Journal of the Korean Society for information Management (정보관리학회지)

Korean Society for Information Management (한국정보관리학회)
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The Stream of Uncertainty in Scientific Knowledge using Topic Modeling

토픽 모델링 기반 과학적 지식의 불확실성의 흐름에 관한 연구

  • Received : 2019.02.18
  • Accepted : 2019.03.27
  • Published : 2019.03.30
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Abstract

The process of obtaining scientific knowledge is conducted through research. Researchers deal with the uncertainty of science and establish certainty of scientific knowledge. In other words, in order to obtain scientific knowledge, uncertainty is an essential step that must be performed. The existing studies were predominantly performed through a hedging study of linguistic approaches and constructed corpus with uncertainty word manually in computational linguistics. They have only been able to identify characteristics of uncertainty in a particular research field based on the simple frequency. Therefore, in this study, we examine pattern of scientific knowledge based on uncertainty word according to the passage of time in biomedical literature where biomedical claims in sentences play an important role. For this purpose, biomedical propositions are analyzed based on semantic predications provided by UMLS and DMR topic modeling which is useful method to identify patterns in disciplines is applied to understand the trend of entity based topic with uncertainty. As time goes by, the development of research has been confirmed that uncertainty in scientific knowledge is moving toward a decreasing pattern.

과학적 지식을 얻는 과정은 연구자의 연구를 통해 이루어진다. 연구자들은 과학의 불확실성을 다루고 과학적 지식의 확실성을 구축해나간다. 즉, 과학적 지식을 얻기 위해서 불확실성은 반드시 거쳐가야 하는 필수적인 단계로 인식되고 있다. 현존하는 불확실성의 특성을 파악하는 연구는 언어학적 접근의 hedging 연구를 통해 소개되었으며 컴퓨터 언어학에서 수작업 기반으로 불확실성 단어 코퍼스를 구축해왔다. 기존의 연구들은 불확실성 단어의 단순 출현 빈도를 기반으로 특정 학문 영역의 불확실성의 특성을 파악해오는데 그쳤다. 따라서 본 연구에서는 문장 내 생의학적 주장이 중요한 역할을 하는 생의학 문헌을 대상으로 불확실성 단어 기반 과학적 지식의 패턴을 시간의 흐름에 따라 살펴보고자 한다. 이를 위해 생의학 온톨로지인 UMLS에서 제공하는 의미적 술어를 기반으로 생의학 명제를 분석하였으며, 학문 분야의 패턴을 파악하는데 용이한 DMR 토픽 모델링을 적용하여 생의학 개체의 불확실성 기반 토픽의 동향을 종합적으로 파악하였다. 시간이 흐름에 따라 과학적 지식의 표현은 불확실성이 감소하는 패턴으로 연구의 발전이 이루어지고 있음을 확인하였다.

Keywords

JBGRBQ_2019_v36n1_191_f0001.png 이미지

<그림 1> 연구 개요

JBGRBQ_2019_v36n1_191_f0002.png 이미지

<그림 2> LDA 모델

JBGRBQ_2019_v36n1_191_f0003.png 이미지

<그림 3> DMR 모델

JBGRBQ_2019_v36n1_191_f0004.png 이미지

<그림 4> 연도별 불확실성 단어 데이터 집합의 비율

JBGRBQ_2019_v36n1_191_f0005.png 이미지

<그림 5> 20개 토픽 분포

JBGRBQ_2019_v36n1_191_f0006.png 이미지

<그림 6> 토픽 분포의 4가지 패턴 : (a) rising, (b) falling, (c)convex, (d) flat

<표 1> 최종 데이터 집합

JBGRBQ_2019_v36n1_191_t0001.png 이미지

<표 2> DMR 토픽 모델링의 입력 정보 예시

JBGRBQ_2019_v36n1_191_t0002.png 이미지

<표 3> 상위 10개 개체 정보

JBGRBQ_2019_v36n1_191_t0003.png 이미지

<표 4> 상위 10개 관계 유형 정보

JBGRBQ_2019_v36n1_191_t0004.png 이미지

<표 5> 상위 10개 개체 의미 유형 정보

JBGRBQ_2019_v36n1_191_t0005.png 이미지

<표 6> 상위 10개 의미적 술어 정보

JBGRBQ_2019_v36n1_191_t0006.png 이미지

<표 7> 상위 10개 개체 쌍 정보

JBGRBQ_2019_v36n1_191_t0007.png 이미지

<표 8> 연도별 데이터 집합

JBGRBQ_2019_v36n1_191_t0008.png 이미지

<표 9> 20개 토픽의 상위 5개 개체 토픽 모델링 결과

JBGRBQ_2019_v36n1_191_t0009.png 이미지

<표 10> 토픽 모델링 결과 개체 빈도

JBGRBQ_2019_v36n1_191_t0010.png 이미지

<표 11> 토픽의 표준편차와 순위

JBGRBQ_2019_v36n1_191_t0011.png 이미지

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