Skip to main content
Springer Nature Link
Log in

An Integrated Ontology-Based Approach for Patent Classification in Medical Engineering

  • Conference paper
  • First Online:
Data Integration in the Life Sciences (DILS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10649))

Included in the following conference series:

Abstract

Medical engineering (ME) is an interdisciplinary domain with short innovation cycles. Usually, researchers from several fields cooperate in ME research projects. To support the identification of suitable partners for a project, we present an integrated approach for patent classification combining ideas from topic modeling, ontology modeling & matching, bibliometric analysis, and data integration. First evaluation results show that the use of semantic technologies in patent classification can indeed increase the quality of the results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://www.dbis.rwth-aachen.de/mi-Mappa.

  2. 2.

    http://webofknowledge.com.

  3. 3.

    https://europepmc.org.

  4. 4.

    https://www.openaire.eu.

  5. 5.

    http://www.elsevier.com/solutions/scopus.

  6. 6.

    https://ncit.nci.nih.gov/ncitbrowser.

  7. 7.

    http://www.snomed.org/snomed-ct.

  8. 8.

    https://www.nlm.nih.gov/mesh.

  9. 9.

    https://code.google.com/archive/p/pharmgkb-owl.

  10. 10.

    http://bioportal.bioontology.org.

References

  1. Awasthi, A., Adetiloye, T., Crainic, T.G.: Collaboration partner selection for city logistics planning under municipal freight regulations. Appl. Math. Model. 40(1), 510–525 (2016)

    Article  MathSciNet  Google Scholar 

  2. Balog, K., De Rijke, M.: Determining expert profiles (with an application to expert finding). IJCAI 7, 2657–2662 (2007)

    Google Scholar 

  3. Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)

    Article  Google Scholar 

  4. Blei, D.M., Lafferty, J.D.: A correlated topic model of science. Ann. Appl. Stat. 1(1), 17–35 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  5. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  6. Bonino, D., Ciaramella, A., Corno, F.: Review of the state-of-the-art in patent information and forthcoming evolutions in intelligent patent informatics. World Patent Inf. 32(1), 30–38 (2010)

    Article  Google Scholar 

  7. BVMed. Branchenbericht Medizintechnologien, June 2015. www.bvmed.de/branchenbericht

  8. Cruz, I.F., Antonelli, F.P., Stroe, C.: Agreementmaker: efficient matching for large real-world schemas and ontologies. PVLDB 2(2), 1586–1589 (2009)

    Google Scholar 

  9. Deutsche Gesellschaft für Biomed. Technik im VDE. Empfehlungen zur Verbesserung der Innovationsrahmenbedingungen für Hochtechnologie-Medizin. Technical report, VDE (2012)

    Google Scholar 

  10. Faria, D., Pesquita, C., Santos, E., Cruz, I.F., Couto, F.M.: Agreement maker light results for oaei 2013. In: Proceedings of 8th International Conference on Ontology Matching, pp. 101–108. CEUR-WS.org (2013)

  11. Geisler, S., Hai, R., Quix, C.: An ontology-based collaboration recommender system using patents. In: Proceedings of International Conference on Knowledge Engineering and Ontology Development, pp. 389–394 (2015)

    Google Scholar 

  12. Hai, R., Geisler, S., Quix, C.: Constance: an intelligent data lake system. In: Proceedings SIGMOD, pp. 2097–2100, San Francisco (2016)

    Google Scholar 

  13. Hornik, K., Grün, B.: Topicmodels: an r package for fitting topic models. J. Stat. Softw. 40(13), 1–30 (2011)

    Google Scholar 

  14. Meyer, D., Hornik, K., Feinerer, I.: Text mining infrastructure in r. J. Stat. Softw. 25(5), 1–54 (2008)

    Google Scholar 

  15. Mogee, M.E., Kolar, R.G.: Patent co-citation analysis of eli lilly & co. patents. Expert Opin. Ther. Pat. 9(3), 291–305 (1999)

    Article  Google Scholar 

  16. Portenoy, J., West, J.D.: Visualizing scholarly publications and citations to enhance author profiles. In: Proceedings of 26th International Conference on World Wide Web (WWW), pp. 1279–1282, Perth (2017)

    Google Scholar 

  17. Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)

    Article  Google Scholar 

  18. Rafiei, M., Kardan, A.A.: A novel method for expert finding in online communities based on concept map and pagerank. Hum. Centric Comput. Inf. Sci. 5(1), 1–18 (2015)

