Abstract
Since the introduction of computing machines into companies and industries, searching large enterprise data is an open challenge including diverse and distributed datasets, missing alignment of vocabularies within divisions as well as data isolated in format silos. In this article, we report the requirements of commercial enterprises to the next generation of semantic search engine for large, distributed data. We describe our elicitation process to gather end user requirements, the challenges arising for real-world use cases as well as how such an implementation of this paradigm can be benchmarked. In the end, we present the design of the DIESEL search engine, which aims to implement the requirements of commercial enterprise to semantic search.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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At the 22nd April, we gathered 13 responses. In the online version you can also see preliminary reports.
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Acknowledgements
This work has been supported by Eurostars projects DIESEL (E!9367) and QAMEL (E!9725) as well as the European Union’s H2020 research and innovation action HOBBIT (GA 688227).
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Usbeck, R., Röder, M., Haase, P., Kozlov, A., Saleem, M., Ngomo, AC.N. (2016). Requirements to Modern Semantic Search Engine. In: Ngonga Ngomo, AC., Křemen, P. (eds) Knowledge Engineering and Semantic Web. KESW 2016. Communications in Computer and Information Science, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-45880-9_25
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DOI: https://doi.org/10.1007/978-3-319-45880-9_25
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