My favorite way to implement hierarchies is via closure tables. I read about a few ways to solve hierarchies in the book SQL Antipatterns ( ). WHERE t1.name = 'ELECTRONICS' AND t3.name = 'FLASH' The retrieving one path: SELECT t1.name AS lev1, t2.name as lev2, t3.name as lev3, t4.name as lev4 The retrieving only leaf names: SELECT t1.name FROM LEFT JOIN category AS t3 ON t3.parent = t2.category_id LEFT JOIN category AS t2 ON t2.parent = t1.category_id The query retrieve all your data: SELECT t1.name AS lev1, t2.name as lev2, t3.name as lev3, t4.name as lev4 SELECT * FROM category ORDER BY category_id 2021.I recommend to read the Managing hierarchical data in mysql article.Ĭategory_id INT AUTO_INCREMENT PRIMARY KEY, David Schindler, Felix Bensmann, Stefan Dietze, Frank Krüger, “SoMeSci-A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles”, Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021).“The role of software in science: a knowledge graph-based analysis of software mentions in PubMed Central”. David Schindler, Felix Bensmann, Stefan Dietze, Frank Krüger.While the final set of tasks will still be announced, details for a more exhaustive set of tasks and affiliated baselines can be found here. version, developer, etc), and 3) disambiguation of detected mentions. We will evaluate method performance using traditional IR metrics (P/R/F1) on specific subtasks, such as 1) detection of software mentions and types, 2) detection of related attributes (e.g. The dataset will be expanded to include Computer Science publications following the SomeSci schema. It describes 3,756 software mentions, including type information and extensive metadata, from 1,367 PubMed Central articles. SoMeSci is a knowledge graph of software mentions including 399,942 triples to date. Subtask 2: Additional Information Detection.As a novelty presented with this task, SoMeSci will be extended to include more publications in the fields of Artificial Intelligence (AI) and Computer Science. The task utilizes the Software Mentions in Science - SoMeSci knowledge graph of software mentions (Schindler et al., 2022). Therefore, we invite participants of our shared task to develop robust supervised information extraction models that facilitate the disambiguation of software mentions and relevant metadata in scholarly publications. While the referencing of scientific articles is handled according to well-established patterns, the citation practices of code bases and software programs are less coherent. Additionally, aggregated observations of software citations can help to measure their usage and impact in the long run. Hence, tracking the provenance of software artifacts is becoming an essential aspect of transparency and reproducibility. SOMD: Software Mention Detection in Scholarly Publications Abstractĭata-driven scientific processes strongly rely on the use of software to collect and prepare data and to generate insights via automated analysis.
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