    Article  Google Scholar 

  19. Rani, S.K., Raju, K.V.S.V.N., Kumari, V.V.: Expert finding system using latent effort ranking in academic social networks. Int. J. Inf. Technol. Comput. Sci. 2, 21–27 (2015)

    Google Scholar 

  20. Schlötelburg, C., Weiß, C., Hahn, P., Becks, T., Mühlbacher, A.C.: Identifizierung von Innovationshürden in der Medizintechnik. Technical report, Bundesministeriums für Bildung und Forschung, October 2008

    Google Scholar 

  21. Steyvers, M., Griffiths, T.: Probabilistic topic models. In: Landauer, T.K., McNamara, D.S., Dennis, S., Kintsch, W. (eds.) Handbook of Latent Semantic Analysis, vol. 427, pp. 424–440. Lawrence Erlbaum Associates Inc. (2007)

    Google Scholar 

  22. Mari Carmen Suárez-Figueroa. NeOn Methodology for building ontology networks: specification, scheduling and reuse. PhD thesis, Univ. Politecnica de Madrid (2010)

    Google Scholar 

  23. Tang, J., Wang, B., Yang, Y., Hu, P., Zhao, Y., Yan, X., Gao, B., Huang, M., Xu, P., Li, W., et al.: Patentminer: topic-driven patent analysis and mining. In: Proceedings 18th ACM SIGKDD, pp. 1366–1374. ACM (2012)

    Google Scholar 

  24. Trappey, A.J.C., Trappey, C.V., Hsu, F.C., Hsiao, D.W.: A fuzzy ontological knowledge document clustering methodology. IEEE Trans. Syst. Man Cybern. Part B 39(3), 806–814 (2009)

    Article  Google Scholar 

  25. Tseng, Y.-H., Lin, C.-J., Lin, Y.-I.: Text mining techniques for patent analysis. Inf. Proces. Manag. 43(5), 1216–1247 (2007)

    Article  Google Scholar 

  26. Wang, G.A., Jiao, J., Abrahams, A.S., Fan, W., Zhang, Z.: Expertrank: a topic-aware expert finding algorithm for online knowledge communities. Decis. Support Syst. 54(3), 1442–1451 (2013)

    Article  Google Scholar 

  27. Wanner, L., et al.: Towards content-oriented patent document processing. World Patent Inf. 30(1), 21–33 (2008)

    Article  Google Scholar 

  28. Chong, W., Barnes, D.: A literature review of decision-making models and approaches for partner selection in agile supply chains. Purchasing Supply Manag. 17(4), 256–274 (2011)

    Article  Google Scholar 

  29. Yimam-Seid, D., Kobsa, A.: Expert-finding systems for organizations: problem and domain analysis and the demoir approach. J. Organ. Comput. Electron. Commer. 13(1), 1–24 (2003)

    Google Scholar 

  30. Yoon, B., Park, Y.: A text-mining-based patent network: analytical tool for high-technology trend. J. High Technol. Manag. Res. 15(1), 37–50 (2004)

    Article  Google Scholar 

  31. Zhang, L., Li, L., Li, T.: Patent mining: a survey. ACM SIGKDD Explor. Newslett. 16(2), 1–19 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

This work has been supported by the Klaus Tschira Stiftung gGmbH in the context of the mi-Mappa project (http://www.dbis.rwth-aachen.de/mi-Mappa/, project no. 00.263.2015). We thank our project partners from the Institute of Applied Medical Engineering at the Helmholtz Institute of RWTH Aachen University & Hospital, especially Dr. Robert Farkas, for the fruitful discussions about the approach and for providing the patent data.

Author information

Authors and Affiliations

  1. Information Systems, RWTH Aachen University, Aachen, Germany

    Christoph Quix, Rihan Hai & Sanchit Alekh

  2. Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany

    Sandra Geisler & Christoph Quix

Authors
  1. Sandra Geisler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christoph Quix
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rihan Hai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sanchit Alekh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sandra Geisler .

Editor information

Editors and Affiliations

  1. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Marcos Da Silveira

  2. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Cédric Pruski

  3. Université du Luxembourg, Esch-sur-Alzette, Luxembourg

    Reinhard Schneider

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Geisler, S., Quix, C., Hai, R., Alekh, S. (2017). An Integrated Ontology-Based Approach for Patent Classification in Medical Engineering. In: Da Silveira, M., Pruski, C., Schneider, R. (eds) Data Integration in the Life Sciences. DILS 2017. Lecture Notes in Computer Science(), vol 10649. Springer, Cham. https://doi.org/10.1007/978-3-319-69751-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-69751-2_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69750-5

  • Online ISBN: 978-3-319-69751-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics