Lesson Learnt from a Large-Scale Industrial Semantic Web Application

Abstract

The design and maintenance of an aero-engine generates a significant amount of documentation. When designing new engines, engineers must obtain knowledge gained from maintenance of existing engines to identify possible areas of concern. We developed a Semantic Web based document repository for transferring front-line maintenance knowledge to design. The Semantic Web is an ideal candidate for this application because of the size and distributed nature of an aerospace manufacturer's operation. The Semantic Web allows us to dynamically cross reference documents with the use of an ontology. However, during the design and implementation of this project, we found deficiencies in the W3C recommended Semantic Web query language SPARQL. It is difficult to answer questions our users sought from the document repository using SPARQL. The problem is that SPARQL is designed for handling textual queries. In industrial applications, many common textual and semantic questions also contain a numerical element, be it data summarization or arithmetic operations. In this paper, we generalize the problems we found with SPARQL, and extend it to cover web applications in non-aerospace domains. Based on this analysis, we recommend that SQL-styled grouping, aggregation and variable operations be added to SPARQL, as they are necessary for industrial applications of the Semantic Web. At the moment, to answer the non-textual questions we identified with an RDF store, custom written software is needed to process the results returned by SPARQL. We incorporated the suggested numerical functionalities from SQL for an example query, and achieved a 21.7% improvement to the speed of execution. More importantly, we eliminate the need of extra processing in software, and thus make it easier and quicker to develop Semantic Web applications.

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ORCID for Gary B Wills: orcid.org/0000-0001-5771-4088

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