Semantic Web Use Cases and Case Studies

Use Case: InSciTe Adaptive– User Centric Technology Intelligence Service for Supporting Decision-Making and Strategic Planning in R&D

 

Jinhyung Kim, Myunggwon Hwang, Mi-Kyung Lee, Do-Heon Jeong, Seungwoo Lee, Sa-Kwang Song, Hanmin Jung, and Won-Kyung Sung

Korea Institute of Science and Technology, Korea

Nov. 2012

kisti-logo

 

General Description

Introduction

The precise information analysis and new opportunity discovery are very important for future forecasting, future countermeasures decision, and future plan establishment. However, as the amount of information in science and IT field increases exponentially every year, data analysis about that information or extraction of new opportunity from documents, papers, patents, etc. becomes more difficult and complicate. Until now, there have been researches regarding information analysis of mass data and new opportunity discovery [1-5]. Traditional studies had focused on information analysis and conclusion deduction based on the scenario method or the Delphi method or AHP method. These methods are based on non-systematic process and dependent to subjective opinions of experts.

 

Korea Institute of Science and Technology Information have researched regarding information analysis about science and technology field, and technology opportunity discovery since 2010. Technology opportunity means a chance of technical innovation [10] and technology opportunity discovery indicates activities that search where such a chance exists. Recent global competition among technologies is summarized as a process of a discovery and preoccupancy of technology opportunities. Therefore, it can be said that core competitiveness of a country or a company on research and development lies in their ability to analyze and apply technology opportunities. To discover technology opportunity, InSciTe Adaptive adopts Semantic Web technologies as a framework for representing and managing semantic data and also employs text mining technologies as a tool for automated and intelligent acquisition of semantic data. It analyzes web resources as well as digital contents on science and technology including academic papers and industrial patents, detects technological issues and discovers emerging technologies. InSciTe Adaptive includes following information resources. We use 2 storages: (1) a relational database for raw data such as papers, patents, and web resources and (2) semantic storage for relational information among technologies, products, organizations, and nations as Table 1.

 

img1.png

User Adaptive Features

InSciTe Adaptive includes 2 user adaptive features: user modeling and user pattern recognition. The user modeling is the first part of InSciTe Adaptive service and consists of 4 levels questions. By analyzing answers of questions, the system can classifies user as one of groups and suggest suitable starting service. The user pattern recognition is applied to each service in InSciTe Adaptive. The system analyzes service usage pattern and suggests related technology which is useful to users or the next services.
The user modeling process consists of five steps: (a) Key category selection, (b) Constitution element selection, (c) Constitution function decision, (d) Service decision, (e) User group decision. In the 1st and 2nd steps, the system requests the user to manually select options for recognizing the user intention. For the precise recognition of the user purpose, the system supports the user optimized selection based on mapping information constitution elements in the 2nd step and constitution functions in the 3rd step.

 

img1.png

 

Figure 1. User Modeling Process

 

The strength of ontology is to provide related information of one concept. In addition to its basic roles, the InSciTe Adaptive uses the ontology to grasp user-interested technologies. User touches a few technologies, products, organizations, and nations in each service to know detail information and the system calculates interesting weight of their related technologies. Let us assume that user watched three technologies (T1, T2, and T3) and two organizations (O1 and O2) in order. Each one has related tech-nologies with relations such as (elementary), (develop) and so on. If a technology(s) appears r times or more in n recent contexts, the system considers it as user-interested technology(s).

 

 

Figure 2. User Adaptive Process

 

Technology Focusing Analytics

InSciTe Adaptive includes 3 technology focusing analysis services: Technology Trends, Core Elementary Technology, and Convergence Technology. Each service focuses on technology analysis and relationship among related technologies.

The technology trends service analyzes technology emerging status, development speed and forecasts when a technology can be reached into the maturity step. The technology trends service is based on 3 analysis models: technology life cycle discovery model, technology maturity forecast model, and emerging technology discover model. The TLCD model decides emerging phase of a specific technology and the TMF model forecast development speed of a specific technology. The ETD model decides whether a specific technology is emerging technology or not.
The core elementary technology service analyzes various elementary technologies of a specific technology. Elementary technologies are extracted by papers, patents, and web resources separately. Because companies usually are focusing on applying patents but universities are focusing on publishing papers, elementary technologies by papers and patents create totally different results. Each elementary technology has relative share ratio and this service evaluate emerging degree of each elementary technology.
The convergence technology service discovers candidate technology for convergence such as (Augmented Reality + Car = Smart Car). Convergence technologies are extracted based on sharing degree of element technologies. If the one technology has same element technologies with the other technology, both of them can be converged in the future. The convergence technology service is the typical forecasting service which analyzes current status of each technology and predict future blueprint. In addition, the service evaluates each candidate convergence technology based on convergence proportion between candidate technology and search keyword given by users.

 

<

 

Figure 3. Technology Focusing Analysis Services

 

Agent Focusing Analytics

InSciTe Adaptive includes 2 agent focusing analysis services: agents levels and agents partners. In this service, the agent means company, university, and institution. Each service focuses on agent analysis and relationship between agents and technologies.

The agent levels service provides relative rank of organizations and nations for given technology. The service has two dimensions analyzed by commercial and scholar aspects and the dimensions are calculated by the amount of patents and papers. In addition, the service evaluates agents based on overall ratio which summarizes both dimensions. Moreover, the service provides technology list of selected organization or nation. User can understand which agent approaches commercially or scholarly to a given technology.
The agent partners service provides collaborating or competing organizations. Organizations can have both relations because they develop many kinds of technologies and products. If there are two organizations and one produces (LCD) and (tablet pc) and the other produces (tablet pc) then this organization can have collaboration in terms of (LCD) for (tablet pc) but they can compete in terms of (tablet pc). This service analyzes collaborating and competing in aspects of technologies and products between organizations. The service users can know relations of organization and their technologies and products which shows the relations.

 

<

 

Figure 4. Agent Focusing Analysis Services

 

Key features of InSciTe Adaptive

l  Combining text mining and Semantic Web technologies

l  Generating a technical summary report automatically

l  User-centric and user guiding services

l  High-qualiity information analysis, prediction and recommendation

 

© Copyright 2011-2012, Korea Institute of Science and Technology Information (KISTI)