Information retrieval approaches are considered as a key technology to empower lay users to access the Web of Data. A large number of related approaches such as Question Answering and Semantic Search have been developed to address this problem. While Question Answering promises more accurate results by returning a specific answer, Semantic Search engines are designed to retrieve the best top-K ranked resources. In this work, we propose *path, a Semantic Search approach that explores term networks for querying RDF knowledge graphs. The adequacy of the approach is evaluated employing benchmark datasets against state-of-the-art Question Answering as well as Semantic Search systems. The results show that *path achieves better F1-score than the currently best performing Semantic Search system.
CITATION STYLE
Marx, E., Höffner, K., Shekarpour, S., Ngomo, A. C. N., Lehmann, J., & Auer, S. (2016). Exploring term networks for semantic search over RDF knowledge graphs. In Communications in Computer and Information Science (Vol. 672, pp. 249–261). Springer Verlag. https://doi.org/10.1007/978-3-319-49157-8_22
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