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Semantische Technologien für Enterprise Intelligence am Beispiel von Lieferkettenbeobachtung

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Semantische Datenintelligenz im Einsatz

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Zusammenfassung

Enterprise Intelligence, d. h. die Überwachung und Interpretation aller Signale der verschiedenen Akteure eines Marktes, wird in einer globalen Wirtschaft mit ihren weltweit verteilten Lieferanten, Kunden und Wettbewerbern sowie der zunehmenden Komplexität von Produkten, Herstellungsprozessen und Regularien zu einem immer entscheidenderen Erfolgsfaktor für Unternehmen. Technologische Fortschritte in den Bereichen künstliche Intelligenz, Big-Data-Management und Webtechnologie ermöglichen aber den Einsatz modernster Informationstechnologien zur Automatisierung der arbeitsintensivsten Prozesse für Enterprise-Intelligence-Lösungen. Wir werden in diesem Kapitel eine KI-basierte Serviceplattform für Enterprise Intelligence beschreiben, die Ergebnisse aus der deutschen und chinesischen KI-Forschung und Softwareentwicklung kombiniert. Ihre Kernkomponenten sind ein Framework für multilinguale semantische Sprachverarbeitung, ein Framework für die Erstellung, Nutzung und Erweiterung von Wissensgraphen sowie die Einbettung dieser Komponenten in einer leistungsstarken Big-Data-Analytik-Plattform.

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Notes

  1. 1.

    https://kafka.apache.org/.

  2. 2.

    https://parquet.apache.org/.

  3. 3.

    http://www.icst.pku.edu.cn/lcwm/pkunlp/.

  4. 4.

    https://www.ims.uni-stuttgart.de/forschung/ressourcen/werkzeuge/matetools/.

  5. 5.

    http://avro.apache.org/.

  6. 6.

    https://www.w3.org/TR/vocab-org/.

  7. 7.

    http://wiki.dbpedia.org/services-resources/ontology.

  8. 8.

    http://permid.org.

  9. 9.

    https://grid.ac.

  10. 10.

    http://geonames.org.

  11. 11.

    https://github.com/dbpedia/links.

  12. 12.

    flink.apache.org.

  13. 13.

    GIANCE wurde Ende 2018 aus dem DFKI Berlin ausgegründet, um KI-Forschungsresultate, insbesondere neue Sprach- und Wissenstechnologien, in kommerzielle Anwendungen zu überführen. Schwerpunkt der Anwendungen war Corporate/Enterprise Intelligence.

Literatur

  1. Smart Data – Innovationen aus Daten (2017). https://www.bmwi.de/Redaktion/DE/Publikationen/Digitale-Welt/smart-data-innovationen-aus-daten.html. Abgerufen am 6.3.2020.

  2. Lee, K. (2018) AI Superpowers: China, Silicon Valley and the New World Order. Houghton Mifflin Harcourt

    Google Scholar 

  3. Bengio, Y., LeCun, Y., Hinton, G. (2015). "Deep Learning". Nature. 521 (7553): 436–444.

    Google Scholar 

  4. Ciresan, D., Meier, U., Schmidhuber, J. (2012). Multi-column deep neural networks for image classification. 2012 IEEE Conference on Computer Vision and Pattern Recognition. pp. 3642–3649.

    Google Scholar 

  5. Google’s AlphaGo AI wins three-match series against the world’s best Go player. TechCrunch. 25 May 2017.

    Google Scholar 

  6. Krizhevsky, A., Sutskever, I., Hinton, Geoffry (2012). ImageNet Classification with Deep Convolutional Neural Networks. NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada.

    Google Scholar 

  7. Schmidhuber, J. (2015). Deep Learning in Neural Networks: An Overview. Neural Networks. 61: 85–117.

    Google Scholar 

  8. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., & Ives, Z.G. (2007). DBpedia: A Nucleus for a Web of Open Data. ISWC/ASWC.

    Google Scholar 

  9. Bollacker, K.D., Evans, C.J., Paritosh, P., Sturge, T., & Taylor, J. (2008). Freebase: a collaboratively created graph database for structuring human knowledge. SIGMOD Conference.

    Google Scholar 

  10. Perez, S. (2012). Wikipedia’s Next Big Thing: Wikidata, A Machine-Readable, User-Editable Database Funded By Google, Paul Allen And Others. TechCrunch

    Google Scholar 

  11. Alexandrov, A., Bergmann, R., Ewen, S., Freytag, J., Hueske, F., Heise, A., Kao, O., Leich, M., Leser, U., Markl, V., Naumann, F., Peters, M., Rheinländer, A., Sax, M., Schelter, S., Höger, M., Tzoumas, K., & Warneke, D. (2014). The Stratosphere platform for big data analytics. The VLDB Journal, 23, 939–964.

