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KNOWLEDGE GRAPH
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NATURAL LANGUAGE PROCESSING
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About |
This special issue focuses on emerging techniques and trendy applications of AI for CKRR in fields such as natural language processing, computer vision, bioinformatics, and more. Mostly, we expect to receive papers on deep representation learning techniques for CKRR. Old-school, purely symbolic approaches to CKRR will be desk-rejected. However, we do welcome hybrid (symbolic and subsymbolic) approaches to CKRR. |
Call for Papers |
Topics of InterestThis special issue focuses on emerging techniques and trendy applications of AI for CKRR in fields such as natural language processing, computer vision, bioinformatics, and more. Works on the creation, use, and evaluation of CKRR resources, e.g., knowledge graphs, ontologies, are also welcome. These could be in English as well as in other languages. Mostly, we expect to receive works on textual CKRR, but papers on multimodal CKRR will also be considered. The topics of this special issue include but are not limited to: - Deep representation learning for CKRR - Hybrid (symbolic and subsymbolic) CKRR - Creation, use, and evaluation of CKRR resources - Multilingual CKRR - Multimodal CKRR - Time-evolving CKRR - Large-scale CKRR - Domain-specific CKRR - SenticNet 6 and other commonsense knowledge bases for sentiment analysis - Sentic LSTM and other commonsense-based deep nets for sentiment analysis - CKRR for recommendation systems - CKRR for question answering and dialogue systems - CKRR for neural machine translation - CKRR for optical character recognition - CKRR for automatic speech recognition - CKRR for digital health, e.g., healthcare and medical diagnosis - CKRR for explainable artificial intelligence |
Credits and Sources |
[1] CKRR 2021 : Commonsense Knowledge Representation and Reasoning |