During today’s period about current information technology, great amount about information be produced each next toward allow succeeding information aggregation moreover psychiatry. but, the IT infrastructures to contain element set awake more than previous little decades also ought near currently be used designed for it principle be awfully heterogeneous also complex. Because result, tasks used for scrutinizing information, such because gathering, searching, kinds also giving out information grow to be extremely time-consuming. It creates difficult near recognized revelation, such like Internet about making, which follow the objective about declaration the ease of use about concurrent information on several time also set in an business location. Near decrease the time just before analytics in such location, we near a information eating, combination also giving out proceed consisting about a flexible also configurable information eating pipeline as well as a dynamic semantic information period name ESKAPE. The major objective be, consequently, the convenient right of entry to information also Meta information enclosed inside machines moreover additional systems lying on the superstore. Moreover, it provides the opportunity near onward the together information near a configurable endpoint, such information mere. ESKAPE acts like individual about person`s endpoints enable dynamic semantic information incorporation also processing. Near explain information sets by dynamic semantic models initiated as of the Semantic Web, information analyst be clever near realized procedure also find out these information sets additional competently. ESKAPE skin a three or more - layered information storage structural design consisting about an information layer intended for accumulated included untreated information sets, a layer included user-defined semantic models near illustrated the relative acquaintance required near understand the accumulated information also a top layer bent by a incessantly developing acquaintance graph, unite semantic information since every individual near semantic models. Based lying on it storage system, ESKAPE facilitate the elastic annotation as well as well-organized investigate also giving out about information basis lacking behind the skill about study also query the original raw information by logical gear. The text suggests to a lot of obstacle have to still be alive deal by near gets improved repeated translations. Individual about these obstacles be lexical also syntactic ambiguity. A promising method about conquered it difficulty be by Semantic Web technologies. It article presents the consequences about a systematic evaluation about machine translation come near to rely lying on Semantic Web technologies used for translating texts. Generally, our inspection propose to as Semantic Web technologies be able to improve the excellence about machine translation production used for a variety about problems, the grouping about equally be still inside its infancy. We there discuss our come near also its profit with limits based lying on a real-world industrial, engineering also scientific utilized case.

semantic web information stage, time to analytics; semantic modeling; knowledge graph; applied semantics

[1] M. Popovi´c, Class error rates for evaluation of machine translation output, in: Proceedings of the Seventh Workshop on Statistical Ma-chine Translation, Association for Computational Linguistics, 2012, pp. 71–75.
[2] M. R. Costa-Jussa, M. Farrús, J. B. Mariño, J. A. Fonollosa, Study and comparison of rule-based and statistical Catalan-Spanish machine translation systems, Computing and Informatics 31 (2) (2012) 245– 270.
[3] G. Thurmair, Comparing rule-based and statistical MT output, in: The Workshop Programme, 2004, p. 5.
[4] P. F. Brown, J. Cocke, S. A. D. Pietra, V. J. D. Pietra, F. Jelinek, J. D. Lafferty, R. L. Mercer, P. S. Roossin, A statistical approach to machine transla-tion, Computational linguistics 16 (2) (1990) 79–85.
[5] M. R. Costa-Jussa, J. A. Fonollosa, Latest trends in hybrid machine translation and its applications, Computer Speech & Language 32 (1) (2015) 3–10.
[6] M. R. Costa-jussà, How much hybridization does machine translation Need?, Journal of the Association for Information Science and Tech-nology 66 (10) (2015) 2160–2165.
[7] G. Thurmair, Comparing different architectures of hybrid Machine Translation systems, MT Summit XII: proceedings of the twelfth Ma-chine Translation Summit (2009) 340–347.
[8] P. Koehn, R. Knowles, Six Challenges for Neural Machine Translation, arXiv preprint arXiv:1706.03872.
[9] T. Berners-Lee, J. Hendler, O. Lassila, The Semantic Web, Scientific american 284 (5) (2001) 34–43.
[10] T. Heuss, Lessons learned (and questions raised) from an interdisci-plinary Machine Translation approach, in: Position paper for the W3C Workshop on the Open Data on the Web, 2013, pp. 23–24.
[11] A. Ettinger, S. Gella, M. Labeau, C. O. Alm, M. Carpuat, M. Dredze (Eds.), Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL, Vancouver, Canada, 2017.
[12] K. Erk, N. A. Smith (Eds.), Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL, Berlin, Germany, 2016.
