Abstract
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 F\(_1\)-score than the currently best performing Semantic Search system.
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Notes
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Not to be confused with rdfs:label.
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Not to be confused with an RDFTerm.
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Other labeling properties may also be used.
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The output of the tokenizer used in this example are lowercase lexemes from a literal.
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Acknowledgements
This work was supported by a grant from the EU H2020 Framework Programme provided for the projects Big Data Europe (GA no. 644564), HOBBIT (GA no. 688227), and CNPq under the program Ciências Sem Fronteiras.
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Marx, E., Höffner, K., Shekarpour, S., Ngomo, AC.N., Lehmann, J., Auer, S. (2016). Exploring Term Networks for Semantic Search over RDF Knowledge Graphs. In: Garoufallou, E., Subirats Coll, I., Stellato, A., Greenberg, J. (eds) Metadata and Semantics Research. MTSR 2016. Communications in Computer and Information Science, vol 672. Springer, Cham. https://doi.org/10.1007/978-3-319-49157-8_22
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