A Development of Designing a Sensor N/W for Retrieving the Data from WSN via SBT

Search engines are essential for getting information from web sites in the internet age. They use a variety of ranking algorithms, depending on content-based information extraction techniques or statistical searching strategies, to organise the retrieved results. It is still difficult, nevertheless, to comprehend the underlying information on each web page without opening each one individually. This challenge motivates the concept and implementation of an object-attribute-value (O-A-V) information extraction system as a web model. To help with future searches, this model functions as a user dictionary by helping to filter search terms in queries. To evaluate the first model, natural language processing (NLP) and semantic web (SW) queries in the form of English phrases are employed. Similar to this, content-based picture information extraction and retrieval against user queries is used by image search engines like Google Images. Using picture descriptions, a domain ontology is constructed in order to close the semantic gap between user expectations and image retrieval outcomes. The second suggested model looks at subject-predicate-object (S-P-O) interactions by analysing natural language user queries using an NLP parser algorithm. This extraction builds upon the O-A-V web model, which is ontology-based. The article presents a SPARQL auto-query generation module to auto-generate SPARQL questions by utilising S-P-O extraction and taking into account the intricacy of creating SPARQL queries from the standpoint of a user. The auto-generated SPARQL query is used to obtain photos after these queries are placed on the ontologies. Systems for retrieving documents and images were developed and evaluated against industry standards in order to provide answers to these problems. The outcomes show improvements over baseline systems.