[13] C. Zong, M. Strube (Eds.), Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th Interna-tional Joint Conference on Natural Language Processing, ACL, Beijing, China, 2015.
[14] K. Toutanova, H. Wu (Eds.), Proceedings of the 52nd Annual Meet-ing of the Association for Computational Linguistics, ACL, Baltimore, Maryland, 2014.
[15] T. H. K. U. o. S. Pascale Fung, Technology, U. o. E. Massimo Poesio (Eds.), Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL, Sofia, Bul-garia, 2013.
[16] H. Li, C.-Y. Lin, M. Osborne, G. G. Lee, J. C. Park (Eds.), Proceedings of the 50th Annual Meeting of the Association for Computational Lin-guistics (Volume 1: Long Papers), ACL, Jeju Island, Korea, 2012.
[17] Y. Matsumoto, R. Mihalcea (Eds.), Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL, Portland, Oregon, USA, 2011.
[18] J. Hajiˇc, S. Carberry, S. Clark, J. Nivre (Eds.), Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL, Uppsala, Sweden, 2010.
[19] K. Knight, A. Nenkova, O. Rambow (Eds.), Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, ACL, San Diego, California, 2016.
[20] R. Mihalcea, J. Y. Chai, A. Sarkar (Eds.), Proceedings of the 2015 Con-ference of the North American Chapter of the Association for Com-putational Linguistics: Human Language Technologies, ACL, Denver, Colorado, USA, 2015.
[21] L. Vanderwende, H. D. III, K. Kirchhoff (Eds.), Proceedings of the 2013 Conference of the North American Chapter of the Association of Com-putational Linguistics: Human Language Technologies, ACL, Atlanta, Georgia, USA, 2013.
[22] E. Fosler-Lussier, E. Riloff, S. Bangalore (Eds.), Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, ACL, Montréal, Canada, 2012.
[23] R. Kaplan, J. Burstein, M. Harper, G. Penn (Eds.), Proceedings of the 2010 Annual Conference of the North American Chapter of the Associ-ation for Computational Linguistics: Human Language Technologies, ACL, Los Angeles, California, 2010.
[24] M. Palmer, R. Hwa, S. Riedel (Eds.), Proceedings of the 2017 Con-ference on Empirical Methods in Natural Language Processing, ACL, Copenhagen, Denmark, 2017.
[25] J. Su, X. Carreras, K. Duh (Eds.), Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, ACL, Texas, USA, 2016.
[26] L. M. rquez, C. Callison-Burch, J. Su (Eds.), Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, ACL, Lisbon, Portugal, 2015.
[27] Q. C. R. I. Alessandro Moschitti, G. Bo Pang, U. o. A. Walter Daelemans (Eds.), Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, ACL, Doha, Qatar, 2014.
[28] D. Yarowsky, T. Baldwin, A. Korhonen, K. Livescu, S. Bethard (Eds.), Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, ACL, Seattle, Washington, USA, 2013.
[29] J. Tsujii, J. Henderson, M. PaÃEÅÿcaˇ (Eds.), Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Process-ing and Computational Natural Language Learning, ACL, Jeju Island, Korea, 2012.
[30] R. Barzilay, M. Johnson (Eds.), Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, ACL, Edinburgh, Scotland, UK., 2011.
[31] H. Li, L. M‘arquez (Eds.), Proceedings of the 2010 Conference on Em-pirical Methods in Natural Language Processing, ACL, Cambridge, MA, 2010.
[32] N. Calzolari, Y. Matsumoto, R. Prasad (Eds.), Proceedings of COLING, the 26th International Conference on Computational Linguistics, ACL, Osaka, Japan, 2014.
[33] J. Tsujii, J. Hajic (Eds.), Proceedings of COLING, the 25th International Conference on Computational Linguistics, ACL, Dublin, Ireland, 2014.
[34] M. Kay, C. Boitet (Eds.), Proceedings of COLING, the 24th Interna-tional Conference on Computational Linguistics, ACL, Mumbai, India, 2012.
[35] C.-R. Huang, D. Jurafsky (Eds.), Proceedings of COLING, the 23th In-ternational Conference on Computational Linguistics, ACL, Beijing, China, 2010.
[36] R. Barrett, R. Cummings, E. Agichtein, E. Gabrilovich (Eds.), WWW ’17 Proceedings of the 26th International Conference on World Wide Web, ACM, Perth, Australia, 2017.
