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Research Areas

Research Areas
Main Convergence Research Topics
    Track Definition : Knowledge systems refer to all types of complex problem-solving systems built upon human knowledge and/or reasoning processes as their core components
    Related Fields : Diverse research areas such as information retrieval, artificial intelligence, knowledge engineering, cognitive engineering, human decision making, knowledge management, knowledge visualization, ontology, and semantic web
    Education Targets : This track aims at cultivating the ability to apply knowledge extraction and acquisition, knowledge modeling and representation, knowledge-based system design, cognitive engineering, collective intelligence, intelligent recommender, knowledge management to real world problems
    Studies in KSE : Our department has researched on multiple knowledge systems such as intelligent tutoring system, export control expert system, stock analytics system, self-growing exobrain system, and personalized search and recommendation system

    Track Definition : The methodical study of the generalizable and scalable extraction of knowledge from data 

    Related Fields : Knowledge discovery in social networks, cloud computing, information retrieval and web information access, databases and information integration, sensor data processing, etc.
    Education Targets : Based on the deep insight of data and application domains, to educate the entire cycle of data science, ranging from the design of meaningful services to the data collection, analysis, and visualization in support of these services
    Studies in KSE : Spatial big data analysis, social network big data analysis, multimedia content analysis using knowledge structure, expert finding through the analysis of Q&A and behavioral big data, vertical search engines for education materials, etc.

    Track Definition : To study the diverse environments that contain computer devices that have significant impact in the lives of individuals in the present-day society
    Related Fields : Interface & interaction design, user-centered design, user experience, user behavior modeling, requirement engineering, service science, human-centered computing, ubiquitous computing, etc.
    Education Targets : To educate individuals who have knowledge from both engineering and humanities in order to build systems that consider the human cognitive processes
    Studies in KSE : UX issues of smart devices, customer-centered service design, human-system interface design, UX design, and serious games, etc.
Introduction for Major Research Project
Knowledge Systems
Knowledge systems, also called knowledge-based systems, refer to computer applications designed to solve complex problems using human knowledge or reasoning. Knowledge systems were once studied as part of artificial intelligence, but the term is now broadly used in various fields to refer to those decision support systems that operate on the basis of explicit knowledge representation. In particular, knowledge systems play a crucial role in creating new values by utilizing the massive amount of data generated by the Internet. The importance of knowledge systems is expected only to continue growing. The objective of this track is to cover knowledge extraction, knowledge modeling, knowledge extraction, ontology, semantic web, recommender system, intelligent web service, knowledge management, and apply them to cutting-edge problems. Students in this track are offered a chance to participate in the on-going projects such expert systems, linked data, self-growing exobrain, personalized search and recommendation.
Experiential Knowledge Platform
Prof. Mun Yong Yi

Building a knowledge management platform

that digitalizes and manages experiental

knowledge of field experts, in order to

support decision making more effectively

- Proposing an inference algorithm

  that automatically creates decision tree

  based on the procedural knowledge

  acquired from field experts 

- Providing evidence for the inference

  by discovering relevant descriptive

  knowledge extracted from

  a large set of documents

Funded by the Ministry of Science, ICT and Future Planning and Korea Evaluation Institute of Industrial Technology from 2015 to 2018
Social Taste Recommendation Platform
Prof. Mun Yong Yi
Developing a core component of a personalized social taste recommendation platform out of the exploding amount of online/SNS contents
- Developing a knowledge structure representation of social contents
- Analyzing social taste to construct a user profile
- Developing a recommendation engine based on the social contents knowledge structure and user’s individual social taste
Funded by the Ministry of Science, ICT and Future Planning and Institute for Information & Telecommunication Technology Promotion from 2015 to 2016
Mobile Crowdsourcing based Smart Urban Surveillance Platform Technologies
Prof. Uichin Lee
Developing a civic engagement platform for mobile neighborhood watch in KAIST campus

- Designing various spatio-temporal coverage models for mobile neighborhood watch

- Developing automatic user scheduling techniques and incentive mechanisms

Funded by KAIST KUSTAR-KAIST Education Research Center (2015~2016)
Geospatial Big Data Management, Analysis and Service Platform Technology Development
Prof. Jae-Gil Lee
This research develops an interactive data analytics platform for real-time geospatial big data, which consists of spatial CEP & spatial OLAP

- Designing a spatial CEP for supporting both parallel processing and geospatial features

- Designing a spatial OLAP for supporting geospatial big data

Ministry of Land, Infrastructure and Transport/Korea Agency for Infrastructure Technology Advancement (2014~2019)

Collaborative Knowledge Synthesis Model for Automatic Hypothesis Generation
Seungwoo Choi & Prof. Aviv Segev
Generate hypothesis which can be considered to contain personal aspects in collaboration by using graph structure and search engines
Build a new automatic framework to integrate knowledge and generate hypothesis for group situation
Funded by National Research Foundation of Korea(2016~2020)
Data Science
Data science is defined as the study of the generalizable extraction of knowledge from data. In the 21st century of information and knowledge, the importance of data science is increasing rapidly to support effective decision making based on the data. Data science requires an integrated skill set of mathematics, machine learning, artificial intelligence, statistics, databases, and optimization; and our department is producing data scientists who are well-educated on these skills. The faculty members are conducting world-class research and development on the entire range of data science, including data collection, data analysis, and data visualization. The research topics include but are not limited to community detection from big data social network, analysis of big data trajectories, multimedia content retrieval based on knowledge structure, expert finding from Q&A data or human behavior data, social media platform supporting collaborative work.
Implementating smart data town platform
Prof. Mun Yong Yi
Solving serious social issues (i.e., transportation, safety, job creation) by collecting, storing, analyzing, and utilizing smart data

