A probabilistic relational model for keyword extraction

A large portion of real-world data is stored relational. In constant, most statistical learning methods work only with "flat" data representations. Probabilistic relational models (PRMs) is one of the models considers the characteristics of relational data. PRMs allow the properties of an object to depend probabilistically both on other properties of that object and on properties of related objects. In this paper an attempt is made to heed keywords extraction. The keywords are not only essential for academic papers but also important for web page retrieval, text mining, and document classification. In this paper, a C++ algorithm is presented to extract keywords. The algorithm generates and extracts keywords from a poetry book. The C++ programming language is used to implement our algorithm in order to obtain experimental results. The results indicate that "Love", "Heart", and "Eyes" are the most important keywords of the selected book.