Review of Approaches for Linked Data Ontology Enrichment

  • S. Subhashree
  • Rajeev Irny
  • P. Sreenivasa Kumar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10722)


Semantic Web technology has established a framework for creating a “web of data” where the nodes correspond to resources of interest in a domain and the edges correspond to logical statements that link these resources using binary relations of interest in the domain. The framework provides a standardized way of describing a domain of interest so that the description is machine-processable. This enables applications to share data and knowledge about entities in an unambiguous manner. Also, as all resources are represented using IRIs, a massive distributed network of datasets gets created. Applications can dynamically discover these datasets, access most recent data, interpret it using the associated meta-data (ontologies) and integrate them into their operations. While the Linked Open Data (LOD) initiative, based on the Semantic Web standards, has resulted in a huge web corpus of domain datasets, it is well-known that the majority of the statements in a dataset are of the type that link specific individuals to specific individuals (e.g. Paris is the capital of France) and there is major need to augment the datasets with statements that link higher-level entities (e.g. A statement about Countries and Cities such as “Every country has a city as its capital”). Adding statements of this kind is part of the task of enrichment of the LOD datasets called “ontology enrichment”. In this paper, we review various recent research efforts that address this task. We investigate different types of ontology enrichments that are possible and summarize the research efforts in each category. We observe that while the initial rapid growth of LOD was contributed by techniques that converted structured data into the LOD space, the ontology enrichment is more involved and requires several techniques from natural language processing, machine learning and also methods that cleverly make use of the existing ontology statements to obtain new statements.


Linked data Knowledge enrichment LOD enrichment T-Box enrichment Schema enrichment 


