Collection

Special Issue on Knowledge-Graph-Enabled Methods and Applications for the Future Web

Submission status
Closed
Guest Editors

**n Wang, Tian** University, China, http://www.tjudb.cn/dbgroup/**n_Wang

Jeff Z. Pan, University of Edinburgh, UK, http://knowledge-representation.org/j.z.pan/

Qingpeng Zhang, City University of Hong Kong, Hong Kong, http://www.cityu.edu.hk/stfprofile/zhang.htm

Yuan-Fang Li, Monash University, Australia, https://users.monash.edu.au/~yli/

Knowledge Graphs (KGs) and related technologies have recently attracted significant attention from both academia and industry in scenarios that require sharing, managing, and exploiting knowledge from diverse sources of data on the Web. The growing publication and utilization of open and enterprise KGs, such as DBpedia, Freebase, Wikidata, YAGO, Google Knowledge Graph, Microsoft Satori, Facebook Entity Graph, Amazon Product Graph, and others, have prompted emerging research efforts on investigating how KGs can be leveraged to realize more knowledgeable techniques and applications for the next-generation Web. However, the existing approaches face a number of challenges on develo** KG-enabled technologies. Firstly, there is still a lack of widely recognized theories and practices for employing KGs in designing and implementing Web applications. Secondly, despite the recent progress of KG embedding techniques, KGs have not yet been well integrated with existing AI methods to achieve knowledgeable Web systems. Thirdly, a unified mechanism remains to be developed to guide users on how to effectively interact with KG-enabled applications on the Web.

Aims and Scope

This special issue aims to establish a forum for the dissemination of recent high-quality research advances from worldwide scholars to address the challenges in knowledge-graph-enabled methods and applications for the future Web. Existing studies on KGs mainly focus on either the traditional direction of knowledge engineering or the model aspect of knowledge representation learning. Currently, there are few research works that are devoted to how the stack of knowledge engineering approaches can be more effectively and efficiently integrated with machine learning approaches to overcome key issues including accuracy, efficiency, explainability, and knowledgeability. Thus, this special issue will encourage researchers to pay more attention to the significant gap and deliver practical contributions. Therefore, this special issue will focus on emerging methods and advance applications that are KG-enabled for the future Web.

Topics of Interest

Topics of interest of this special issue include but are not limited to:

--KG-enabled embedding and representation learning

--KG-enabled knowledge engineering

--KG-enabled neural network and deep learning

--KG-enabled multimodal methods and systems

--KG-enabled natural language processing

--KG-enabled question answering systems

--KG-enabled recommendation systems

--KG-enabled explainable AI

--KG-enabled data mining and analysis

--KG-enabled Web information systems

--KG-enabled Web applications

--KG-enabled Web 3.0

Submission Guidelines

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals. Springer offers authors, editors and reviewers of World Wide Web Journal a web-enabled online manuscript submission and review system. Our online system offers authors the ability to track the review process of their manuscript. Manuscripts should be submitted to: http://wwwj.edmgr.com. This online system offers easy and straightforward log-in and submission procedures, and supports a wide range of submission file formats.

Important Dates

Submission due: 1st October, 2022

First review notification: 1st December, 2022

Revision due: 20th February, 2023

Final Decision: 20th March, 2023

Author Resources

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals. All papers will be reviewed following standard reviewing procedures for the Journal. Papers must be prepared in accordance with the Journal guidelines: www.springer.com/11280 Springer provides a host of information about publishing in a Springer Journal on our Journal Author Resources page, including FAQs, Tutorials along with Help and Support.

Editors

  • **n Wang **n Wang , 

    **n Wang

    **n Wang, Tian** University, China, http://www.tjudb.cn/dbgroup/**n_Wang

  • Jeff Z. Pan Jeff Z. Pan , 

    Jeff Z. Pan

    Jeff Z. Pan, University of Edinburgh, UK. http://knowledge-representation.org/j.z.pan/

  • Qingpeng Zhang Qingpeng Zhang  &

    Qingpeng Zhang

    Qingpeng Zhang, City University of Hong Kong, Hong Kong, http://www.cityu.edu.hk/stfprofile/zhang.htm

  • Yuan-Fang Li Yuan-Fang Li

    Yuan-Fang Li

    Yuan-Fang Li, Monash University, Australia, https://users.monash.edu.au/~yli/

Articles (10 in this collection)