[37]J. Bourdeau, J. Hendler, R. Nkambou, I. Horrocks, B. Y. Zhao (Eds.),’16 Proceedings of the 25th International Conference on World Wide Web, ACM, Montreal, Canada, 2016
[38]S. Srinivasan, K. Ramamritham, A. Kumar, M. P. Ravindra, E. Bertino, r Kumar (Eds.), WWW ’11: Proceedings of the 20th International Con-ference on World Wide Web, ACM, New York, NY, USA, 2011.
[39]A. Mille, F. L. Gandon, J. Misselis, M. Rabinovich, S. Staab (Eds.), WWW ’12: Proceedings of the 21st International Conference on World Wide Web, ACM, New York, NY, USA, 2012.
[40]D. Schwabe, V. A. F. Almeida, H. Glaser, R. A. Baeza-Yates, S. B. Moon (Eds.), WWW ’13 Companion: Proceedings of the 22Nd International Conference on World Wide Web Companion, International World Wide Web Conferences Steering Committee, Rio de Janeiro, Brazil, 2013.
[41]C. Chung, A. Z. Broder, K. Shim, T. Suel (Eds.), WWW ’14: Proceedings of the 23rd International Conference on World Wide Web, ACM, New York, NY, USA, 2014.
[42]A. Gangemi, S. Leonardi, A. Panconesi (Eds.), WWW ’15 Companion: Proceedings of the 24th International Conference on World Wide Web Companion, ACM, Republic and Canton of Geneva, Switzerland, 2015.
[43]P. Buitelaar, P. Cimiano, E. Montiel-Ponsoda (Eds.), Proceedings of the 1st International Workshop on the Multilingual Semantic Web, CEUR-WS.org, Raleigh, North Carolina, USA, 2010.
[44]L. Aroyo, H. Welty, Chris Alani, J. Taylor, A. Bernstein, L. Kagal, N. Noy, E. Blomqvist (Eds.), The Semantic Web–ISWC 2011, Springer-Verlag, Berlin Heidelberg, Germany, 2011.
[45]P. Cudré-Mauroux, J. Heflin, E. Sirin, T. Tudorache, J. Euzenat, M. Hauswirth, J. X. Parreira, J. Hendler, G. Schreiber, A. Bernstein, E. Blomqvist (Eds.), The Semantic Web–ISWC 2012, Springer-Verlag, Berlin Heidelberg, Germany, 2012.
[46]H. Alani, L. Kagal, A. Fokoue, P. Groth, C. Biemann, J. X. Parreira, L. Aroyo, N. Noy, C. Welty, K. Janowicz (Eds.), The Semantic Web–ISWC 2013, Springer-Verlag, Berlin Heidelberg, Germany, 2013.
[47]P. Mika, T. Tudorache, A. Bernstein, C. Welty, C. A. Knoblock, D. Vran-decic, P. T. Groth, N. F. Noy, K. Janowicz, C. A. Goble (Eds.), The Se-mantic Web - ISWC 2014, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Vol. 8796, Springer, 2014.
[48]M. Arenas, Ó. Corcho, E. Simperl, M. Strohmaier, M. d’Aquin, K. Srini-vas, P. T. Groth, M. Dumontier, J. Heflin, K. Thirunarayan, S. Staab (Eds.), The Semantic Web – ISWC 2015, Springer, Bethlehem, PA, USA, 2015.
[48]P. T. Groth, E. Simperl, A. J. G. Gray, M. Sabou, M. Krötzsch, F. Lécué, F. Flöck, Y. Gil (Eds.), The Semantic Web - ISWC 2016 - 15th Interna-tional Semantic Web Conference, Springer, Kobe, Japan, 2016.
[49]C. d’Amato, M. Fernández, V. A. M. Tamma, F. Lécué, P. Cudré-Mauroux, J. F. Sequeda, C. Lange, J. Heflin (Eds.), The Semantic Web - ISWC 2017 - 16th International Semantic Web Conference, Lecture Notes in Computer Science, Springer, Vienna, Austria, 2017.
[50]E. Blomqvist, D. Maynard, A. Gangemi, R. Hoekstra, P. Hitzler, O. Har-tig (Eds.), The Semantic Web - 14th International Conference, ESWC, Lecture Notes in Computer Science, Portorož, Slovenia, 2017.
