Do-Heon Jeong

Technology trends analysis and forecasting application based on decision tree and statistical feature analysis

Analyzing mass information and supporting foresight are very important task but they are extremely time-consuming work. In addition, information analysis and forecasting about the science and technology are also very critical tasks for researchers, government officers, businessman, etc. Some related studies recently have been executed and semi-automatic tools have been developed actively. Many researchers, annalists, and businessmen also generally use those tools for strategic decision making. However, existing projects and tools are based on subjective opinions from several experts and most of tools simply explain current situations, not forecasting near future trends. Therefore, in this paper, we propose a technology trends analysis and forecasting model based on quantitative analysis and several text mining technologies for effective, systematic, and objective information analysis and forecasting technology trends. Additionally, we execute a comparative evaluation between the suggested model and Gartners forecasting model for validating the suggested model because the Gartners model is widely and generally used for information analysis and forecasting.

Time gap analysis by the topic model-based temporal technique

This study proposes a temporal analysis method to utilize heterogeneous resources such as papers, patents, and web news articles in an integrated manner. We analyzed the time gap phenomena between three resources and two academic areas by conducting text mining-based content analysis. To this end, a topic modeling technique, Latent Dirichlet Allocation (LDA) was used to estimate the optimal time gaps among three resources (papers, patents, and web news articles) in two research domains. The contributions of this study are summarized as follows: firstly, we propose a new temporal analysis method to understand the content characteristics and trends of heterogeneous multiple resources in an integrated manner. We applied it to measure the exact time intervals between academic areas by understanding the time gap phenomena. The results of temporal analysis showed that the resources of the medical field had more up-to-date property than those of the computer field, and thus prompter disclosure to the public. Secondly, we adopted a power-law exponent measurement and content analysis to evaluate the proposed method. With the proposed method, we demonstrate how to analyze heterogeneous resources more precisely and comprehensively.

Prescriptive Analytics System for Improving Research Power

We introduce a prescriptive analytics system, InSciTe advisory, to provide researchers with advice of their future research direction and strategy. The system analyzes several thousands of heterogeneous types of data sources such as papers, patents, reports, Web news, Web magazines, and collective intelligence data. It consists of two main parts of descriptive analytics and prescriptive analytics. Once given a researcher, the descriptive analytics part provides results from activity history and research power w.r.t the designated researcher. Then, prescriptive analytics part suggests a group of role model researchers to the researcher, as well as how to be like the role model researchers. The prescription for the researcher is provided according to 5W1H questions and their corresponding answers. All of the analytical results and their explanations about the given researcher are automatically generated and saved to a report. This researcher-centric prescriptive analytics has not been introduced before and it is useful tool to understand the designated researcher in the perspective of prescriptive as well as descriptive analytics.

User-centered innovative technology analysis and prediction application in mobile environment

Business intelligence is a critical in defining the strategy and roadmap of organizations. However, business intelligence covers too much wide coverage to consider all of fields such as data analytics, text mining, predictive analytics, and so on. Among these fields, the most important is information analysis and prediction. Therefore, we suggest a business intelligence application based on the adaptive recognition of user intention and usage patterns in the mobile environment. This application is named InSciTe Adaptive and is based on text mining and semantic web technologies. It supports technology-focused analysis and predictions, such as technology trends analysis, element technology analysis, and convergence technology discovery, as well as adaptive recognition of the user’s intention by using semi-automatic user modeling processes. Through adaptive user modeling, this application can provide a more dynamic service flow and more up-to-date analysis results based on the user’s intention, compared to existing applications, which provide static analysis results and service flow.

Research Advising System Based on Prescriptive Analytics

As the amount of data increases enormously, business analytics such as descriptive, predictive, and prescriptive analytics is one of the most important topics for better decision making especially for CTO or CIO in corporate. Prescriptive analytics shows fundamental difference with descriptive analytics and predictive analytics in that it requires high-value alternative actions or decisions to achieve a given goal. However, only a few studies have been introduced since it is a emerging technology. Thus, this study aims to trigger research on this technical area by implementing a prescriptive analytics system and by verifying it in the point of usability and usefulness. The system, InSciTe Advisory, is focused on improving research performance and is based on 5W1H questions to build actionable strategies to achieve a given goal. The comparison evaluation of the system with Elsevier SciVal showed a rate of 118.8% in usefulness and reliability.

A term normalization method for efficient knowledge acquisition through text processing

The importance of research on knowledge management is growing due to recent issues on Big Data. One of the most fundamental steps in knowledge management is the extraction of terminologies. Terms are often expressed in various forms and the variations often play a negative role, becoming an obstacle which causes knowledge systems to extract unnecessary ones. To solve the problem, we propose a method of term normalization which finds a normalized form (original and standard form defined in dictionaries) of variant terms. The method employs two characteristics of terms: appearance similarity measuring how similar terms are, context similarity measuring how many clue words they share. Through experiment, we show its positive influence of both similarities in term normalization.

Generating Knowledge Map for Acronym-Expansion Recognition

In this paper, we present a method for instance mapping and URI resolving to merge two heterogeneous resources and construct a new semantic network in a viewpoint of Acronym-Expansion. Acronym-Expansion information extracted from two unstructured large dataset can be remapped by using linkage information between instances and measuring string similarity. All the semantic resources and systems are utilized for constructing infrastructures of KISTI’s TOD, a new information analytics project based on semantic technologies.

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.

Application for Temporal Analysis of Scientific Technology Information

In recent, business intelligence becomes one of important issues due to various analyses on technology trends. Especially, understanding the relations and influences between technologies is core property for the high-performed analysis. To do this, a few works have utilized ontologies constructed automatically but still have many errors and it causes difficulty while interpreting technology trends. Therefore this paper introduces an application which visualizes relationships and influences between technologies according to time series. Our application provides clues for intuitive observations of relationship change between technologies.

Domain Terminology Collection for Semantic Interpretation of Sensor Network Data

Many studies have investigated the management of data delivered over sensor networks and attempted to standardize their relations. Sensor data come from numerous tangible and intangible sources, and existing work has focused on the integration and management of the sensor data itself. The data should be interpreted according to the sensor environment and related objects, even though the data type, and even the value, is exactly the same. This means that the sensor data should have semantic connections with all objects, and so a knowledge base that covers all domains should be constructed. In this paper, we suggest a method of domain terminology collection based on Wikipedia category information in order to prepare seed data for such knowledge bases. However, Wikipedia has two weaknesses, namely, loops and unreasonable generalizations in the category structure. To overcome these weaknesses, we utilize a horizontal bootstrapping method for category searches and domain-term collection. Both the category-article and article-link relations defined in Wikipedia are employed as terminology indicators, and we use a new measure to calculate the similarity between categories. By evaluating various aspects of the proposed approach, we show that it outperforms the baseline method, having wider coverage and higher precision. The collected domain terminologies can assist the construction of domain knowledge bases for the semantic interpretation of sensor data.