scholarly journals Can Urban Environmental Problems Be Accurately Identified? A Complaint Text Mining Method

2021 ◽  
Vol 11 (9) ◽  
pp. 4087
Author(s):  
Yaran Jiao ◽  
Chunming Li ◽  
Yinglun Lin
Keyword(s):  
Text Mining ◽  
Semantic Network ◽  
Semantic Networks ◽  
Environmental Problems ◽  
Pollution Sources ◽  
Unstructured Data ◽  
Guangzhou City ◽  
Individual Health ◽  
Sensory Features ◽  
Urban Environmental Problems

With the popularization of social networks, the abundance of unstructured data regarding environmental complaints is rapidly increasing. This study established a text mining framework for Chinese civil environmental complaints and analyzed the characteristics of environmental complaints, including keywords, sentiment, and semantic networks, with two–year environmental complaints records in Guangzhou city, China. The results show that the keywords of environmental complaints can be effectively extracted, providing an accurate entry point for solving environmental problems; light pollution complaints are the most negative, and electromagnetic radiation complaints have the most fluctuating emotions, which may be due to the diversity of citizens’ perceptions of pollution; the nodes of the semantic network reveal that citizens pay the most attention to pollution sources but the least attention to stakeholders; the edges of the semantic network shows that pollution sources and pollution receptors show the most concerning relationship, and the pollution receptors’ relationships with pollution behaviors, sensory features, stakeholders, and individual health are also highlighted by citizens. Thus, environmental pollution management should not only strengthen the control of pollution sources but also pay attention to these characteristics. This study provides an efficient technical method for unstructured data analysis, which may be helpful for precise and smart environmental management.

Implicit Knowledge Discovery in Design Semantic Network by Applying Pythagorean Means on Shortest Path Searching

2017 ◽  
Author(s):  
Feng Shi ◽  
Liuqing Chen ◽  
Ji Han ◽  
Peter Childs
Keyword(s):  
Text Mining ◽  
Knowledge Discovery ◽  
Language Processing ◽  
Shortest Path ◽  
Large Scale ◽  
Semantic Network ◽  
Semantic Networks ◽  
Implicit Knowledge ◽  
Implicit Associations ◽  
Correlation Degree

With the advent of the big-data era, massive textual information stored in electronic and digital documents have become valuable resources for knowledge discovery in the fields of design and engineering. Ontology technologies and semantic networks have been widely applied with text mining techniques including Natural Language Processing (NLP) to extract structured knowledge associations from the large-scale unstructured textual data. However, most existing works mainly focus on how to construct the semantic networks by developing various text mining methods such as statistical approaches and semantic approaches, while few studies are found to focus on how to subsequently analyze and fully utilize the already well-established semantic networks. In this paper, a specific network analysis method is proposed to discover the implicit knowledge associations from the existing semantic network for improving knowledge discovery and design innovation. Pythagorean means are applied with Dijkstra’s shortest path algorithm to discover the implicit knowledge associations either around a single knowledge concept or between two concepts. Six criteria are established to evaluate and rank the correlation degree of the implicit associations. Two engineering case studies were conducted to illustrate the proposed knowledge discovery process, and the results showed the effectiveness of the retrieved implicit knowledge associations on helping providing relevant knowledge from various aspects, and provoking creative ideas for engineering innovation.

scholarly journals Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

2021 ◽  
Vol 11 (14) ◽  
pp. 6368
Author(s):  
Fátima A. Saiz ◽  
Garazi Alfaro ◽  
Iñigo Barandiaran ◽  
Manuel Graña
Keyword(s):  
Ad Hoc ◽  
Data Augmentation ◽  
Semantic Network ◽  
Semantic Networks ◽  
Stereo Image ◽  
Generative Adversarial Networks ◽  
Specific Class ◽  
Adversarial Networks ◽  
Augmentation Techniques ◽  
Image Acquisition System

This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional data augmentation techniques. We resort to Generative Adversarial Networks (GANs) that have shown the capability to generate highly convincing samples of a specific class as a result of a game between a discriminator and a generator module. Here, we apply the GANs to generate samples of images of metallic manufactured components with specific defects, in order to improve training of Semantic Networks (specifically DeepLabV3+ and Pyramid Attention Network (PAN) networks) carrying out the defect detection and segmentation. Our process carries out the generation of defect images using the StyleGAN2 with the DiffAugment method, followed by a conventional data augmentation over the entire enriched dataset, achieving a large balanced dataset that allows robust training of the Semantic Network. We demonstrate the approach on a private dataset generated for an industrial client, where images are captured by an ad-hoc photometric-stereo image acquisition system, and a public dataset, the Northeastern University surface defect database (NEU). The proposed approach achieves an improvement of 7% and 6% in an intersection over union (IoU) measure of detection performance on each dataset over the conventional data augmentation.

Semantic Network Language Generation based on a Semantic Networks Serialization Grammar

2010 ◽  
Vol 13 (3) ◽  
pp. 307-341 ◽  
Author(s):  
Yintang Dai ◽  
Shiyong Zhang ◽  
Jidong Chen ◽  
Tianyuan Chen ◽  
Wei Zhang
Keyword(s):  
Semantic Network ◽  
Semantic Networks ◽  
Language Generation ◽  
Network Language

A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Feng Shi ◽  
Liuqing Chen ◽  
Ji Han ◽  
Peter Childs
Keyword(s):  
Information Retrieval ◽  
Network Analysis ◽  
Text Mining ◽  
Engineering Design ◽  
Large Scale ◽  
Semantic Network ◽  
Document Retrieval ◽  
Design Information ◽  
Improve Design ◽  
Correlation Degree

With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. Ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the large-scale unstructured textual data environment. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a “WordNet” focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.

scholarly journals Structural differences in the semantic networks of younger and older adults

2018 ◽  
Author(s):  
Dirk U. Wulff ◽  
Thomas Hills ◽  
Rui Mata
Keyword(s):  
Older Adults ◽  
Cognitive Modeling ◽  
Semantic Network ◽  
Semantic Networks ◽  
Edge Weight ◽  
Younger Adults ◽  
Age Related ◽  
Weight Distributions ◽  
Path Lengths ◽  
First Time

Cognitive science invokes semantic networks to explain diverse phenomena from reasoning to memory retrieval and creativity. While diverse approaches are available, researchers commonly assume a single underlying semantic network that is shared across individuals. Yet, semantic networks are considered the product of experience implying that individuals who make different experiences should possess different semantic networks. By studying differences between younger and older adults, we demonstrate that this is the case. Using a network analytic approach and diverse empirical data, we present converging evidence of age-related differences in semantic networks of groups and, for the first time, individuals. Specifically, semantic networks of older adults exhibited larger degrees, less clustering, and longer path lengths. Furthermore, the edge weight distributions of older adults individual networks exhibited significantly more skew and higher entropy across node pairs and, except for unrelated node pairs, less inter-individual agreement, suggesting that older adults networks are generally more distinct than younger adults networks. Our results challenge the common conception of a single semantic network shared by individuals and highlight the importance of individual differences in cognitive modeling. They also present valuable benchmarks to discern between theories of age-related changes in cognitive performance.

The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems

2011 ◽  
Vol 31 (2) ◽  
pp. 273-288 ◽  
Author(s):  
Fei Chen ◽  
Hiroyuki Kusaka ◽  
Robert Bornstein ◽  
Jason Ching ◽  
C. S. B. Grimmond ◽  
...  
Keyword(s):  
System Development ◽  
Environmental Problems ◽  
Urban Modelling ◽  
Development Evaluation ◽  
Modelling System ◽  
Urban Environmental Problems
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