[51]H. Sack, E. Blomqvist, M. d’Aquin, C. Ghidini, S. P. Ponzetto, C. Lange (Eds.), The Semantic Web. Latest Advances and New Domains, Vol. 9678 of Lecture Notes in Computer Science, Springer, Berlin Heidel-berg, Germany, 2016.
[52]G. Antoniou, M. Grobelnik, E. Simperl, B. Parsia, D. Plexousakis, P. de Leenheer, J. Z. Pan (Eds.), The Semantic Web: Research and ap-plications, Vol. 6643 of Lecture Notes in Computer Science, Semantic Technology Institute International (STI2), Springer-Verlag, Berlin Hei-delberg, Germany, 2011.
[53]E. Simperl, P. Cimiano, A. Polleres, O. Corcho, V. Presutti (Eds.), The Semantic Web: Research and applications, Vol. 7295 of Lecture Notes in Computer Science, Semantic Technology Institute International (STI2), Springer-Verlag, Berlin Heidelberg, Germany, 2012.
[54]V. Presutti, C. d’Amato, F. Gandon, M. d’Aquin, S. Staab, A. Tordai (Eds.), The Semantic Web: Trends and Challenges, Vol. 8465 of Lecture Notes in Computer Science, Semantic Technology Institute Interna-tional (STI2), Springer International Publishing, Cham, Switzerland, 2014.
[55]F. Gandon, M. Sabou, H. Sack, P., A. Zimmermann (Eds.), The Se-mantic Web. Latest Advances and New Domains, Vol. 9088 of Lecture Notes in Computer Science, Semantic Technology Institute Interna-tional (STI2), Springer International Publishing, Cham, Switzerland, 2015.
[56]L. Aroyo, G. Antoniou, E. Hyvönen, A. ten Teije, H. Stuckenschmidt, A Cabral, T. Tudorache (Eds.), The Semantic Web: Research and Applications, 7th Extended Semantic Web Conference, ESWC 2010, Springer, Heraklion, Crete, Greece, 2010.
[57]A. M. Almasoud, H. S. Al-Khalifa, A proposed semantic machine trans-lation system for translating Arabic text to Arabic sign language, in: Proceedings of the Second Kuwait Conference on e-Services and e-Systems, ACM, 2011, p. 23.
[58]J. P. McCrae, P. Cimiano, Mining translations from the web of open linked data, in: Joint Workshop on NLP&LOD and SWAIE: Semantic Web, Linked Open Data and Information Extraction, 2013, p. 8.
[59]O. Lozynska, M. Davydov, Information technology for Ukrainian Sign Language translation based on ontologies, ECONTECHMOD: an in-ternational quarterly journal on economics of technology and mod-elling processes 4 (2) (2015) 13–18.
[60]K. Simov, P. Osenova, A. Popov, Towards Semantic-based Hybrid Ma-chine Translation between Bulgarian and English, Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
[61]T. Santosh Kumar, Word Sense Disambiguation Using Semantic Web for Tamil to English Statistical Machine Translation, IRA-International Journal of Technology & Engineering 5 (2) (2016) 22–31.
[62]I. A. A.-B. G. H. G. Neama Abdulaziz Bin-Dahan, Fadl Mutaher Ba-Alwi, Towards an Arabic-English Machine-Translation Based on Semantic Web, in: SCITN, Vol. 1, 2016, p. 6.
[63]J. Du, A. Way, A. Zydron, Using babelnet to improve OOV coverage in SMT, in: Proceedings of the Tenth International Conference on Lan-guage Resources and Evaluation LREC 2016, Portorož, Slovenia, May 23-28, 2016., 2016, pp. 9–15.
[64]A. Srivastava, F. Sasaki, P. Bourgonje, J. M. Schneider, J. Nehring, G. Rehm, How to Configure Statistical Machine Translation with Linked Open Data Resources, in: Translating and the Computer 38 - Proceedings, AsLing, 2016, pp. 138–148.
[65]C. Shi, S. Liu, S. Ren, S. Feng, M. Li, M. Zhou, X. Sun, H. Wang, Knowledge-Based Semantic Embedding for Machine Translation, in: Proceedings of the 54th Annual Meeting of the Association for Com-putational Linguistics (Volume 1: Long Papers), Vol. 1, 2016, pp. 2245– 2254.
[66]A. Srivastava, G. Rehm, F. Sasaki., Improving Machine Translation through Linked Data, in: Improving Machine Translation through Linked Data., The Prague Bulletin of Mathematical Linguistics - EAMT, 2017, pp. 355–366.
