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Studies are actively ongoing for better understanding and strengthening the capabilities of researchers. To do so requires an accurate diagnosis and analysis of such researchers. Therefore, data of each researcher must be collected and be identified in a big-data environment. Consequently, researcher-name identification has emerged as an important issue. This paper proposes a framework for collecting, refining, identifying, and publicly offering researcher data. For identifying authors' name, the proposed framework extracts timeline based patterns that make help to identify the same name authors with their representative attributes such as emails and affiliations. The results of the proposed framework based on timeline patterns, show a 69.5 % average author-identification rate given a group of otherwise unidentified authors.

With the recent tremendous advances in Internet of Things (IoT), analyzing technology trends and providing high quality services also play an important role in analyzing big data. Patent data has been on the rise, and the importance of patents also has been increasing to demand a company's competitiveness and to claim technology rights since patents contain important feature technologies that can be used to analyzing new technology trends and to find competitor companies or technologies. Especially IoT techniques are evolving rapidly and a number of new technologies have emerged. Hence a variety of analysis techniques on inventions patented by specific companies are required. In this paper, a trend analysis method is proposed to extract patterns from patent dataset using ETI relations. The experiment results based on the real patent dataset of IoT domain invented a specific organization show that the characteristics of the organization that have the expansion relations is 98.6 % and the transition relations is 1.4 % among ETI relations. Furthermore, we verify proposed dictionaries are useful for enhancing the accuracy to extract ETI relations from a patent network automatically.

Owing to the technological advancements in Semantic Web and sensor networks, a large amount of data has been produced in association with the open data policy. However, data stream management systems that process stream data have focused on the processing of a large amount of data with little priority on data identification, integration, and external linkage. Furthermore, entity resolution is focused mainly on static database-based technologies. In this study, a real-time stream data processing architecture that can perform the integration and entity resolution of streaming-type heterogeneous input data and interlink with external data is designed. To achieve this goal, a light adapter to integrate heterogeneous data into standard scheme and blocking technique to reduce comparison candidates are applied. The implemented data adapters shows 4 times higher throughput than open source data parsers and the entity resolution results with streaming data shows similar performance with the static data sets. The proposed streaming data entity resolution architecture is expected to form the basis of data integration research that can integrate various information sources of data efficiently, enrich internal data.

The explosive growth of data and the rapidly changing technical social evolution new analysis paradigm for predicting and reacting the future the past and present ig data. Prescriptive analysis has a fundamental difference because can support specific behaviors and results according to user's goals with defin researchers establish judgments and activities achiev the goals. However research methods not widely implemented and even the terminology, Prescriptive analysis, is still unfamiliar. This paper thus propose an infrastructure in the prescriptive analysis field with key considerations for enhancing capability of researchers through a case study based on InSciTe Advisory developed with scientific big data. InSciTe Advisory system s developed in 2013, and offers a prescriptive analytics report which contains various As-Is analysis results and To-Be analysis results 5W1H methodology. InSciTe Advisory therefore shows possibility strategy aims to reach a target role model group. Through the availability and reliability of the measurement model the evaluation results obtained relative advantage of 118.8% compared to Elsevier SciVal.

A number of web applications provide completely automated machine translation services, allowing users to easily translate information of interest. However, these services still generate inaccurate results when translating technical terminologies. Therefore, we propose a new method that collects reliable pairs of English-Korean technical terms and translates the given English terminology to Korean. To collect the pairs, we utilize textual big data, such as Korean academic papers, and develop a new statistical model to determine appropriate characteristics. Our method is evaluated in terms of the reliability of English-Korean pairs and the precision of translation. We thus confirm that our method can produce highly reliable data and can positively influence the translation quality of technical terminologies. Copyright © 2014 John Wiley & Sons, Ltd.

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.

In this study, we suggest the mobile business intelligence service based on adaptive recognition of user intention and usage patterns. This service is named as InSciTe adaptive and based on text mining and semantic web technologies. This service supports not only technology-focusing analysis and prediction but also adaptive recognition about user’s intention by semi-automatic user modeling process. By the adaptive user modeling, this service can provide more suitable service flow and more proper analysis results based on user’s intention.

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.

