• Title/Summary/Keyword: Causal Relations

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Definition and Extraction of Causal Relations for Question-Answering on Fault-Diagnosis of Electronic Devices (전자장비 고장진단 질의응답을 위한 인과관계 정의 및 추출)

  • Lee, Sheen-Mok;Shin, Ji-Ae
    • Journal of KIISE:Software and Applications
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    • v.35 no.5
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    • pp.335-346
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    • 2008
  • Causal relations in ontology should be defined based on the inference types necessary to solve problems specific to application as well as domain. In this paper, we present a model to define and extract causal relations for application ontology for Question-Answering (QA) on fault-diagnosis of electronic devices. Causal categories are defined by analyzing generic patterns of QA application; the relations between concepts in the corpus belonging to the causal categories are defined as causal relations. Instances of casual relations are extracted using lexical patterns in the concept definitions of domain, and extended incrementally with information from thesaurus. On the evaluation by domain specialists, our model shows precision of 92.3% in classification of relations and precision of 80.7% in identifying causal relations at the extraction phase.

A Study on Causal Relations among BSC Performance Measurement Indexes - Focused on the case of C University Hospital - (BSC 성과측정지표간의 인과관계에 관한 연구 - C대학병원 사례 중심으로 -)

  • Shin, Seung-Kwon
    • Korean Business Review
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    • v.20 no.2
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    • pp.119-133
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    • 2007
  • The present study purposed to examine whether BSC, a management renovation method, is as effective in university hospitals, which are non-profit institutions, as it is in profit-making corporations. In order to determine causal relations among the BSC performance measurement indexes, we analyzed a case of university hospital using a structural equation model. The results of analyzing the causal relations among the BSC performance measurement indexes were all statistically significant, and therefore the research hypotheses were all accepted. Future research needs to study causal relations among the BSC performance measurement indexes from the viewpoint of the learning of financial data, growth, internal processes, and customers.

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Category-Based Feature Inference: Testing Causal Strength (범주기반 속성추론: 인과관계 강도의 검증)

  • JunHyoung Jo;Hyung-Chul O. Li;ShinWoo Kim
    • Science of Emotion and Sensibility
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    • v.26 no.1
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    • pp.55-64
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    • 2023
  • This research investigated category-based feature inference when category features were connected in common cause and common effect causal networks. Previous studies that tested feature inference in causal categories showed unique inference patterns depending on causal direction, number of related features, whether the to-be-inferred feature was cause or effect, etc. However, these prior studies primarily focused on inference pattens that arise from causal relations, and few studies directly explored how the effects of causal relations vary depending on causal strength. We tested feature inference in common cause (Expt. 1) and common effect (Expt. 2) causal categories when casual strengths were either strong or weak. To this end, we had participants learn causal categories where features were causally linked and then perform feature inference task. The results showed that causal strengths as well as causal relations had important impacts on feature inference. When causal strength was strong, inference for common cause feature became weaker but that for the common effect feature became stronger. Moreover, when causal strength was strong and common cause was present, inference for the effect features became stronger, whereas the results were reversed in common effect networks. In particular, in common effect networks, casual discounting was more evident with strong causal strength. These results consistently demonstrate that participants consider not only causal relations but also causal strength in feature inference of causal categories.

Sequence Labeling-based Multiple Causal Relations Extraction using Pre-trained Language Model for Maritime Accident Prevention (해양사고 예방을 위한 사전학습 언어모델의 순차적 레이블링 기반 복수 인과관계 추출)

  • Ki-Yeong Moon;Do-Hyun Kim;Tae-Hoon Yang;Sang-Duck Lee
    • Journal of the Korean Society of Safety
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    • v.38 no.5
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    • pp.51-57
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    • 2023
  • Numerous studies have been conducted to analyze the causal relationships of maritime accidents using natural language processing techniques. However, when multiple causes and effects are associated with a single accident, the effectiveness of extracting these causal relations diminishes. To address this challenge, we compiled a dataset using verdicts from maritime accident cases in this study, analyzed their causal relations, and applied labeling considering the association information of various causes and effects. In addition, to validate the efficacy of our proposed methodology, we fine-tuned the KoELECTRA Korean language model. The results of our validation process demonstrated the ability of our approach to successfully extract multiple causal relationships from maritime accident cases.