[67]R. Sennrich, B. Haddow, A. Birch, Neural machine translation of rare words with subword units, in: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pa-pers), Association for Computational Linguistics, 2016, pp. 1715–1725.
[68]M.-T. Luong, C. D. Manning, Achieving open vocabulary neural ma-chine translation with hybrid word-character models, in: Proceedings of the 54th Annual Meeting of the Association for Computational Lin-guistics (Volume 1: Long Papers), Association for Computational Lin-guistics, 2016, pp. 1054–1063.
[69]J. Chung, K. Cho, Y. Bengio, A character-level decoder without explicit segmentation for neural machine translation, in: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, 2016, pp. 1693–1703.
[70]A. Lopez, M. Post, Beyond bitext: Five open problems in machine translation, in: Proceedings of the EMNLP Workshop on Twenty Years of Bitext, 2013, pp. 1–3.
[71]C.-k. Lo, P. Dowling, D. Wu, Improving evaluation and optimization of MT systems against MEANT., in: WMT@ EMNLP, 2015, pp. 434–441.
[72]M. Popovi´c, chrF: character n-gram F-score for automatic MT evalu-ation, in: Proceedings of the Tenth Workshop on Statistical Machine Translation, 2015, pp. 392–395.
[73]M. Popovi´c, chrF deconstructed: beta parameters and n-gram weights, in: Proceedings of the First Conference on Machine Trans-lation: Volume 2, Shared Task Papers, Vol. 2, 2016, pp. 499–504.
[74]A. Zaveri, A. Rula, A. Maurino, R. Pietrobon, J. Lehmann, S. Auer, Qual-ity assessment for linked data: A survey, Semantic Web 7 (1) (2015) 63–93.
[75]J. Bosque-Gil, J. Gracia, G. Aguado-de Cea, E. Montiel-Ponsoda, Ap-plying the OntoLex Model to a Multilingual Terminological Resource, in: The Semantic Web: ESWC 2015 Satellite Events, Springer, 2015, pp. 283–294.
[76]R. Navigli, S. P. Ponzetto, BabelNet: The automatic construction, eval-uation and application of a wide-coverage multilingual semantic net-work, Artificial Intelligence 193 (2012) 217–250.
[77]O. Bojar, R. Chatterjee, C. Federmann, Y. Graham, B. Haddow,Huang, M. Huck, P. Koehn, Q. Liu, V. Logacheva, et al., Findings of the 2017 conference on machine translation (WMT17), in: Proceed-ings of the Second Conference on Machine Translation, 2017, pp. 169– 214.C. Hokamp, Q. Liu, Lexically constrained decoding for sequence gen-eration using grid beam search, in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, 2017, pp. 1535–1546.
[78]RWTH Aachen University. Digital Connected Production. 2017. Available online: https://www.rwth-campus.com/wp-content/uploads/2015/01/Broschuere-Cluster-Productionstechnik-20170508-web.pdf (accessed on 6 September 2018).
[79]Hai, R.; Geisler, S.; Quix, C. Constance: An Intelligent Data Lake System. In Proceedings of the 2016 International Conference on Management of Data, SIGMOD’16, San Francisco, CA, USA, 26 June–1 July 2016; ACM: New York, NY, USA, 2016; pp. 2097–2100.
[80]Pomp, A.; Paulus, A.; Jeschke, S.; Meisen, T. ESKAPE: Platform for Enabling Semantics in the Continuously Evolving Internet of Things. In Proceedings of the 2017 IEEE 11th International Conference on Semantic Computing, ICSC, San Diego, CA, USA, 30 January–1 February 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 262–263.
[81]Pomp, A.; Paulus, A.; Jeschke, S.; Meisen, T. ESKAPE: Information Platform for Enabling Semantic Data Processing. In Proceedings of the 19th International Conference on Enterprise Information. SCITEPRESS— Science and Technology Publications, Porto, Portugal, 26–29 April 2017.
[82]Moghe, P. Time to Analytics: The New Metric for Data Management. In Bloomberg Professional Services2016. Available online: https://www.bloomberg.com/professional/blog/time-to-analytics-the-new-metric-for-data-management/ (accessed on 6 September 2018).
[83]Khan, M.; Wu, X.; Xu, X.; Dou, W. Big data challenges and opportunities in the hype of Industry 4.0. In Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France, 21–25 May 2017; pp. 1–6.
