This is the website of Teeradaj RACHARAK. I am a Thai Assistant Professor in the field of Artificial Intelligence at Japan Advanced Institute of Science and Technology (JAIST). Academically, I did my 1st Ph.D. in Description Logic under the supervision of Professor Satoshi Tojo and my 2nd Ph.D. in Computational Argumentation under the supervision of Assistant Professor Nguyen Duy Hung; and my master's in Logic Programming under the supervision of Professor Phan Minh Dung. Apart from the studies in Computational Logic, I am an open-minded software engineer and am interested in many things related to Artificial Intelligence and software development methodologies. My research interest is centered on formal development toward Human Intelligence i.e. how a machine can 'learn' and 'reason' like a human? In particular, I have been studying to address the following research areas:
- Machine Learning,
- Computational Logic,
- Neural-Symbolic Learning and Reasoning, and
- Their Applications to Natural Language Understanding.
- 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 formal knowledge representation languages. Many 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.
- Ontology Learning
- Ontology learning is an emerging area aiming at nothing less than the automatic generation of ontologies. Approaches to ontology learning can be roughly classified into: ontology learning from text, linked data mining, concept learning in description logic and OWL, and crowdsourcing. These approaches are used to support the construction of ontologies and populating them with their instantiations. See http://jens-lehmann.org/files/2014/pol_introduction.pdf for a summary of this area.
Japan Advanced Institute of Science and Technology
School of Information Science (Building 1, 7F)
Nomi city, Ishikawa Prefecture, Japan
Phone: (+81) 0761-51-1222