AUTOMATED SYSTEM FOR DETECTION OF NON-STANDARD ACTIONS USING SCRIPTURAL ANALYSIS OF THE TEXT

Abstract

The scenario (narrative schemas) is some established (in society) sequence of steps to achieve the set goal and contains the most complete information about all possible ways of development of the described situation (with selection points and branches). The creation of the XML platform gave rise to a new high-tech and technologically more advanced stage in the development of the Web. As a result, the XML platform becomes a significant component in the technology of information systems development, and the tendency of their integration at the level of corporations, agencies, ministries only strengthens the position of XML in the field of information technology in general.  A system for automatic detection of non-standard scripts in text messages has been developed. System programming consists of stages of ontology formation, sentence parsing and scenario comparison. the classic natural language processing (NLP) method, which supports the most common tasks such as tokenization, sentence segmentation, tagging of a part of speech, extraction of named entities, partitioning, parsing and co-referential resolution, is used for parsing sentences in the system. Maximum entropy and machine learning based on perceptrons are also possible. Ontologies are stored using OWL technology. The object-target sentence parsers with the described OWL are compared in the analysis process. From a SPARQL query on a source object, query models are returned to the table object. The table class is the base class for all table objects and provides an interface for accessing values in the rows and columns of the results table. If the table object has exactly three columns, it can be used to build a new data source object. This provides a convenient mechanism for retrieving a subset of data from one data source and adding them to another. In the context of the RDF API, a node is defined as all statements about the subject of a URI. The content of the table is compared with the semantics of the sentence. If the sentence scenario does not match the OWL ontology model, there is a possibility of atypical object actions. In this case, a conclusion is formed about the suspicion of the message. For more correct use of possibilities of the analysis of the text it is necessary to form the case of ontologies or to use existing (Akutan, Amazon, etc.) taking into account their features. To increase the ontologies of objects, it is possible to use additional neural network teaching methods.