Abstract
Policy recommendations aim to inform people who are faced with policy decisions on specific issues about how research and evidence can assist them in making the best decisions possible. This paper proposes an ontology-focused semantical driven integrative system for policy recommendation. The recommendation is user query-centric and uses Structural Topic Modelling to find topics that can be correlated. The semantic similarities are computed using Resnik and concept similarity methods to achieve ontology alignment, and for the alignment of principle classes, three models, normalized compression distance, Twitter semantic similarity, and Hiep’s Evenness Index, are used. The IPR achieves the best-in-class accuracy of 94.72% and precision of 93.14% for a wide range of recommendations over the other baseline models, making it an efficient and semantically compliant system for the policies recommendation.
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Singh, D., Deepak, G. (2022). IPR: Integrative Policy Recommendation Framework Based on Hybrid Semantics. In: Villazón-Terrazas, B., Ortiz-Rodriguez, F., Tiwari, S., Sicilia, MA., Martín-Moncunill, D. (eds) Knowledge Graphs and Semantic Web . KGSWC 2022. Communications in Computer and Information Science, vol 1686. Springer, Cham. https://doi.org/10.1007/978-3-031-21422-6_9
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