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Field
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, hybrid retrieval (BM25 + embeddings), graph databases (e.g., Neo4j), RDF/SPARQL, schema mapping, entity resolution. 3. Research Fit: Interest in applied research with real operational datasets. Motivation
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, hybrid retrieval (BM25 + embeddings), graph databases (e.g., Neo4j), RDF/SPARQL, schema mapping, entity resolution. 3. Research Fit: Interest in applied research with real operational datasets. Motivation
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representation languages such as RDF, OWL, SPARQL Experience with Semantic Web technologies, including graph databases (GraphDB, Neo4j), relevant libraries (RDFLib, owlready2) and other software (Protégé
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(e.g.MySQL) and NoSQL databases (e.g. MongoDB) for efficient and flexible data management Well-developed expertise in knowledge graph technologies (e.g., RDF, SPARQL, OWL, GraphDB (Neo4j)) and semantic web
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modelling, and some experience with linked data standards and knowledge graph tools (e.g., RDF, OWL, SHACL); familiarity with or interest for GIS, cartographic maps, geodata infrastructures and geo-analytical
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Framework (RDF). enables advanced data mining queries using the SPARQL query language. provides a natural language-based interface to perform these queries on the knowledge graph using a large language model