Mikyoung Lee

OntoFrame s3: semantic web-based academic research information portal service empowered by STAR-WIN

We have developed a prototype of a practical knowledge-driven semantic portal, OntoFrame S3, which provides various reasoning-based analysis services on academic research information. To realize this semantic portal, we developed and applied several Semantic Web and linguistic technologies. Through this demonstration, we will show how Semantic Web technologies can be utilized for information connection and fusion in the academic research information service sector and empowered by linguistic knowledge.

Decision-Making Support Service Based on Technology Opportunity Discovery Model

This paper discribes InSciTe Advanced, a decision-making support service, based on TOD(technology opportunity discovery) model. TOD model is a logical model for discovery of emerging technologies and prediction of phase and speed on a technology life cycle. InSciTe Advanced is based on semantic technologies such as ontology, semantic repository and inference as well as text mining. It aims to provide multi-facet services on emerging technologies, their elements and alternations in all domain. InSciTe Advanced has major services such as trends and predictions, technology levels, relationship paths, roadmaps and competitiors and collaborators.

Design of TOD Model for Information Analysis and Future Prediction

Analyzing mass information and supporting insight based on analysis results are very important work but it needs much effort and time. Information analysis and future prediction about science and IT filed data are also very critical tasks for researchers, government officers, businessman, etc. Therefore, in this paper, we propose technology opportunity discovery (TOD) model based on feature selection and decision making for effective, systematic, and objective information analysis and future forecasting of science and IT field.

OntoPipeliner: An ontology-based automatic semantic service pipeline generator

The Web is a distributed environment rich with Web services going through continual metamorphosis; thus, sustaining semantic stability of service composition has become a major challenge. Automatic service composition - enabled both by the use of ontologies that describe service domains and by user-specified constraints bound to the ontologies - provides us candidate service pipelines at composition design time. The ontology-based languages for semantically describing web services, such as OWL-S, have been widely used. Though rich and comprehensive in their expressiveness, the use of these languages still leaves much of composition process manual. In this work, we present an ontology-based semantic web service composition system called OntoPipeliner. It employs a novel way of utilizing characteristics of Web services that reflect the classes and properties of domain ontologies and provides the ontology-guided constraints for automatic composition of services, in order to guide the user toward the best pipeline that meets the user requirements.

Prescriptive Analytics System for Scholar Research Performance Enhancement

We introduce a prescriptive analytics system, InSciTe Advisory, to provide researchers with advice for their future research direction and strategy. It consists of two main parts: descriptive analytics and prescriptive analytics. Descriptive analytics provides results from research activity history as well as the research power index for the designated researcher. Prescriptive analytics suggests a group of role model researchers to the researcher, as well as methods to adopt their best practices. The prescription for the researcher is provided according to 5W1H questions and their corresponding answers. All of the analytical results and their explanations related to the given researcher are automatically generated and saved to a report. This researcher-centric prescriptive analytics framework is expected to be a useful tool to understand the designated researcher from the perspective of prescriptive and descriptive analytics. We evaluated user satisfaction results for InSciTe Advisory and Elsvier Scival by five test users. The result of the evaluation demonstrated that user satisfaction of InSciTe Advisory is 126.5% higher than Scival.

Technology-based decision-making support system

This paper describes a decision-making support system focused on technologies, R&D agents, and R&D results. To deal with heterogeneous literatures and metadata, we introduce text mining and Semantic Web-based service platforms. InSciTe, a decision-making support system developed by us, provides a through process including analysis as well as ETL, verifies search and analysis results, connects its information with Semantic Web open sources in the level of RDF, and generates automatic summary reports. This system is significant in the sense that it has been implemented about a year earlier than similar projects such as CUBIST and FUSE.

Using semantic web technologies for technology intelligence services

The change in technological environment presents threats as well as opportunities to companies in the related fields. Existing Technology Intelligence procedures require complicated techniques and high-skilled labor results. Large expert-interviews and manual work is also needed, so single small companies can not undertake this alone. To spread and activate Technology Intelligence in research and industrial fields, we propose shallow, but automated, Technology Intelligence services based on Semantic Web technologies, which can reduce the amount of labor required from experts. We explain our Semantic Web technologies, such as ontology modeling, semantic repository, inference and verification and how they make our Technology Intelligence services possible.

Auto-complete for Improving Reliability on Semantic Web Service Framework

This paper presents two methods for enhancing auto-complete which providing search keywords that the user wants. The first is to display only search keywords that can guarantee a successful search result in real time regardless of documents insertion, deletion, and update. The second is to display search keywords with their entity types such as person, institution, and topic. To accomplish them, we introduce an auto-complete table that stores the entities extracted and indexed from input documents and their document frequency (DF). An auto-complete manager checks whether each entity in the table can guarantee a successful search result or not by considering its DF, and provides proper entities with their types to the user. To verify the effect of the auto-complete, we are designing a comparative experiment. OntoFrame 2007 without the functions will be compared with OntoFrame 2008 with the functions for discovering the effect of our auto-complete on the reliability of Semantic Web services.

Study on the Visualization Elements of Web Information Services: Focused on Researcher Network and Graphic Chart

To provide information efficiently to users with web service, we study information visualization techniques, especially focused on researcher network and graphic chart. In the viewpoint of data set and level of functionality, we analyze the following four academic information services, Authoratory, Research gate, Biomedexperts, and Academic. research. We analyze researcher network and graphic chart of each service and then propose evaluation criteria of visualization elements. We aim to contribute to practical development of technologies for information visualization that can enhance the usefulness of web information services.

Study on Providing Multi-faceted Information on Technology Intelligence Service.

In this study, we propose an information visualization method that provides multi-faceted information about technologies, research agents and research results on technology intelligence service supporting researchers to facilitate strategic planning and decisions. We consider selecting a suitable type among existing visualization types to increase the efficiency of information delivery and then customizing it for multi-faceted information. In this manner, we select an appropriate visualization type and then add meaningful information by using additional features such as color and size of node. As further work, we would like to develop an automatic explanation module for all visualizations in the technology intelligence service.