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Quantitative cross impact analysis with latent semantic indexing

: Thorleuchter, Dirk; Poel, Dirk van den

Preprint urn:nbn:de:0011-n-2643850 (201 KByte PDF)
MD5 Fingerprint: c969c0b5eb2db91297cebdc3a9c5f608
Created on: 28.11.2013

Expert Systems with Applications 41 (2014), No.2, pp.406-411
ISSN: 0957-4174
Journal Article, Electronic Publication
Fraunhofer INT ()
cross impact analysis; Latent Semantic Indexing; text mining; conditional probability

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.