User intention modeling is a key component for providing appropriate services within ubiquitous and pervasive computing environments. Intention modeling should be concentrated on inferring user activities based on the objects a user approaches or touches. In order to support this kind of modeling, we propose the creation of object-activity pairs based on relatedness in a general domain. In this paper, we show our method for achieving this and evaluate its effectiveness.

In this study we evaluate usefulness of InSciTe Advanced, technology opportunity service offering trends information related technologies by analyzing automatically large amount of data. Before developing the service, InSciTe Advanced, we design 5 main services - Trends and Predictions, Technology Levels, Relationship Paths, Roadmaps, Competitors and Collaborators - through the interviews with expert in planning R&D strategy. And we evaluate usefulness of each service in InSciTe Advanced to examine how the information analyzed by InSciTe Advanced are useful to user from two points of view, Satisfaction of Information and Completeness of Information. Users set a high value on 5 main services in all tis aspects, but give relative low grades to completeness of Information. We will learn lessons from this study, increase kinds and amount of data, and strengthen the process about accuracy and reliability of analyzed information from the service.

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 Gartner's forecasting model for validating the suggested model because the Gartner's model is widely and generally used for information analysis and forecasting.

Semantic web is utilized to construct advanced information service by using semantic relationships between entities. Text mining can be applied to generate semantic relationships from unstructured data resources. In this study, ontology schema guideline, ontology instance generation, disambiguation of same name by text mining and advanced historical people finding service by reasoning have been proposed. Various relationships between historical event, organization, people, which are created by domain experts, are linked to literatures of National Institute of Korean History (NIKH). It improves the effectiveness of user access and proposes advanced people finding service based on relationships. In order to distinguish between people with the same name, we compares the structure and edge, nodes of personal social network. To provide additional i nformation, external resources including thesaurus and web are linked to all of internal related resources as well.

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.

Queries entered in a search box are the results of users' activities to actively seek information. Therefore, search logs are important data which represent users' information needs. The purpose of this study is to examine if there is a relationship between the results of queries automatically classified and the categories of documents accessed. Search sessions were identified in 2009 NDSL(National Discovery for Science Leaders) log dataset of KISTI (Korea Institute of Science and Technology Information). Queries and items used were extracted by session. The queries were processed using an automatic classifier. The identified queries were then compared with the subject categories of items used. As a result, it was found that the average similarity was 58.8% for the automatic classification of the top 100 queries. Interestingly, this result is a numerical value lower than 76.8%, the result of search evaluated by experts. The reason for this difference explains that the terms used as queries are newly emerging as those of concern in other fields of research.

The trends of technical terms express the changes of particular subjects in a specific research field over time. However, the amount of academic literature and patent data is too large to be analyzed by human resources. In this paper, we propose a method that can detect and analyze the trends of terms by modeling the life cycle of the terms. The proposed method is composed of the following steps. First, the technical terms are extracted from academic literature data, and the TDVs(Term Dominance Values) of terms are computed on a periodic basis. Based on the TDVs, the life cycles of terms are modeled, and technical terms with similar temporal patterns of the life cycles are classified into the same trends class. The experiments shown in this paper is performed by exploiting the NDSL academic literature data maintained by KISTI.

The proposed method in this study is the Maximal Concept-Strength Recognition Method(MCR). In case that we don't know which database is the most suitable for automatic-classification when new database is imported, MCR met hod can support to select the most similar database among many databases in the legacy sy stem. For experiments, we constructed four heterogeneous scholarly databases and measu red the best performance with MCR method. In result, we retrieved the exact database exp ected and the precision value of MCR based automatic-classification was close to the best performance.키워드: 자동분류, 자동범주화, 최대 개념강도 인지기법, 자동 데이터베이스 선택, 텍스트마이닝automatic classification, automatic categorization, maximal con cept-strength recognition, automatic database selection, text mining *** 관련특허: 최대 개념강도 인지기법을 이용한 최적의 데이터베이스 선택장치 및 그 방법 (10-2009-0118944).한국과학기술정보연구원 정보유통본부 정보기술연구실 선임연구원(heon@kisti.re.kr) ￭ 논문접수일자:2010년 8월 15일 ￭ 최초심사일자:2010년 8월 21일 ￭ 게재확정일자:2010년 8월 27일 ￭ 정보관리학회지, 27(3): 265-281, 2010. [DOI:10.3743/KOSIM.2010.27.3.265]

In this study, we link informal Web resources to KISTI NDSL`s collections using automatic semantic indexing and tagging to examine the possibility of the service which recommends related documents using the similarity between KISTI`s formal information resources and informal web resources. We collect and index Web resources and make automatic semantic linking through STEAK with KISTI`s collections for NDSL retrieval. The macro precision which shows retrieval precision per a subject category is 62.6% and the micro precision which shows retrieval precision per a query is 66.9%. The experts` evaluation score is 76.7. This study shows the possibility of semantic linking NDSL retrieval results with Web information resources and expanding information services` coverage to informal information resources.

