Alnahas, DimaAlagoz, Bans Baykant2024-08-042024-08-042019https://doi.org/10.1109/idap.2019.8875980https://hdl.handle.net/11616/99028International Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 21-22, 2019 -- Inonu Univ, Malatya, TURKEYThis study demonstrates an application of probabilistic relational graph connectivity analysis in bigram models (2-gram) of texts. The probabilistic relation matrices are calculated by estimating bigram co-occurrence probabilities in word sequence of a given short text. Then, deeper probabilistic connectivity relations among word sequences can be considered by calculating powers of probabilistic relation matrix of bigram models. Cosine similarity measure is used to evaluate similarity of probabilistic connectivity patterns of lexemes in a given message. Illustrative analysis examples are presented to discuss results of sentence-wise and short text analyses.eninfo:eu-repo/semantics/closedAccessSemantic networktransitivitygraph connectivitybigram co-occurrence probabilitytext comparisonConference Object10.1109/idap.2019.88759802-s2.0-85074879160N/AWOS:000591781100107N/A