The Reciprocal Relationship between Caregiver Relations and Peer Relations of Children in Out-of-home Care: Longitudinal Study Using Autoregressive Cross-lagged Modeling (가정외보호 아동의 양육자 관계와 교우관계의 상호 영향: 자기회귀교차지연모형을 활용한 종단연구)

  • Kim, Dami;Kang, Hyunah
    • Journal of Child Welfare and Development
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    • v.16 no.2
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    • pp.109-135
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    • 2018
  • The purpose of this study was to analyze the longitudinal causal relationship between caregiver relations and peer relations of children in out-of-home care. We analyzed the three years(2011-2013) of longitudinal data from the Panel Study on Korean Children in Out-of-Home Care. The autoregressive cross-lagged model (ARCL) was used to measure the longitudinal causal relationship between caregiver relations and peer relations. As a result, first, caregiver relations and peer relations showed stability over time. In other words, the results of the measurement at three time points showed that the caregiver relations and peer relations at the previous time had a significant effect on the caregiver relations and peer relations at the later time point. Second, the previous caregiver relations had a significant effect on the subsequent peer relations over time. Third, the previous peer relations had a significant effect on the subsequent caregiver relations over time. This study confirmed the interrelationships of caregiver relations and peer relations of children in care by examining the longitudinal data using the longitudinal analysis method.

An Exploratory Study on Marital Dissatisfaction and Extramarital Relations among married Women (기혼여성의 결혼 불만족과 혼외관계에 대한 탐색적 고찰)

  • 공미혜
    • Journal of the Korean Home Economics Association
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    • v.40 no.1
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    • pp.195-208
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    • 2002
  • This study examines how marital dissatisfaction is connected with extramarital relations among married women. To explore this subject,1 am using in-depth interview techniques. The data from 16 married women who involved (and are involving) extramarital relations are collected in semistructured interviews. In this article, I describe four specific types of extramarital relations with particular relevance to marital dissatisfaction: (1) temporary extramarital relations caused by marital dissatisfaction, (2) positive extramarital relations as maintaining dissatisfied marriage, (3) unavoidable extramarital relations as breaking dissatisfied marriage, and (4) extramarital relations as a part of life with satisfied marriage. With these results, I believe that equity theory could be applicable in explaining the relationships between marital satisfaction and extramarital relations. There are limitations when the qualitative research is analyzed. One problem is measurement. It is difficult to measure equality (or equity), life dissatisfaction, and other concepts. furthermore, this study is not abbe to explain causal relationships among equality, life dissatisfaction, actual extramarital relations. The future study should perhaps be in quantitative research focused on the causal model in which all exchange variables are conceptualized and properly measured for the intimate relationship.

A Mechanism for Combining Quantitative and Qualitative Reasoning (정량 추론과 정성 추론의 통합 메카니즘 : 주가예측의 적용)

  • Kim, Myoung-Jong
    • Knowledge Management Research
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    • v.10 no.2
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    • pp.35-48
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    • 2009
  • The paper proposes a quantitative causal ordering map (QCOM) to combine qualitative and quantitative methods in a framework. The procedures for developing QCOM consist of three phases. The first phase is to collect partially known causal dependencies from experts and to convert them into relations and causal nodes of a model graph. The second phase is to find the global causal structure by tracing causality among relation and causal nodes and to represent it in causal ordering graph with signed coefficient. Causal ordering graph is converted into QCOM by assigning regression coefficient estimated from path analysis in the third phase. Experiments with the prediction model of Korea stock price show results as following; First, the QCOM can support the design of qualitative and quantitative model by finding the global causal structure from partially known causal dependencies. Second, the QCOM can be used as an integration tool of qualitative and quantitative model to offerhigher explanatory capability and quantitative measurability. The QCOM with static and dynamic analysis is applied to investigate the changes in factors involved in the model at present as well discrete times in the future.