    Google Scholar 

  12. Shvachko, K., Kuang, H., Radia, S., & Chansler, R. (2010). The Hadoop Distributed File System. 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), 1–10.

    Google Scholar 

  13. Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., & Wang, P. (2019). K-BERT: Enabling Language Representation with Knowledge Graph. ArXiv, abs/1909.07606.

    Google Scholar 

  14. Peters, M.E., Neumann, M., RobertLLogan, I., Schwartz, R., Joshi, V., Singh, S., & Smith, N.A. (2019). Knowledge Enhanced Contextual Word Representations. EMNLP/IJCNLP.

    Google Scholar 

  15. Zhang, Z., Han, X., Liu, Z., Jiang, X., Sun, M., & Liu, Q. (2019). ERNIE: Enhanced Language Representation with Informative Entities. ACL.

    Google Scholar 

  16. Quinn, J.B. (1992). Intelligent Enterprise: A Knowledge and Service Based Paradigm for Industry. Free Press

    Google Scholar 

  17. Business Intelligence, https://en.wikipedia.org/wiki/Business_intelligence (2020). Wikipedia. Abgerufen am 6.3.2020.

  18. CIO.com (2019). What is business intelligence? Transforming data into business insights. https://www.cio.com/article/2439504/business-intelligence-definition-and-solutions.html. Abgerufen am 6.3.2020.

  19. Meier A. (2018) Was heißt Big Data? In: Werkzeuge der digitalen Wirtschaft: Big Data, NoSQL & Co. essentials. Springer Vieweg, Wiesbaden

    Google Scholar 

  20. Hennig, L., Thomas, P., Ai, R., Kirschnick, J., Wang, H., Pannier, J., Zimmermann, N., Schmeier, S., Xu, F., Ostwald, J., & Uszkoreit, H. (2016). Real-Time Discovery and Geospatial Visualization of Mobility and Industry Events from Large-Scale, Heterogeneous Data Streams. ACL.

    Google Scholar 

  21. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., & McClosky, D. (2014). The Stanford CoreNLP Natural Language Processing Toolkit. ACL.

    Google Scholar 

  22. Marneffe, M.D., Dozat, T., Silveira, N., Haverinen, K., Ginter, F., Nivre, J., & Manning, C.D. (2014). Universal Stanford dependencies: A cross-linguistic typology. LREC.

    Google Scholar 

  23. Strötgen, J., & Gertz, M. (2010). HeidelTime: High Quality Rule-Based Extraction and Normalization of Temporal Expressions. SemEval@ACL.

    Google Scholar 

  24. Ferrucci, D.A., & Lally, A. (2004). UIMA: an architectural approach to unstructured information processing in the corporate research environment. Natural Language Engineering, 10, 327–348.

    Google Scholar 

  25. Eckart de Castilho, R. and Gurevych, I. (2014). A broad-coverage collection of portable NLP components for building shareable analysis pipelines. In Proceedings of the Workshop on Open Infrastructures and Analysis Frameworks for HLT (OIAF4HLT) at COLING 2014, p 1–11, Dublin, Ireland.

    Google Scholar 

  26. Akbik, A., Blythe, D., & Vollgraf, R. (2018). Contextual String Embeddings for Sequence Labeling. COLING.

    Google Scholar 

  27. Ma, X., & Hovy, E.H. (2016). End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF. ACL.

    Google Scholar 

  28. Drozdzynski, W., Krieger, H., Piskorski, J., Schäfer, U., & Xu, F. (2004). Shallow Processing with Unification and Typed Feature Structures – Foundations and Applications. KI, 18.

    Google Scholar 

  29. Bunescu, R.C., & Pasca, M. (2006). Using Encyclopedic Knowledge for Named entity Disambiguation. EACL.

    Google Scholar 

  30. Dredze, M., McNamee, P., Rao, D., Gerber, A., & Finin, T.W. (2010). Entity Disambiguation for Knowledge Base Population. COLING.

    Google Scholar 

  31. Thomas, P., & Hennig, L. (2017). Twitter Geolocation Prediction Using Neural Networks. GSCL.

    Google Scholar 

  32. Thomas, P., Kirschnick, J., Hennig, L., Ai, R., Schmeier, S., Hemsen, H., Xu, F., & Uszkoreit, H. (2017). Streaming Text Analytics for Real-Time Event Recognition. RANLP.

