초록

빅 데이터 시대에 접어들면서 저장 기술과 처리 기술이 급속도로 발전함에 따라, 과거에는 간과되었던 롱테일(long tail) 데이터가 많은 기업과 연구자들에게 관심의 대상이 되고 있다. 본 연구는 롱테일 법칙의 영역에 존재하는 데이터의 활용률을 높이기 위해 텍스트 마이닝 기반의 기술 용어 네트워크 생성 및 통제 기법을 제안한다. 특히 텍스트 마이닝의 편집 거리(edit distance) 기법을 이용해 학문 분야에서 사용되는 기술 용어의 상호 네트워크를 자동으로 생성하는 효과적인 방안을 제시하였다. 데이터의 활용률 향상 실험을 위한 데이터 수집을 위해 LOD(linked open data) 환경을 이용하였으며, 이 과정에서 효과적으로 LOD 시스템의 데이터를 활용하는 기법과 용어의 패턴 처리 알고리즘을 제안하였다. 마지막으로, 생성된 기술 용어 네트워크의 성능 측정을 통해 제안한 기법이 롱테일 데이터의 활용률 제고에 효과적이었음을 확인하였다.

Abstract

As data management and processing techniques have been developed rapidly in the era of big data, nowadays a lot of business companies and researchers have been interested in long tail data which were ignored in the past. This study proposes methods for generating and controlling a network of technical terms based on text mining technique to enhance data utilization in the distribution of long tail theory. Especially, an edit distance technique of text mining has given us efficient methods to automatically create an interlinking network of technical terms in the scholarly field. We have also used linked open data system to gather experimental data to improve data utilization and proposed effective methods to use data of LOD systems and algorithm to recognize patterns of terms. Finally, the performance evaluation test of the network of technical terms has shown that the proposed methods were useful to enhance the rate of data utilization.

초록

Abstract

The goal of this paper is to explore the field of Computer and Information Science with the aid of text mining techniques by mining Computer and Information Science related conference data available in DBLP (Digital Bibliography & Library Project). Although studies based on bibliometric analysis are most prevalent in investigating dynamics of a research field, we attempt to understand dynamics of the field by utilizing Latent Dirichlet Allocation (LDA)-based multinomial topic modeling. For this study, we collect 236,170 documents from 353 conferences related to Computer and Information Science in DBLP. We aim to include conferences in the field of Computer and Information Science as broad as possible. We analyze topic modeling results along with datasets collected over the period of 2000 to 2011 including top authors per topic and top conferences per topic. We identify the following four different patterns in topic trends in the field of computer and information science during this period: growing (network related topics), shrinking (AI and data mining related topics), continuing (web, text mining information retrieval and database related topics), and fluctuating pattern (HCI, information system and multimedia system related topics).