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A Causational Study for Urban 4-legged Signalized Intersections using Structural Equation Method (구조방정식을 이용한 도시부 4지 신호교차로의 사고원인 분석)

  • Oh, Jutaek;Lee, Sangkyu;Heo, Taeyoung;Hwang, Jeongwon
    • International Journal of Highway Engineering
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    • v.14 no.6
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    • pp.121-129
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    • 2012
  • PURPOSES : Traffic accidents at intersections have been increased annually so that it is required to examine the causations to reduce the accidents. However, the current existing accident models were developed mainly with non-linear regression models such as Poisson methods. These non-linear regression methods lack to reveal complicated causations for traffic accidents, though they are right choices to study randomness and non-linearity of accidents. Therefore, to reveal the complicated causations of traffic accidents, this study used structural equation methods(SEM). METHODS : SEM used in this study is a statistical technique for estimating causal relations using a combination of statistical data and qualitative causal assumptions. SEM allow exploratory modeling, meaning they are suited to theory development. The method is tested against the obtained measurement data to determine how well the model fits the data. Among the strengths of SEM is the ability to construct latent variables: variables which are not measured directly, but are estimated in the model from several measured variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which allows the structural relations between latent variables to be accurately estimated. RESULTS : The study results showed that causal factors could be grouped into 3. Factor 1 includes traffic variables, and Factor 2 contains turning traffic variables. Factor 3 consists of other road element variables such as speed limits or signal cycles. CONCLUSIONS : Non-linear regression models can be used to develop accident predictions models. However, they lack to estimate causal factors, because they select only few significant variables to raise the accuracy of the model performance. Compared to the regressions, SEM has merits to estimate causal factors affecting accidents, because it allows the structural relations between latent variables. Therefore, this study used SEM to estimate causal factors affecting accident at urban signalized intersections.

A Study on Causal Relationships among Sensibility Satisfaction Factors for Mobile Phone (이동통신 단말기의 감성만족 요소간 인과관계에 관한 연구)

  • Jeon, Yeong-Ho;Baek, In-Gi;Kim, Jeong-Il;Son, Gi-Hyeok
    • Journal of the Ergonomics Society of Korea
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    • v.22 no.2
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    • pp.1-13
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    • 2003
  • In general, causal relationship for theoretical concepts is hypothesized based on precedent studies and tested by a structural equation model. However, when theoretical backgrounds are scarce or absent, the causal relationship is hypothesized operatively by the purpose and scope of research and tested by overall goodness-of-fit indices such as GFI and RMR. Such a causal relationship can't be most appropriate statistically because it is selected as specific relationship from researcher's view among possible causal relationships. Therefore, this study is to propose a procedure for identifying the causal relationship that produces the best GFI among possible causal relationships for theoretical concepts.

A Study on Theoretical Improvement of Causal Mapping for Dynamic Analysis and Design (동태적 분석 및 설계를 위한 인과지도 작성법의 한계와 개선방안에 관한 연구)

  • Jung, Jae-Un;Kim, Hyun-Soo
    • Korean System Dynamics Review
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    • v.10 no.1
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    • pp.33-60
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    • 2009
  • This study explores the limitation in making a causal model through an existing case and proposes an alternative plan to improve a theoretical system of causation modeling. To make a dynamic and actual model, several principles are needed such as reality based analysis of system structures and dynamics, consistent expression of causations, conversion of numerical formulas to causal relations, classification and arrangement of variables by size of concept, etc. However, it is hard to find cases to apply these considerations from existing models in System Dynamics. Therefore, this study verifies errors of derived models from literatures and proposes principles and guides that should be considered to make a sound dynamic model on a causal map. It contributes to making an opportunity for exciting public opinion to improve theory about causal maps, yet it has limitation that the study does not advance forward to the experimental step. For future study, it plans to make up by classifying and leveling causal variables, developing a dynamic BSC model.

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