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Future Internet
Special Issues
Knowledge Graph Mining and Its Applications
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Knowledge Graph Mining and Its Applications

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 14754

Special Issue Editor

Dr. Lei Li

E-Mail Website
Guest Editor
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
Interests: graph computing; social computing; data mining

Special Issue Information

Dear Colleagues,

A knowledge graph describes the entities and their relationships in the objective world with a structured form and expresses knowledge information in a form closer to the human cognitive world, which provides a good ability to organize, manage and reconcile the massive amount of knowledge information that exists. The knowledge graph, together with big data, has become one of the core driving forces to promote the development of Internet artificial intelligence.

This Special Issue covers all aspects of the knowledge graph, including algorithms, software, platforms, and applications for knowledge graph construction, maintenance and inference. This Special Issue draws researchers and application developers from a wide range of knowledge-graph-related areas such as knowledge engineering, big data analytics, statistics, machine learning, pattern recognition, data mining, knowledge visualization, high-performance computing, and the World Wide Web.

Dr. Lei Li
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • foundations of knowledge graph
  • machine learning
  • data mining
  • ontologies and reasoning
  • crowdsourcing
  • deep learning
  • edge computing
  • visualization
  • knowledge graph navigation
  • knowledge graph systems and platforms

Published Papers (6 papers)

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Research

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17 pages, 8706 KiB  
Article
Research on Urban Traffic Incident Detection Based on Vehicle Cameras
by Zhuofei Xia, Jiayuan Gong, Hailong Yu, Wenbo Ren and Jingnan Wang
Future Internet 2022, 14(8), 227; https://doi.org/10.3390/fi14080227 - 26 Jul 2022
Cited by 1 | Viewed by 1742
Abstract
Situational detection in the traffic system is of great significance to traffic management and even urban management. Traditional detection methods are generally based on roadside equipment monitoring roads, and it is difficult to support large-scale and fine-grained traffic incident detection. In this study, [...] Read more.
Situational detection in the traffic system is of great significance to traffic management and even urban management. Traditional detection methods are generally based on roadside equipment monitoring roads, and it is difficult to support large-scale and fine-grained traffic incident detection. In this study, we propose a detection method applied to the mobile edge, which detects traffic incidents based on the video captured by vehicle cameras, so as to overcome the limitations of roadside terminal perception. For swarm intelligence detection, we propose an improved YOLOv5s object detection network, adding an atrous pyramid pooling layer to the network and introducing a fusion attention mechanism to improve the model accuracy. Compared with the raw YOLOv5s, the mAP metrics of our improved model are increased by 3.3% to 84.2%, enabling it to detect vehicles, pedestrians, traffic accidents, and fire traffic incidents on the road with high precision in real time. This provides information for city managers to help them grasp the abnormal operation status of roads and cities in a timely and effective manner. Full article
(This article belongs to the Special Issue Knowledge Graph Mining and Its Applications)
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16 pages, 2701 KiB  
Article
Analysis and Visualization of New Energy Vehicle Battery Data
by Wenbo Ren, Xinran Bian, Jiayuan Gong, Anqing Chen, Ming Li, Zhuofei Xia and Jingnan Wang
Future Internet 2022, 14(8), 225; https://doi.org/10.3390/fi14080225 - 26 Jul 2022
Cited by 1 | Viewed by 1951
Abstract
In order to safely and efficiently use their power as well as to extend the life of Li-ion batteries, it is important to accurately analyze original battery data and quickly predict SOC. However, today, most of them are analyzed directly for SOC, and [...] Read more.
In order to safely and efficiently use their power as well as to extend the life of Li-ion batteries, it is important to accurately analyze original battery data and quickly predict SOC. However, today, most of them are analyzed directly for SOC, and the analysis of the original battery data and how to obtain the factors affecting SOC are still lacking. Based on this, this paper uses the visualization method to preprocess, clean, and parse collected original battery data (hexadecimal), followed by visualization and analysis of the parsed data, and finally the K-Nearest Neighbor (KNN) algorithm is used to predict the SOC. Through experiments, the method can completely analyze the hexadecimal battery data based on the GB/T32960 standard, including three different types of messages: vehicle login, real-time information reporting, and vehicle logout. At the same time, the visualization method is used to intuitively and concisely analyze the factors affecting SOC. Additionally, the KNN algorithm is utilized to identify the K value and P value using dynamic parameters, and the resulting mean square error (MSE) and test score are 0.625 and 0.998, respectively. Through the overall experimental process, this method can well analyze the battery data from the source, visually analyze various factors and predict SOC. Full article
(This article belongs to the Special Issue Knowledge Graph Mining and Its Applications)
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17 pages, 9315 KiB  
Article
Cross-Domain Transfer Learning Prediction of COVID-19 Popular Topics Based on Knowledge Graph
by Xiaolin Chen, Qixing Qu, Chengxi Wei and Shudong Chen
Future Internet 2022, 14(4), 103; https://doi.org/10.3390/fi14040103 - 24 Mar 2022
Viewed by 2530
Abstract
The significance of research on public opinion monitoring of social network emergencies is becoming increasingly important. As a platform for users to communicate and share information online, social networks are often the source of public opinion about emergencies. Considering the relevance and transmissibility [...] Read more.
The significance of research on public opinion monitoring of social network emergencies is becoming increasingly important. As a platform for users to communicate and share information online, social networks are often the source of public opinion about emergencies. Considering the relevance and transmissibility of the same event in different social networks, this paper takes the COVID-19 outbreak as the background and selects the platforms Weibo and TikTok as the research objects. In this paper, first, we use the transfer learning model to apply the knowledge obtained in the source domain of Weibo to the target domain of TikTok. From the perspective of text information, we propose an improved TC-LDA model to measure the similarity between the two domains, including temporal similarity and conceptual similarity, which effectively improves the learning effect of instance transfer and makes up for the problem of insufficient sample data in the target domain. Then, based on the results of transfer learning, we use the improved single-pass incremental clustering algorithm to discover and filter popular topics in streaming data of social networks. Finally, we build a topic knowledge graph using the Neo4j graph database and conduct experiments to predict the evolution of popular topics in new emergencies. Our research results can provide a reference for public opinion monitoring and early warning of emergencies in government departments. Full article
(This article belongs to the Special Issue Knowledge Graph Mining and Its Applications)
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19 pages, 776 KiB  
Article
Coarse-to-Fine Entity Alignment for Chinese Heterogeneous Encyclopedia Knowledge Base
by Meng Wu, Tingting Jiang, Chenyang Bu and Bin Zhu
Future Internet 2022, 14(2), 39; https://doi.org/10.3390/fi14020039 - 25 Jan 2022
Cited by 1 | Viewed by 2130
Abstract
Entity alignment (EA) aims to automatically determine whether an entity pair in different knowledge bases or knowledge graphs refer to the same entity in reality. Inspired by human cognitive mechanisms, we propose a coarse-to-fine entity alignment model (called CFEA) consisting of three stages: [...] Read more.
Entity alignment (EA) aims to automatically determine whether an entity pair in different knowledge bases or knowledge graphs refer to the same entity in reality. Inspired by human cognitive mechanisms, we propose a coarse-to-fine entity alignment model (called CFEA) consisting of three stages: coarse-grained, middle-grained, and fine-grained. In the coarse-grained stage, a pruning strategy based on the restriction of entity types is adopted to reduce the number of candidate matching entities. The goal of this stage is to filter out pairs of entities that are clearly not the same entity. In the middle-grained stage, we calculate the similarity of entity pairs through some key attribute values and matched attribute values, the goal of which is to identify the entity pairs that are obviously not the same entity or are obviously the same entity. After this step, the number of candidate entity pairs is further reduced. In the fine-grained stage, contextual information, such as abstract and description text, is considered, and topic modeling is carried out to achieve more accurate matching. The basic idea of this stage is to use more information to help judge entity pairs that are difficult to distinguish using basic information from the first two stages. The experimental results on real-world datasets verify the effectiveness of our model compared with baselines. Full article
(This article belongs to the Special Issue Knowledge Graph Mining and Its Applications)
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10 pages, 667 KiB  
Article
Global Contextual Dependency Network for Object Detection
by Junda Li, Chunxu Zhang and Bo Yang
Future Internet 2022, 14(1), 27; https://doi.org/10.3390/fi14010027 - 13 Jan 2022
Cited by 1 | Viewed by 2361
Abstract
Current two-stage object detectors extract the local visual features of Regions of Interest (RoIs) for object recognition and bounding-box regression. However, only using local visual features will lose global contextual dependencies, which are helpful to recognize objects with featureless appearances and restrain false [...] Read more.
Current two-stage object detectors extract the local visual features of Regions of Interest (RoIs) for object recognition and bounding-box regression. However, only using local visual features will lose global contextual dependencies, which are helpful to recognize objects with featureless appearances and restrain false detections. To tackle the problem, a simple framework, named Global Contextual Dependency Network (GCDN), is presented to enhance the classification ability of two-stage detectors. Our GCDN mainly consists of two components, Context Representation Module (CRM) and Context Dependency Module (CDM). Specifically, a CRM is proposed to construct multi-scale context representations. With CRM, contextual information can be fully explored at different scales. Moreover, the CDM is designed to capture global contextual dependencies. Our GCDN includes multiple CDMs. Each CDM utilizes local Region of Interest (RoI) features and single-scale context representation to generate single-scale contextual RoI features via the attention mechanism. Finally, the contextual RoI features generated by parallel CDMs independently are combined with the original RoI features to help classification. Experiments on MS-COCO 2017 benchmark dataset show that our approach brings continuous improvements for two-stage detectors. Full article
(This article belongs to the Special Issue Knowledge Graph Mining and Its Applications)
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Review

