Graph-Based Semantic Analysis of a Scientific Article Corpus
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Abstract
The problem of effective navigation and search for relevant information within growing volumes of scientific publications necessitates a shift from classical full-text search methods to semantic models. This work proposes an approach to structuring a heterogeneous corpus of scientific texts by constructing a Knowledge Graph (KG). A data processing pipeline is developed that encompasses the extraction of metadata, keywords, and structural elements of articles, followed by their integration into a unified graph. Based on the constructed Knowledge Graph, methods for analyzing explicit connections and extracting implicit connections between publications are implemented. The research results demonstrate the effectiveness of the graph-based representation of scientific information for uncovering hidden patterns within subject domains and supporting intelligent navigation.
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References
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