An acronym is an abbreviation of several words in such a way that the abbreviation itself forms a pronounceable word. Acronyms occur frequently throughout various documents, especially those of a technical nature, for example, research papers and patents. While these acronyms can enhance document readability, in a variety of fields, they have a negative effect on business intelligence. To resolve this problem, we propose a method of acronym-expansion disambiguation to collect high-quality enterprise information. In experimental evaluations, we demonstrate its efficiency through the use of objective comparisons.

This paper proposes an extended association rule mining algorithm to improve the accuracy of the Onto-Apriori algorithm which is one of the association rule mining algorithm. The Onto-Apriori algorithm considered the usage of ontology in the association rule mining algorithms. However it does not reflect relationships between items which might have similarity between them. Therefore generated rules from the algorithm are not sufficient to find association between items. To solve the problem, we propose an algorithm, XOnto-Apriori, that can generates association rules and can suggest more valuable candidate item sets applying ontology reasoning function. Experimental results show our proposed approach. The XOnto-Apriori algorithm shows more effective results.

A terminology system capable of providing interpretations and classification information on a multilingual science and technology(S&T) terminology is essential to establish an integrated search environment for multilingual S&T information systems. This paper aims to build a base system to manage an integrated information system for multilingual S&T terminology search. It introduces a method to build a search system for S&T terminologies internally linked through the multilingual semantic network and a search technique on the multiple linked nodes. In order to provide a foundation for further analysis researches, it also attempts to suggest a basic approach to interpret terminology clusters generated with those two search methods.

Continual attempts to accumulate and apply information eventually gave birth to the concept of the "Semantic Web". Thus, the "Semantic Web" can be defined as a product of mankind`s desire to standardize information. At the same time, the term provides "a method that standardizes mankind`s concept of linguistical expression", and can be noted as an effort to combine such methods into a standard web environment that may materialize to form a catalogue. This study introduced RDF schema, ontology languages for the semantic web, and ontology-based systems. The purpose of the study was to construct a system based on the semantic web environment`s ontology by utilizing the ontology schema derived from the facettype Art and Architecture Thesaurus(AAT). The aforementioned ontology schema is based on the Web Ontology Language(OWL), which is being widely considered the standard ontology language for the W3C-centered semantic web environment.

The purpose of the study was to construct a system based on the semantic web environment`s ontology by utilizing the ontology schema derived from the facet-type Art and Architecture Thesaurus(AAT). The aforementioned ontology schema is based on the Web Ontology Language(OWL), which is being widely considered the standard ontology language for the W3C-centered semantic web environment. Also, the concepts were limited to terms within AAT`S Furniture Facet, and the system was tested using the Chair concept, which is a lower-level facet that has a diverse conceptual relationship and broad vocabulary base. The ontology system is capable of searching for concepts, while controlling the search results by always providing a `Preferred term` for synonymous terms. In addition, the system provides the user with first, a relationship between the terms centered around the inquiry, and second, related terms along with their classification properties. Also, the system is presented as and application example of the ontology system that constructs a information system that intakes an Instance value and reproduces it into a RDF file. During this process, utilization of multiple ontologies was introduced, and the stored Instance value`s meta-data elements were used.

Analysis, prediction, and recommendation of information about experts are very important tasks for future research planning and strategy establishment. However, it takes much time and efforts even for precise analysis of experts because we need to analyze huge and diverse heterogeneous information. There are several application and tools for supporting analysis about researchers, but they provides fragmentary analysis result based on simple evaluation criteria. Therefore, in this paper, we suggest new researcher evaluation model based on diverse performance evaluation features, named RSW model. By using RSW model, we can analyze and compare researchers in various perspectives. In addition, we can ranked researchers and recommend outstanding collaborator in a specified research field.

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.

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.

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.