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15 pages, 1784 KiB  
Review
Mapping Art to a Knowledge Graph: Using Data for Exploring the Relations among Visual Objects in Renaissance Art
by Alexandros Kouretsis, Iraklis Varlamis, Laida Limniati, Minas Pergantis and Andreas Giannakoulopoulos
Future Internet 2022, 14(7), 206; https://doi.org/10.3390/fi14070206 - 03 Jul 2022
Cited by 2 | Viewed by 2364
Abstract
Graph-like structures, which are increasingly popular in data representation, stand out since they enable the integration of information from multiple sources. At the same time, clustering algorithms applied on graphs allow for group entities based on similar characteristics, and discover statistically important information. [...] Read more.
Graph-like structures, which are increasingly popular in data representation, stand out since they enable the integration of information from multiple sources. At the same time, clustering algorithms applied on graphs allow for group entities based on similar characteristics, and discover statistically important information. This paper aims to explore the associations between the visual objects of the Renaissance in the Europeana database, based on the results of topic modeling and analysis. For this purpose, we employ Europeana’s Search and Report API to investigate the relations between the visual objects from this era, spanning from the 14th to the 17th century, and to create clusters of similar art objects. This approach will lead in transforming a cultural heritage database with semantic technologies into a dynamic digital knowledge representation graph that will relate art objects and their attributes. Based on associations between metadata, we will conduct a statistic analysis utilizing the knowledge graph of Europeana and topic modeling analysis. Full article
(This article belongs to the Special Issue Knowledge Graph Mining and Its Applications)
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