- Developing a matching table of compatible jobs, reflecting the trend of job recruiting & offering in Worknet.go.kr

- Developing and evaluating a job matching algorithm tailored for middle-aged class

Funded by the Ministry of Science, ICT and Future Planning and Institute for Information & Telecommunication Technology Promotion from 2015 to 2016
Smart Cloudlet Technology for Mobile Big Data Processing
Prof. Uichin Lee & Prof. Jae-Gil Lee
Developing a novel technology (called smart cloudlet) that supports multi-device cooperation for mobile big data processing
- Designing an app development framework supporting distributed data mining
- Developing cloudlet-user interaction methodologies
Funded by MSIP, Korea in the ICT R&D Program from 2013 to 2017
Study on Detection of Geo-Social Patterns from Big Data Social Networks
Prof. Jae-Gil Lee
This research proposes the algorithms of finding geo-social patterns to extract useful knowledge from geo-social networks on top of a memory-resident distributed platform
- Developing geo-social pattern mining algorithms
- Developing distributed and parallel algorithms on a memory-resident distributed platform in support of big data
Funded by Korea National Research from 2015 to 2018
Interruptibility Prediction Using Smartphone Context Data
Prof. Jae-Gil Lee
This research develops a model which predicts human interruptibility (availability) using various context information
- User classification by utilizing smartphone sensor data, user activity data, etc.
- Personalization by reflecting each user’s behavior

Funded by The Air Force Office of Scientific Research Asian Office of Aerospace Research and Development from 2015 to 2016

Future Nodes in a Knowledge Network
Sukhwan Jung & Tuan Manh Lai & Prof. Aviv Segev
- Proposing methods for knowledge prediction using network analytics
- Planning to label predicted knowledge with its impact on future domain knowledge
- Viewing the domain knowledge as a Knowledge network, with knowledge concepts as nodes and their relationships as links
- Introduces pEgonet, sub-networks within knowledge networks consisting of to-be-neighbors of new knowledge
Funded by the Ministry of Science, ICT and Future Planning and Korea Evaluation Institute of Industrial Technology from 2016 to 2020
Detection Model for Knowledge Processing in Structured Networks
Prof. Aviv Segev
- Analyze knowledge processing inside and outside our brain
- Redefine the concept of ‘Thinking’
- Use tree growth simulator for modeling structured networks
- Compare optimized simulated images to real world images to verify knowledge processing
Funded by National Research Foundation of Korea from(2016~2020)
Human-Computer Interaction
Human computer interaction (HCI) is one of the primary interdisciplinary research areas that is attracting the attention of the future 21st century leaders who are interested in interdisciplinary work. The HCI track aims to produce outstanding individuals who have knowledge from both engineering and humanities and thus provide curriculum that is composed of subjects that cover the basics of both areas. With the common goal of conducting interdisciplinary research, the research areas in HCI include understanding and analyzing the experience of smart device users, analyzing customer centered services, designing human-system interface and user experience. More broadly, HCI is being studied in diverse environments that contain computers and have general impact in the lives of individuals in the present-day society.
KAISTARs’ Research Village
Prof. Wan Chul Yoon & Prof. Uichin Lee
Facilitating in-campus research collaboration via a novel online service (called Koala)
Locating experts on the topics of interest
Providing up-to-date lab information with same format
Enabling topic-based online group discussion for research Q&A
This work is a joint research collaboration project In the Graduate School of Knowledge Service Engineering at KAIST
Developing Experiential Knowledge Interactions for Harnessing Convergent Expert Knowledge
Prof. Wan Chul Yoon
Developing an efficient clinical interaction system that supports medical users by minimizing cognitive load based on techniques from human-computer Interaction (HCI) and cognitive engineering
- Visualizing decision-making information that helps medical experts to interpret various knowledge types in a clinical system
- Designing a convergent knowledge management tool to acquire and manage field experts’ experience

Funded by the Ministry of Trade, Industry & Energy (MI, Korea) from 2015 to 2018

Aviation Human Factor Enhancement System
Prof. Wan Chul Yoon
The goal is to improve accident analysis and system safety for human error accident reduction in aviation industry
- Developing an aviation accident analysis system with cognitive engineering and establishing management requirement for system
- Improving crew resources to prevent human errors, and establishing effective crew training programs

Funded by Korea Agency for Infrastructure Technology Advancement from 2015 to 2016
Mitigating Technological Distraction with Context-Aware Technology
Prof. Uichin Lee
Developing intelligent services that help users to deal with technological distraction (e.g., smartphones, IoT) by leveraging context-aware technologies
- Providing group-based limiting assistance in group contexts with automatic co-location detection
- Designing tools for monitoring and managing multi-devices to deal with technological distractions in smart home environments
Funded by the Korea government (MSIP) from 2011 to 2016