Template-based information access, in which templates are constructed for keywords, is a recent development of linked data information retrieval. However, most such approaches suffer from ineffective template management. Because linked data has a structured data representation, we assume the data's inside statistics can effectively influence template management. In this work, we use this influence for template creation, template ranking, and scaling. Our proposal can effectively be used for automatic linked data information retrieval and can be incorporated with other techniques such as ontology inclusion and sophisticated matching to further improve performance. © Springer-Verlag 2013.
CITATION STYLE
Rahoman, M. M., & Ichise, R. (2013). An automated template selection framework for keyword query over linked data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7774 LNCS, pp. 175–190). https://doi.org/10.1007/978-3-642-37996-3_12
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