Quantitative cross impact analysis with latent semantic indexing |
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| Affiliation: | 1. Fraunhofer INT, Appelsgarten 2, D-53879 Euskirchen, Germany;2. Ghent University, Faculty of Economics and Business Administration, Tweekerkenstraat 2, B-9000 Gent, Belgium;1. College of Biomedical Engineering and Instrument Science, Zhejiang University, 310008 Zhou Yiqing Building 510, Zheda road 38#, Hangzhou, Zhejiang, China;2. Department of Information and Communication Engineering, University of Murcia, Spain;1. Innovative Information Industry Research Center, School of Computer Science and Technology, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;2. Information and Communications Research Laboratories, ITRI, Hsinchu, Taiwan, ROC;3. CyLab, Carnegie Mellon University, Pittsburgh, USA;4. Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan, ROC;1. Department of Computer Science and Engineering, University of Bologna, Cesena, FC 47521, Italy;2. Umpi R&D, Cattolica, RN 47841, Italy;1. Graduate School of Water Resources, Sungkyunkwan University, Suwon 440-746, Republic of Korea;2. School of Civil Engineering, Seoul National University of Science and Technology, Seoul 139-743, Republic of Korea;1. Badji Mokhtar University, LRS, Annaba, Algeria;2. Badji Mokhtar University, LRI, Annaba, Algeria;3. Université de Lorraine, LORIA, Nancy, France;4. CNRS UMR 7503, Nancy, France;5. Inria Nancy Grand Est, France |
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| Abstract: | Cross impact analysis (CIA) consists of a set of related methodologies that predict the occurrence probability of a specific event and that also predict the conditional probability of a first event given a second event. The conditional probability can be interpreted as the impact of the second event on the first. Most of the CIA methodologies are qualitative that means the occurrence and conditional probabilities are calculated based on estimations of human experts. In recent years, an increased number of quantitative methodologies can be seen that use a large number of data from databases and the internet. Nearly 80% of all data available in the internet are textual information and thus, knowledge structure based approaches on textual information for calculating the conditional probabilities are proposed in literature. In contrast to related methodologies, this work proposes a new quantitative CIA methodology to predict the conditional probability based on the semantic structure of given textual information. Latent semantic indexing is used to identify the hidden semantic patterns standing behind an event and to calculate the impact of the patterns on other semantic textual patterns representing a different event. This enables to calculate the conditional probabilities semantically. A case study shows that this semantic approach can be used to predict the conditional probability of a technology on a different technology. |
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| Keywords: | Cross impact analysis Latent semantic indexing Text mining Conditional probability |
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