[84]Kirmse, A.; Kraus, V.; Hoffmann, M.; Meisen, T. An Architecture for Efficient Integration and Harmonization of Heterogeneous, Distributed Data Sources Enabling Big Data Analytics. In Proceedings of the 20th International Conference on Enterprise Information Systems, Funchal, Madeira, Portugal, 21–24 March 2018; SCITEPRESS—Science and Technology Publications: Setúbal, Portugal, 2018; Volume 1, pp. 175–182.
[85]Vinoski, S. Advanced Message Queuing Protocol. IEEE Internet Comput. 2006, 10, 87–89. [Cross Ref]
[86]Spring. Available online: https://spring.io (accessed on 6 September 2018).
[87]Technology, I. ISO/IEC 9075 Database languages—SQL; Technical Report; International Organization for Standardization/International Electrotechnical Commission: Geneva, Switzerland, 2008.
[88]Gruber, T. What is an Ontology. WWW Site. 1993. Available online: http://www-ksl.stanford.edu/kst/ whatis-an-ontology.html (accessed on 24 January 2018).
[89]Ehrlinger, L.; Wöß, W. Towards a Definition of Knowledge Graphs. In proceedings of the SEMANTICS 2016, Leipzig, Germany, September 13–14 2016.
[90]The Neo4j Graph Platform. Available online: https://neo4j.com (accessed on 6 September 2018)DB-Engines Ranking. Available online: https://db-engines.com/de/ranking/graph+dbms (accessed on 6 September 2018).
[91]Apache TinkerPop. Available online: https://tinkerpop.apache.org (accessed on 6 September 2018
[92]Paulus, A.; Pomp, A.; Poth, L.; Lipp, J.; Meisen, T. Gathering and Combining Semantic Concepts from Multiple Knowledge Bases. In Proceedings of the 20th International Conference on Enterprise Information, Madeira, Portugal, 21–24 March 2018; SCITEPRESS—Science and Technology Publications: Setúbal, Portugal, 2018.
[93]Measurement, V.S.; GMA. Reference Architecture Model Industrie 4.0 (RAMI4.0). 2016. Available online: https://ec.europa.eu/futurium/en/system/files/ged/a2-schweichhart-reference_architectural_model_industrie_4.0_rami_4.0.pdf (accessed on 6 September 2018).
[94]Group, C.C.E.S.G.C. Smart Grid Reference Architecture. 2011. Available online: http://www.pointview. com/data/files/1/636/2181.pdf (accessed on 6 September 2018).
[95]Society, F. Reference Architecture Model For The Industrial Data Space. 2017. Available online: https://www.fraunhofer.de/content/dam/zv/de/Forschungsfelder/industrial-data-space/Industrial-Data-Space_Reference-Architecture-Model-2017.pdf (accessed on 6 September 2018).
[96]Quix, C.; Hai, R.; Vatov, I. GEMMS: A Generic and Extensible Metadata Management System for Data Lakes. In Proceedings of the CAiSE’16 Forum at the 28th International Conference on Advanced Information Systems Engineering, Ljubljana, Slovenia, 13–17 June 2016.
[97]Microsoft Corporation. Microsoft Data Catalog; Microsoft Corporation: Redmond, WA, USA, 2018.
[98]Informatica Corporation. Enterprise Data Catalog; Informatica Corporation: Redwood City, CA, USA, 2018.
[99]Enterprise Data Lake| Big Data | Data Lake | Informatica US. Available online: https://www.informatica. com/products/big-data/enterprise-data-lake.html (accessed on 6 September 2018).
[100]Knoblock, C.A.; Szekely, P.; Ambite, J.L.; Goel, A.; Gupta, S.; Lerman, K.; Muslea, M.; Taheriyan, M.; Mallick, P. Semi-Automatically Mapping Structured Sources into the Semantic Web. In Proceedings of the Extended Semantic Web Conference, Heraklion, Crete, Greece, 27–31 May 2012.
[101]Cambridge Semantics. Anzo Smart Data Discovery. 2016. Available online: https://www. cambridgesemantics.com (accessed on 6 September 2018).
[102]MAANA. Knowledge Platform. 2018. Available online: https://www.maana.io (accessed on 6 September 2018).
[103]Mantra. Smart Data Platform. 2018. Available online: http://www.altiliagroup.com/platform/mantra-platform (accessed on 6 September 2018).