RACHARAK, Teeradaj Assistant Professor
School of Information Science, Intelligent Robotics Area, School of Information Science
Bachelor of Engineering (Software and Knowledge Engineering) from Kasetsart University (2010), Master of Engineering (Computer Science) from Asian Institute of Technology (2012), Ph.D (Information Science) from Japan Advanced Institute of Science and Technology (2018), Ph.D (Engineering and Technology) from Thammasat University (2019)
◆Professional Experience
: Lecturer at Thai Programmer Association, Thailand (2018)
: Researcher at Institute for Information Technology Innovation, Faculty of Engineering, Kasetsart University, Thailand (2010), Software Development Engineer at Octosoft Co. Ltd., Thailand (2012 - 2014)
Intelligent informatics
◆Research Keywords
Explainable Artificial Intelligence, Description Logic, Argumentation, Machine Learning
◆Research Interests
Argumentation is a form of non-monotonic reasoning which could be viewed as a dispute resolution of participants' arguments subject to certain propositions. A theoretical study of argumentation is about mechanisms for constructing arguments and the attack relation between them; argumentation semantics for deciding which arguments should be accepted or rejected; or proof procedures for computing the acceptability of arguments w.r.t. different semantics.
Argument Mining
Argument mining (a.k.a. argumentation mining) is an emerging area in computational linguistics. It involves automatic identification of argumentative structures in free text, such as the conclusions, premises, and inference schemes of arguments as well as their interrelations and counter-considerations. It requires interdisciplinary approaches, namely natural language processing techniques, knowledge of discourse in application domains, and argumentation theory.
Description Logic
Description logic is a family of knowledge representation languages. Most of description logic dialects are more expressive than propositional logic but less expressive than first-order logic. They are often used to describe and reason about the relevant concepts of an application domain (i.e. terminological knowledge) and are of importance in providing a logical formalism for ontologies and the Semantic Web.


◆Published Papers
Progressive Training in Recurrent Neural Networks for Chord Progression Modeling
Trung-Kien Vu, Teeradaj Racharak, Satoshi Tojo, Nguyen Ha Thanh, Nguyen Le Minh
Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART), Valletta, Malta, -, 2020
On Construction and Evaluation of Analogical Arguments for Persuasive Reasoning
Teeradaj Racharak, Satoshi Tojo, Nguyen Duy Hung, Prachya Boonkwan
Applied Artificial Intelligence, 33, 13, 1107-1132, 2019
Concept Similarity under the Agent’s Preferences for the Description Logic ALEH
Teeradaj Racharak, Watanee Jearanaiwongkul, Chutiporn Anutariya
Proceedings of the 9th Joint International Semantic Technology Conference (JIST), Hangzhou, China, -, 2019
Abstract Argumentation for Summarizing Product Reviews: A Case Study in Shopee Thailand
Teeradaj Racharak
Proceedings of the 11th IEEE International Conference on Knowledge and Systems Engineering (KSE), -, 2019
Inherited Properties of FL0 concept similarity measure under preference profile
Teeradaj Racharak, Satoshi Tojo
Agents and Artificial Intelligence, Lecture Notes in Artificial Intelligence, Springer International Publishing, -, 2018

■Contributions to  Society

◆Social Contribution
・ Editor Assistant, Journal of Intelligent Informatics and Smart Technology 2019
・ Organizing Committee, The 10th Joint International Conference on Knowledge Graph (JIST-KG) 2020 - 2020
・ Session Program Committee, Knowledge and Systems Engineering (Special Session: Text Mining from Social Media for Intelligent Systems) 2020 - 2020

■Academic  Awards

・ Dimensionality Reduction using an Autoencoder in Python authorized by Coursera Project Network and offered through Coursera , 2020
・ Tensorflow in Practice by deeplearning.ai on Coursera , 2019
・ Deep Learning by deeplearning.ai on Coursera , 2019