Knowledge Graph: Semantic Representation and Assessment of Innovation Ecosystems

  • Klaus UlmschneiderEmail author
  • Birte Glimm
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 786)


Innovative capacity is highly dependent upon knowledge and the possession of unique competences can be an important source of enduring strategic advantage. Hence, being able to identify, locate, measure, and assess competence occupants can be a decisive competitive edge. In this work, we introduce a framework that assists with performing such tasks. To achieve this, NLP-, rule-based, and machine learning techniques are employed to process raw data such as academic publications or patents. The framework gains normalized person and organization profiles and compiles identified entities (such as persons, organizations, or locations) into dedicated objects disambiguating and unifying where needed. The objects are then mapped with conceptual systems and stored along with identified semantic relations in a Knowledge Graph, which is constituted by RDF triples. An OWL reasoner allows for answering complex business queries, and in particular, to analyze and evaluate competences on multiple aggregation levels (i.e., single vs. collective) and dimensions (e.g., region, technological field of interest, time). In order to prove the general applicability of the framework and to illustrate how to solve concrete business cases from the automotive domain, it is evaluated with different datasets.


Competence analysis Competence detection Competence assessment Computational linguistics Corporate strategy Data mining Decision making Expert matching Expert mining Information extraction Information retrieval Innovation ecosystem Knowledge graph Knowledge representation Machine learning Name normalization Name disambiguation Natural language processing Ontology Patent analysis Question-answering Reasoning Semantic technologies Semantic analysis 


  1. 1.
    Aswani, N., Bontcheva, K., Cunningham, H.: Mining information for instance unification. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 329–342. Springer, Heidelberg (2006). doi: 10.1007/11926078_24 CrossRefGoogle Scholar
  2. 2.
    Barirani, A., Agard, B., Beaudry, C.: Competence maps using agglomerative hierarchical clustering. J. Intell. Manufact. 24(2), 373–384 (2013). doi: 10.1007/s10845-011-0600-y CrossRefGoogle Scholar
  3. 3.
    Ernst, H.: Patent information for strategic technology management. World Pat. Inf. 25(3), 233–242 (2003). doi: 10.1016/S0172-2190(03)00077-2 CrossRefGoogle Scholar
  4. 4.
    Giereth, M., Stäbler, A., Brügmann, S., Rotard, M., Ertl, T.: Application of semantic technologies for representing patent metadata. In: Informatik 2006, vol. 1, pp. 297–304 (2006)Google Scholar
  5. 5.
    Huang, S., Yang, B., Yan, S., Rousseau, R.: Institution name disambiguation for research assessment. Scientometrics 99(3), 823–838 (2014). doi: 10.1007/s11192-013-1214-2 CrossRefGoogle Scholar
  6. 6.
    Jacob, F., Javed, F., Zhao, M., Mcnair, M.: sCooL: a system for academic institution name normalization. In: Proceedings of 15th International Conference on Collaboration Technologies and Systems (CTS 2014), pp. 86–93 (2014). doi: 10.1109/CTS.2014.6867547
  7. 7.
    Jijkoun, V., Khalid, M.A., Marx, M., de Rijke, M.: Named entity normalization in user generated content. In: Proceedings of 2nd Workshop on Analytics for Noisy Unstructured Text Data (AND 2008), pp. 23–30 (2008). doi: 10.1145/1390749.1390755
  8. 8.
    Jonnalagadda, S., Topham, P.: NEMO: extraction and normalization of organization names from PubMed affiliation strings. J. Biomed. Discov. Collab. 5, 50–75 (2010)Google Scholar
  9. 9.
    Khalid, M.A., Jijkoun, V., de Rijke, M.: The impact of named entity normalization on information retrieval for question answering. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 705–710. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-78646-7_83 CrossRefGoogle Scholar
  10. 10.
    Liu, Q., Javed, F., Mcnair, M.: CompanyDepot: employer name normalization in the online recruitment industry. In: Proceedings of 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016), pp. 521–530 (2016). doi: 10.1145/2939672.2939727
  11. 11.
    Moehrle, M.G., Walter, L., Geritz, A., Müller, S.: Patent-based inventor profiles as a basis for human resource decisions in research and development. R&D Manag. 35(5), 513–524 (2005). doi: 10.1111/j.1467-9310.2005.00408.x CrossRefGoogle Scholar
  12. 12.
    Ronda-Pupo, G.A., Guerras-Martín, L.Á.: Collaboration network of knowledge creation and dissemination on management research: ranking the leading institutions. Scientometrics 107(3), 917–939 (2016). doi: 10.1007/s11192-016-1924-3 CrossRefGoogle Scholar
  13. 13.
    Tseng, Y.H., Lin, C.J., Lin, Y.I.: Text mining techniques for patent analysis. Inf. Process. Manag. 43(5), 1216–1247 (2007). doi: 10.1016/j.ipm.2006.11.011 CrossRefGoogle Scholar
  14. 14.
    Ulmschneider, K., Glimm, B.: Semantic exploitation of implicit patent information. In: Proceedings of 7th IEEE Symposium Series on Computational Intelligence (SSCI 2016) (2016). doi: 10.1109/SSCI.2016.7849943
  15. 15.
    Zhang, L., Li, L., Li, T.: Patent mining: a survey. SIGKDD Explor. 16(2), 1–19 (2014)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Zhao, M., Javed, F., Jacob, F., Mcnair, M.: SKILL: a system for skill identification and normalization. In: Proceedings of 29th Conference on Innovative Applications of Artificial Intelligence (AAAI 2015), pp. 4012–4017 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Artificial IntelligenceUlm UniversityUlmGermany

Personalised recommendations