Recent years have witnessed the release of many open-source and enterprise-driven knowledge graphs with a dramatic increase of applications of knowledge representation and reasoning in fields such as natural language processing, computer vision, and bioinformatics. With those large-scale knowledge graphs, recent research tends to incorporate human knowledge and imitate human’s ability of relational reasoning. Factual knowledge stored in knowledge bases or knowledge graphs can be utilized as a source for logical reasoning and, hence, be integrated to improve real-world applications.
Emerging embedding-based methods for knowledge graph representation have shown their
ability to capture relational facts and model different scenarios with heterogenous information. By combining symbolic reasoning methods or Bayesian models, deep representation learning techniques on knowledge graphs attempt to handle complex reasoning with relational path and symbolic logic and capture the uncertainty with probabilistic inference. Furthermore, efficient representation learning and reasoning can be one of the paths towards the emulation of high-level cognition and human-level intelligence. Knowledge graphs can also be seen as a means to tackle the problem of explainability in AI. These trends naturally facilitate relevant downstream applications which inject structural knowledge into wide-applied neural architectures such as attention-based transformers and graph neural networks.
This special issue focuses on emerging techniques and trendy applications of knowledge graph representation learning and reasoning in fields such as natural language processing, computer vision, bioinformatics, and more.
Topics of Interests
The topics of this special issues include but not limited to:
Representation learning on knowledge graphs Representation learning on text data Logical rule mining and symbolic reasoning Knowledge graph completion and link prediction Relation extraction Community embeddings Knowledge representation and reasoning over large-scale knowledge graphs Hybrid methods with symbolic and non-symbolic representation and reasoning Automatic knowledge graph construction Domain specific knowledge graphs, e.g., medical knowledge graphs Knowledge dynamics of temporal knowledge graphs Time-evolving knowledge representation learning Question answering and dialogue systems with knowledge graphs Knowledge-injected sentiment analysis Commonsense knowledge representation and reasoning Knowledge graphs for neural machine translation Knowledge-aware recommendation systems Knowledge graphs for digital health, e.g., mental healthcare and medical diagnosis Few-shot relational learning on knowledge graphs Federated learning with multi-source knowledge graphs in the decentralized setting Graph representation learning for structured data Explainable artificial intelligence with knowledge-aware models
Composition and Review Procedures
The Special Issue will consist of papers on novel methods and techniques that further develop and apply knowledge graph representation and reasoning for the development of intelligent
tools, techniques, and applications. Some papers may survey various aspects of the topic. The balance between these will be adjusted to maximize the issue’s impact. Paper submissions for the special issue should follow the submission format and guidelines for regular papers and submitted at https://ees.elsevier.com/neucom. All the papers will be peer-reviewed following NEUCOM reviewing procedures. Guest editors will make an initial assessment of the suitability and scope of all submissions. Papers will be evaluated based on their originality, presentation, relevance and contributions, as well as their suitability to the special issue. Papers that either lack originality, clarity in presentation or fall outside the scope of the special issue will not be sent for review. Authors should select “SI: KGRR” when they reach the “Article Type” step in the submission process. The submitted papers must propose original research that has not been published nor currently under review in other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended. Such contributions must have at least 50% difference from the research work they stem from.
Paper submission: 31 August 2020
Initial review feedback: 31 October 2020
Revision: 15 January 2021
Publication date: March 2021
Erik Cambria, Nanyang Technological University, Singapore
Shaoxiong Ji, Aalto University, Finland
Shirui Pan, Monash University, Australia
Philip S. Yu, University of Illinois at Chicago, USA