A distributional approach for terminological semantic search on the Linked Data Web
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Editorial: Querying linked data graphs using semantic relatedness: A vocabulary independent approach
Data & Knowledge Engineering
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The vision of creating a Linked Data Web brings together the challenge of allowing queries across highly heterogeneous and distributed datasets. In order to query Linked Data on the Web today, end-users need to be aware of which datasets potentially contain the data and also which data model describes these datasets. The process of allowing users to expressively query relationships in RDF while abstracting them from the underlying data model represents a fundamental problem for Web-scale Linked Data consumption. This article introduces a multidimensional semantic space model which enables data model independent natural language queries over RDF data. The center of the approach relies on the use of a distributional semantic model to address the level of semantic interpretation demanded to build the data model independent approach. The final multidimensional semantic space proved to be flexible and precise under real-world query conditions achieving mean reciprocal rank = 0.516, avg. precision = 0.482 and avg. recall = 0.491.