A relation-aware heterogeneous graph convolutional network for relationship prediction

Mo, Xian; Tang, Rui; Líu, Hao

doi:10.1016/j.ins.2022.12.059

Cited by 12 publications

(3 citation statements)

References 35 publications

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“…Their methods can be applied to other artificial intelligence-based tasks. Other effective opinion mining methods include end-to-end neural-based methods, such as those in [25][26][27][28], as well as graph-based methods, such as those in [29][30][31][32].…”

Section: Arabic Dataset and Opinion Mining Methodsmentioning

confidence: 99%

User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques

Al-Mutawa

Al-Aama

2023

Applied Sciences

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Opinion mining refers to the process that helps to identify and to classify users’ emotions and opinions from any source, such as an online review. Thus, opinion mining provides organizations with an insight into their reputation based on previous customers’ opinions regarding their services or products. Automating opinion mining in different languages is still an important topic of interest for scientists, including those using the Arabic language, especially since potential customers mostly do not rate their opinion explicitly. This study proposes an ensemble-based deep learning approach using fastText embeddings and the proposed Arabic emoji and emoticon opinion lexicon to predict user opinion. For testing purposes, the study uses the publicly available Arabic HARD dataset, which includes hotel reviews associated with ratings, starting from one to five. Then, by employing multiple Arabic resources, it experiments with different generated features from the HARD dataset by combining shallow learning with the proposed approach. To the best of our knowledge, this study is the first to create a lexicon that considers emojis and emoticons for its user opinion prediction. Therefore, it is mainly a helpful contribution to the literature related to opinion mining and emojis and emoticons lexicons. Compared to other studies found in the literature related to the five-star rating prediction using the HARD dataset, the accuracy of the prediction using the proposed approach reached an increase of 3.21% using the balanced HARD dataset and an increase of 2.17% using the unbalanced HARD dataset. The proposed work can support a new direction for automating the unrated Arabic opinions in social media, based on five rating levels, to provide potential stakeholders with a precise idea about a service or product quality, instead of spending much time reading other opinions to learn that information.

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Section: Arabic Dataset and Opinion Mining Methodsmentioning

confidence: 99%

User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques

Al-Mutawa

Al-Aama

2023

Applied Sciences

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“…This work uses the dataset from Aminer 1. As the release dataset version continues to update, it has become more popular and used for analyzing the information spread [33], studying the scientific influence [34][35][36], building recommendations in academic networks [37,38], researching citation and cooperation networks [39][40][41][42], and developing the prediction in academic networks [43,44]. This work adopts the 12th version of the dataset, which includes 4.9 million papers from 113,887 disciplines.…”

Section: Data Preparationmentioning

confidence: 99%

The profit and risk in the interdisciplinary behavior

Luo

Liang

et al. 2023

Front. Phys.

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Evaluating the influence of interdisciplinary research is important to the development of science. This work considers the large and small disciplines, calculates the interdisciplinary distance, and analyzes the influence of interdisciplinary behavior and interdisciplinary distance in the academic network. The results show that the risk of interdisciplinary behavior in the large discipline is more significant than the benefits. The peer in the small disciplines will tend to agree with the results of the small discipline across the large discipline. We further confirmed this conclusion by utilizing PSM-DID. The analysis between interdisciplinary distance and scientists’ influence shows that certain risks will accompany any distance between disciplines. However, there still exists a “Sweet Spot” which could bring significant rewards. Overall, this work provides a feasible approach to studying and understanding interdisciplinary behaviors in science.

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“…Zeng et al [24] proposed a heterogeneous graph convolution based on in-domain self-supervision for multimodal sentiment analysis; it makes full use of domain knowledge by constructing a heterogeneous graph and integrating text modality features. Mo et al [25] proposed a relation-aware heterogeneous graph convolutional network to learn different relationships of a specific node type. Fei et al [26] proposed to further enhance dual learning with structure matching that explicitly builds structural connections in between.…”

Section: Heterogeneous Graph Neural Networkmentioning

confidence: 99%

Integrating Relational Structure to Heterogeneous Graph for Chinese NL2SQL Parsers

Zhang

Huang

et al. 2023

Electronics

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The existing models for NL2SQL tasks are mainly oriented toward English text and cannot solve the problems of column name reuse in Chinese text data, description in natural language query, and inconsistent representation of data stored in the database. To address this problem, this paper proposes a Chinese cross-domain NL2SQL model based on a heterogeneous graph and relative position attention mechanism. This model introduces relational structure information defined by the expert to construct initial heterogeneous graphs for database schemas and natural language questions. The heterogeneous graph is pruned based on natural language questions, and the multi-head relative position attention mechanism is used to encode the database schema and natural language questions. The target SQL statement is generated using a tree-structured decoder with predefined SQL syntax. Experimental results on the CSpider dataset demonstrate that our model better aligns database schema with natural language questions and understands the semantic information in natural language queries, effectively improving the matching accuracy of Chinese multi-table SQL statement generation.

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A relation-aware heterogeneous graph convolutional network for relationship prediction

Cited by 12 publications

References 35 publications

User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques

User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques

The profit and risk in the interdisciplinary behavior

Integrating Relational Structure to Heterogeneous Graph for Chinese NL2SQL Parsers

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