The article describes the specifics of OOP concepts implementation in the open source projects that contain address book model in comparison to OASIS UBL Party Reference model. The open source software analysis is performed using the formal criteria for evaluating the matching between open source project models and reference model based on quantitative characteristics, conceptual graph transformation and cluster analysis. First, the scope coverage, elaboration factor and structural connectedness were calculated based on model parameters. Second, the domain models were represented as conceptual graphs and they were compared based on semantic equivalence. And third, the model was clustered into four subdomains, and quantitative characteistics were evaluated separately for each cluster. Based on the experimental results of model analysis the authors propose methods to reduce the conceptual mismatch between reference model and project models.

Keywords: OOP, semantic analysis, reference model, OASIS UBL, conceptual graph, cluster analysis

The article presents the usage of the semantic network for storing and retrieving information from unstructured sources. The authors describe the semantic network model based on labeled oriented graph, consider basic semantic network elements (concepts, relations and attributes) and basic relation types between elements (hyponymic and meronimic as well as class-instance relations), and define main single and group operations on the network. Besides the method of storing the concept instances and related wordforms and glosses for automated information retrieval from semi-structured sources is provided. The designed semantic network is decomposed into atomic concepts providing absence of stored information redundancy without necessity to apply the normalization procedure. The developed model was applied in several practical tasks, for example in real estate information parsing, the address information system input validation, and information extraction from spreadsheet data. The model was improved and extended based on experimental results.

Keywords: semantic network, graph model, concept, relation, attribute, instance, semi-structured data