Automatic structuring and retrieval of large text files
Communications of the ACM
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Semantic clustering: Identifying topics in source code
Information and Software Technology
A semantic term weighting scheme for text categorization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Neurocomputing
Expert Systems with Applications: An International Journal
Topics modeling based on selective Zipf distribution
Expert Systems with Applications: An International Journal
Global data mining: An empirical study of current trends, future forecasts and technology diffusions
Expert Systems with Applications: An International Journal
Using latent topics to enhance search and recommendation in Enterprise Social Software
Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Predicting e-commerce company success by mining the text of its publicly-accessible website
Expert Systems with Applications: An International Journal
Improved multilevel security with latent semantic indexing
Expert Systems with Applications: An International Journal
Technology classification with latent semantic indexing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Protecting research and technology from espionage
Expert Systems with Applications: An International Journal
Web mining based extraction of problem solution ideas
Expert Systems with Applications: An International Journal
Weak signal identification with semantic web mining
Expert Systems with Applications: An International Journal
Semantic compared cross impact analysis
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.