The exponential growth of digital data has underscored the importance of efficient and intelligent bibliographic search systems. Traditional keyword-based search methods often fail to capture the complexity of relationships between concepts, leading to incomplete or irrelevant results. This thesis explores the application of semantic web technologies, such as the Resource Description Framework (RDF) and the SPARQL query language, to enhance bibliographic search capabilities. By utilizing structured datasets from sources like Wikidata and the Mathematics Subject Classification (MSC), the research demonstrates how semantic understanding can significantly improve the accuracy and relevance of search results. The core of this work involves the transformation of traditional bibliographic data into semantic formats, enabling richer queries that extend beyond simple keyword matching. The thesis also addresses challenges in integrating multiple data sources, including mapping issues between Wikidata entities and MSC concepts, and proposes methods to overcome these limitations. Through a case study focused on the mathematical concept of "Linear Inference and Regression," the research illustrates the benefits of a semantically enhanced bibliographic search. The results show that semantic-based search can provide more comprehensive results than conventional methods, particularly by incorporating relationships between broader and narrower concepts. While this approach has limitations related to data availability and completeness, it offers a promising alternative to traditional bibliographic systems, paving the way for more intelligent and flexible search solutions in academic research.