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    @Article{OJIS_2017v4i1n01_Ekaputra,
        title     = {Ontology-Based Data Integration in Multi-Disciplinary Engineering Environments: A Review},
        author    = {Fajar J. Ekaputra and
                     Marta Sabou and
                     Estefan\'{i}a Serral and
                     Elmar Kiesling and
                     Stefan Biffl},
        journal   = {Open Journal of Information Systems (OJIS)},
        issn      = {2198-9281},
        year      = {2017},
        volume    = {4},
        number    = {1},
        pages     = {1--26},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201711266863},
        urn       = {urn:nbn:de:101:1-201711266863},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Today's industrial production plants are complex mechatronic systems. In the course of the production plant lifecycle, engineers from a variety of disciplines (e.g., mechanics, electronics, automation) need to collaborate in multi-disciplinary settings that are characterized by heterogeneity in terminology, methods, and tools. This collaboration yields a variety of engineering artifacts that need to be linked and integrated, which on the technical level is reflected in the need to integrate heterogeneous data. Semantic Web technologies, in particular ontologybased data integration (OBDI), are promising to tackle this challenge that has attracted strong interest from the engineering research community. This interest has resulted in a growing body of literature that is dispersed across the Semantic Web and Automation System Engineering research communities and has not been systematically reviewed so far. We address this gap with a survey reflecting on OBDI applications in the context of Multi-Disciplinary Engineering Environment (MDEE). To this end, we analyze and compare 23 OBDI applications from both the Semantic Web and the Automation System Engineering research communities. Based on this analysis, we (i) categorize OBDI variants used in MDEE, (ii) identify key problem context characteristics, (iii) compare strengths and limitations of OBDI variants as a function of problem context, and (iv) provide recommendation guidelines for the selection of OBDI variants and technologies for OBDI in MDEE.}
    }