    Google Scholar 

  33. Chen, Z. (2019). Neural Entity Linking For Company Names. Msc. Thesis, TU Berlin.

    Google Scholar 

  34. Zelenko, D., Aone, C., & Richardella, A. (2002). Kernel Methods for Relation Extraction. J. Mach. Learn. Res., 3, 1083–1106.

    Google Scholar 

  35. Alt, C., Hübner, M., & Hennig, L. (2019). Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction. ACL.

    Google Scholar 

  36. Alt, C., Hübner, M., & Hennig, L. (2019). Improving Relation Extraction by Pre-trained Language Representations. AKBC.

    Google Scholar 

  37. Soares, L.B., FitzGerald, N., Ling, J., & Kwiatkowski, T. (2019). Matching the Blanks: Distributional Similarity for Relation Learning. ACL.

    Google Scholar 

  38. Surdeanu, M., Tibshirani, J., Nallapati, R., & Manning, C.D. (2012). Multi-instance Multi-label Learning for Relation Extraction. EMNLP-CoNLL.

    Google Scholar 

  39. Zeng, D., Liu, K., Chen, Y., & Zhao, J. (2015). Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks. EMNLP.

    Google Scholar 

  40. Surdeanu, M., & Ji, H. (2014). Overview of the English Slot Filling Track at the TAC 2014 Knowledge Base Population Evaluation.

    Google Scholar 

  41. Zhang, Y., Zhong, V., Chen, D., Angeli, G., & Manning, C.D. (2017). Position-aware Attention and Supervised Data Improve Slot Filling. EMNLP.

    Google Scholar 

  42. Li, H.X., Krause, S., Xu, F., Moro, A., Uszkoreit, H., & Navigli, R. (2015). Improvement of n-ary Relation Extraction by Adding Lexical Semantics to Distant-Supervision Rule Learning. ICAART.

    Google Scholar 

  43. Xu, F., Uszkoreit, H., & Li, H.X. (2007). A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity. ACL.

    Google Scholar 

  44. Mintz, M., Bills, S., Snow, R., & Jurafsky, D. (2009). Distant supervision for relation extraction without labeled data. ACL/IJCNLP.

    Google Scholar 

  45. Gabrilovich, E., Ringgaard, M. & Subramanya, A. (2013). FACC1: Freebase annotation of ClueWeb corpora, Version 1 (Release date 2013-06-26, Format version 1, Correction level 0), http://lemurproject.org/clueweb12/.

  46. Krause, S., Li, H.X., Uszkoreit, H., & Xu, F. (2012). Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web. International Semantic Web Conference.

    Google Scholar 

  47. Lin, Y., Shen, S., Liu, Z., Luan, H., & Sun, M. (2016). Neural Relation Extraction with Selective Attention over Instances. ACL.

    Google Scholar 

  48. Zeng, D., Liu, K., Lai, S., Zhou, G., & Zhao, J. (2014). Relation Classification via Convolutional Deep Neural Network. COLING.

    Google Scholar 

  49. Krause, S., Xu, F., Uszkoreit, H., & Weissenborn, D. (2016). Event Linking with Sentential Features from Convolutional Neural Networks. CoNLL.

    Google Scholar 

  50. Hellmann, S., Lehmann, J., Auer, S., & Brümmer, M. (2013). Integrating NLP Using Linked Data. International Semantic Web Conference.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Deutsches Forschungszentrum für Künstliche Intelligenz, Berlin, Deutschland

    Leonhard Hennig

  2. GIANCE Technologies GmbH / Deutsches Forschungszentrum für Künstliche Intelligenz, Berlin, Deutschland

    Hans Uszkoreit

Authors
  1. Leonhard Hennig
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  2. Hans Uszkoreit
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Correspondence to Leonhard Hennig .

Editor information

Editors and Affiliations

  1. AG Corporate Semantic Web, Freie Universität Berlin, Berlin, Deutschland

    Börteçin Ege

  2. Data Analytics Center, Fraunhofer FOKUS, Berlin, Deutschland

    Adrian Paschke

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Hennig, L., Uszkoreit, H. (2021). Semantische Technologien für Enterprise Intelligence am Beispiel von Lieferkettenbeobachtung. In: Ege, B., Paschke, A. (eds) Semantische Datenintelligenz im Einsatz. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-31938-0_6

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