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Progress in Computing Applications(PCA)
 

Open Source Tools for Querying Virtual Ontology
Lucas Peres, Ticiana L Coelho da Silva, Jose Macedo, David Araujo
Insight Data Science Lab, Fortaleza - CE, BR
Abstract: The Web has evolved to a large variety of data usually published in RDF from multiple domains. A recurrent problem in recent literature concerns to perform a search over RDF instead of using structured queries in triple-pattern-based languages like SPARQL, which only expert programmers can precisely specify their information needs. In this paper, we propose Von-QBE, an open source tool to query over RDF databases without any technical knowledge about RDF or the queried ontology structure. This differs from the-state-of-art tools by being schema-based instead of instance-based. It can be impracticable to use instance-based approaches in big data scenarios where the RDF data is huge and demands lots of computational resources to keep the knowledge base in memory. Moreover, most of these solutions need the knowledge base materialized into RDF(or triplified), which can be costly for legacy bases. We present various demonstration scenarios using the IMDB movie ontology.
Keywords: RDF Schema, PARQL Query, Query by Example Open Source Tools for Querying Virtual Ontology
DOI:https://doi.org/10.6025/pca/2020/9/1/6-10
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References:

[1] Arnaout, H., Elbassuoni, S. (2018). Effective searching of rdf knowledge graphs. Journal of Web Semantics 48 (2018), 66 – 84.
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[6] Usbeck, R., Ngomo, A.-C. N., Bühmann, L., Unger, C. (2015). Hawk–hybrid question answering using linked data. In European Semantic Web Conference (2015), Springer, p 353–368.
[7] Xu, K., Zhang, S., Feng, Y., Zhao, D. (2014). Answering natural language questions via phrasal semantic parsing. In Natural Language Processing and Chinese Computing. Springer, 2014, p 333–344.
[8] Yahya, M., Berberich, K., Elbassuoni, S., Ramanath, M., Tresp, V., Weikum, G. (2012). Natural language questions for the web of data. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (2012), Association for Computational Linguistics, p 379–390.
[9] Yih, S. W.-t., Chang, M.-W., He, X., Gao, J. Semantic parsing via staged query graph generation: Question answering with knowledge base.


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