grained knowledge graph for effective personalized service recommendation

2019 ◽  
Vol 100 ◽  
pp. 600-617 ◽  
Author(s):  
Haifang Wang ◽  
Zhongjie Wang ◽  
Sihang Hu ◽  
Xiaofei Xu ◽  
Shiping Chen ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
Service Recommendation ◽  
Personalized Service ◽  
Fine Grained

scholarly journals Graph-based service recommendation in Social Internet of Things

2021 ◽  
Vol 17 (4) ◽  
pp. 155014772110090
Author(s):  
Yuanyi Chen ◽  
Yanyun Tao ◽  
Zengwei Zheng ◽  
Dan Chen
Keyword(s):  
Internet Of Things ◽  
Social Relationships ◽  
Latent Variable ◽  
Smart Cities ◽  
Knowledge Graph ◽  
Service Recommendation ◽  
Variable Model ◽  
Completion Problem ◽  
Social Internet Of Things ◽  
Object Usage

While it is well understood that the emerging Social Internet of Things offers the capability of effectively integrating and managing massive heterogeneous IoT objects, it also presents new challenges for suggesting useful objects with certain service for users due to complex relationships in Social Internet of Things, such as user’s object usage pattern and various social relationships among Social Internet of Things objects. In this study, we focus on the problem of service recommendation in Social Internet of Things, which is very important for many applications such as urban computing, smart cities, and health care. We propose a graph-based service recommendation framework by jointly considering social relationships of heterogeneous objects in Social Internet of Things and user’s preferences. More exactly, we learn user’s preference from his or her object usage events with a latent variable model. Then, we model users, objects, and their relationships with a knowledge graph and regard Social Internet of Things service recommendation as a knowledge graph completion problem, where the “like” property that connects users to services needs to be predicted. To demonstrate the utility of the proposed model, we have built a Social Internet of Things testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.

Semi-Supervised Aspect-Based Sentiment Analysis for Case-Related Microblog Reviews Using Case Knowledge Graph Embedding

2021 ◽  
pp. 2050012
Author(s):  
Peilian Zhao ◽  
Cunli Mao ◽  
Zhengtao Yu
Keyword(s):  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Opinion Mining ◽  
Data Augmentation ◽  
Training Data ◽  
Knowledge Graph ◽  
Fine Grained ◽  
Learning Framework ◽  
Proposed Model ◽  
Real World Applications

Aspect-Based Sentiment Analysis (ABSA), a fine-grained task of opinion mining, which aims to extract sentiment of specific target from text, is an important task in many real-world applications, especially in the legal field. Therefore, in this paper, we study the problem of limitation of labeled training data required and ignorance of in-domain knowledge representation for End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA) in legal field. We proposed a new method under deep learning framework, named Semi-ETEKGs, which applied E2E framework using knowledge graph (KG) embedding in legal field after data augmentation (DA). Specifically, we pre-trained the BERT embedding and in-domain KG embedding for unlabeled data and labeled data with case elements after DA, and then we put two embeddings into the E2E framework to classify the polarity of target-entity. Finally, we built a case-related dataset based on a popular benchmark for ABSA to prove the efficiency of Semi-ETEKGs, and experiments on case-related dataset from microblog comments show that our proposed model outperforms the other compared methods significantly.

Fine-Grained Evaluation of Rule- and Embedding-Based Systems for Knowledge Graph Completion

2018 ◽  
pp. 3-20 ◽  
Author(s):  
Christian Meilicke ◽  
Manuel Fink ◽  
Yanjie Wang ◽  
Daniel Ruffinelli ◽  
Rainer Gemulla ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
Fine Grained

Trust-Based Personalized Service Recommendation: A Network Perspective

2014 ◽  
Vol 29 (1) ◽  
pp. 69-80 ◽  
Author(s):  
Shui-Guang Deng ◽  
Long-Tao Huang ◽  
Jian Wu ◽  
Zhao-Hui Wu
Keyword(s):  
Service Recommendation ◽  
Personalized Service

scholarly journals Aspect-level sentiment analysis merged with knowledge graph and graph convolutional neural network

2021 ◽  
Vol 2083 (4) ◽  
pp. 042044
Author(s):  
Zuhua Dai ◽  
Yuanyuan Liu ◽  
Shilong Di ◽  
Qi Fan
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Structural Information ◽  
Knowledge Graph ◽  
Convolutional Network ◽  
Text Data ◽  
Short Text ◽  
Fine Grained ◽  
Syntactic Information ◽  
Text Information

Abstract Aspect level sentiment analysis belongs to fine-grained sentiment analysis, w hich has caused extensive research in academic circles in recent years. For this task, th e recurrent neural network (RNN) model is usually used for feature extraction, but the model cannot effectively obtain the structural information of the text. Recent studies h ave begun to use the graph convolutional network (GCN) to model the syntactic depen dency tree of the text to solve this problem. For short text data, the text information is not enough to accurately determine the emotional polarity of the aspect words, and the knowledge graph is not effectively used as external knowledge that can enrich the sem antic information. In order to solve the above problems, this paper proposes a graph co nvolutional neural network (GCN) model that can process syntactic information, know ledge graphs and text semantic information. The model works on the “syntax-knowled ge” graph to extract syntactic information and common sense information at the same t ime. Compared with the latest model, the model in this paper can effectively improve t he accuracy of aspect-level sentiment classification on two datasets.

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