Titlebook: Research Challenges in Information Science; 18th International C João Araújo,Jose Luis de la Vara,Saïd Assar Conference proceedings 2024 Th

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Research Challenges in Information Science978-3-031-59465-6Series ISSN 1865-1348 Series E-ISSN 1865-1356

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https://doi.org/10.1007/978-3-031-59465-6conceptual modeling; ontologies; machine learning; data analytics; process mining; Business Process Manag

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TD-CRESTS: Top-Down Chunk Retrieval Based on Entity, Section, and Topic Selection levels, prioritizing the most relevant contexts based on their overlap with the query. Our method achieves chunk-level F1-measure of . and . on the SciREX and DrugSemantics benchmark datasets, respectively. It is able to handle diverse domains and languages with a balance between information retrieval efficiency and effectiveness.

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An Ontology-Driven Solution for Capturing Spatial and Temporal Dynamics in Smart Agriculturesk forecasting. This work opens a new perspective regarding the development of smart farming applications, by enabling ontological models to fully exploit the spatial and temporal dimensions of agricultural information.

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