  1. 1.
    Linked Data - Connect Distributed Data across the Web.
  2. 2.
    Alex Mathews, K., Sreenivasa Kumar, P.: Extracting ontological knowledge from textual descriptions through grammar-based transformation. In: Proceedings of the Ninth International Conference on Knowledge Capture (K-CAP), 4–6 December, Austin, Texas, USA (2017)Google Scholar
  3. 3.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, New York (2003)zbMATHGoogle Scholar
  4. 4.
    Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, 6–12 January 2007, pp. 2670–2676 (2007)Google Scholar
  5. 5.
    Barati, M., Bai, Q., Liu, Q.: Mining semantic association rules from RDF data. Knowl. Based Syst. 133, 183–196 (2017)CrossRefGoogle Scholar
  6. 6.
    Barchi, P.H., Hruschka, E.R.: Never-ending ontology extension through machine reading. In: 2014 14th International Conference on Hybrid Intelligent Systems, pp. 266–272, December 2014Google Scholar
  7. 7.
    Barchi, P.H., Hruschka, E.R.: Two different approaches to ontology extension through machine reading. J. Netw. Innov. Comput. 3(1), 78–87 (2015)Google Scholar
  8. 8.
    Basse, A., Gandon, F., Mirbel, I., Lo, M.: DFS-based frequent graph pattern extraction to characterize the content of RDF triple stores. In: Web Science Conference 2010 (WebSci 2010) (2010)Google Scholar
  9. 9.
    Borgelt, C., Kruse, R.: Induction of association rules: apriori implementation. In: Härdle, W., Rönz, B. (eds.) Compstat, pp. 395–400. Springer, Heidelberg (2002). CrossRefGoogle Scholar
  10. 10.
    Bühmann, L., Fleischhacker, D., Lehmann, J., Melo, A., Völker, J.: Inductive lexical learning of class expressions. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds.) EKAW 2014. LNCS (LNAI), vol. 8876, pp. 42–53. Springer, Cham (2014). Google Scholar
  11. 11.
    Bühmann, L., Lehmann, J.: Pattern based knowledge base enrichment. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 33–48. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  12. 12.
    Bühmann, L., Lehmann, J., Westphal, P.: DL-Learner - a framework for inductive learning on the semantic web. Web Semant. Sci. Serv. Agents WWW 39, 15–24 (2016)CrossRefGoogle Scholar
  13. 13.
    Cergani, E., Miettinen, P.: Discovering relations using matrix factorization methods. In: 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013, San Francisco, CA, USA, 27 October-1 November, 2013, pp. 1549–1552 (2013)Google Scholar
  14. 14.
    Christensen, J., Mausam, Soderland, S., Etzioni, O.: An analysis of open information extraction based on semantic role labeling. In: Proceedings of the 6th International Conference on Knowledge Capture (K-CAP 2011), 26–29 June, 2011, Banff, Alberta, Canada, pp. 113–120 (2011)Google Scholar
  15. 15.
    Del Corro, L., Gemulla, R.: ClausIE: clause-based open information extraction. In: Proceedings of the 22nd International Conference on World Wide Web, WWW 2013, pp. 355–366 (2013)Google Scholar
  16. 16.
    Dutta, A., Meilicke, C., Stuckenschmidt, H.: Semantifying triples from open information extraction systems. In: STAIRS 2014 - Proceedings of the 7th European Starting AI Researcher Symposium, Prague, Czech Republic, 18–22 August 2014, pp. 111–120 (2014)Google Scholar
  17. 17.
    Etzioni, O., Fader, A., Christensen, J., Soderland, S., Mausam, M.: Open information extraction: the second generation. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - IJCAI 2011, vol. 1, pp. 3–10. AAAI Press (2011)Google Scholar
  18. 18.
    Fleischhacker, D., Völker, J.: Inductive learning of disjointness axioms. In: Meersman, R., et al. (eds.) OTM 2011. LNCS, vol. 7045, pp. 680–697. Springer, Heidelberg (2011). CrossRefGoogle Scholar
  19. 19.
    Fleischhacker, D., Völker, J., Stuckenschmidt, H.: Mining RDF data for property axioms. In: Meersman, R., et al. (eds.) OTM 2012. LNCS, vol. 7566, pp. 718–735. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  20. 20.
    Galárraga, L., Heitz, G., Murphy, K., Suchanek, F.M.: Canonicalizing open knowledge bases. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1679–1688. ACM (2014)Google Scholar
  21. 21.
    Galárraga, L.A., Preda, N., Suchanek, F.M.: Mining rules to align knowledge bases. In: Proceedings of the 2013 Workshop on Automated Knowledge Base Construction, pp. 43–48. ACM (2013)Google Scholar
  22. 22.
    Galárraga, L.A., Teflioudi, C., Hose, K., Suchanek, F.: AMIE: Association rule Mining under Incomplete Evidence in ontological knowledge bases. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 413–422. ACM (2013)Google Scholar
  23. 23.
    Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: 14th International Conference on Computational Linguistics, COLING 1992, Nantes, France, 23–28 August 1992, pp. 539–545 (1992)Google Scholar
  24. 24.
    Iglesias, J., Lehmann, J.: Towards integrating fuzzy logic capabilities into an ontology-based inductive logic programming framework. In: 2011 11th International Conference on Intelligent Systems Design and Applications, pp. 1323–1328, November 2011Google Scholar
  25. 25.
    Irny, R., Kumar, S.P.: Mining inverse and symmetric axioms in Linked Data. In: Proceedings of the Seventh Joint International Semantic Technologies Conference, Gold Coast, Australia, 10–12 November (2017)Google Scholar
  26. 26.
    Kaljurand, K., Fuchs, N.E.: Verbalizing OWL in Attempto Controlled English. In: Proceedings of the OWLED 2007 Workshop on OWL: Experiences and Directions, Innsbruck, Austria, 6–7 June 2007 (2007)Google Scholar
  27. 27.
    Kasneci, G., Elbassuoni, S., Weikum, G.: MING: mining informative entity relationship subgraphs. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1653–1656. ACM (2009)Google Scholar
  28. 28.
    Koutraki, M., Preda, N., Vodislav, D.: Online relation alignment for linked datasets. In: Blomqvist, E., Maynard, D., Gangemi, A., Hoekstra, R., Hitzler, P., Hartig, O. (eds.) ESWC 2017. LNCS, vol. 10249, pp. 152–168. Springer, Cham (2017). CrossRefGoogle Scholar
  29. 29.
    Lehmann, J., Auer, S., Bühmann, L., Tramp, S.: Class expression learning for ontology engineering. J. Web Semant. 9(1), 71–81 (2011)CrossRefGoogle Scholar
  30. 30.
    Lehmann, J., Haase, C.: Ideal downward refinement in the \(\cal{EL}\) description logic. In: De Raedt, L. (ed.) ILP 2009. LNCS (LNAI), vol. 5989, pp. 73–87. Springer, Heidelberg (2010). CrossRefGoogle Scholar
  31. 31.
    Lehmann, J., Hitzler, P.: Foundations of refinement operators for description logics. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 161–174. Springer, Heidelberg (2008). CrossRefGoogle Scholar
  32. 32.
    Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6, 167–195 (2015)Google Scholar
  33. 33.
    Li, H., Sima, Q.: Parallel mining of OWL 2 EL ontology from large linked datasets. Knowl. Based Syst. 84, 10–17 (2015)CrossRefGoogle Scholar
  34. 34.
    Mahdisoltani, F., Biega, J., Suchanek, F.M.: YAGO3: a knowledge base from multilingual Wikipedias. In: CIDR 2015, Seventh Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, 4–7 January 2015, Online Proceedings (2015)Google Scholar
  35. 35.
    Mausam, M.S., Bart, R., Soderland, S., Etzioni, O.: Open language learning for information extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, pp. 523–534 (2012)Google Scholar
  36. 36.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119 (2013)Google Scholar
  37. 37.
    Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Betteridge, J., Carlson, A., Dalvi, B., Gardner, M., Kisiel, B., Krishnamurthy, J., Lao, N., Mazaitis, K., Mohamed, T., Nakashole, N., Platanios, E., Ritter, A., Samadi, M., Settles, B., Wang, R., Wijaya, D., Gupta, A., Chen, X., Saparov, A., Greaves, M., Welling, J.: Never-ending learning. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015) (2015)Google Scholar
  38. 38.
    Mohamed, T.P., Hruschka Jr., E.R., Mitchell, T.M.: Discovering relations between noun categories. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, pp. 1447–1455 (2011)Google Scholar
  39. 39.
    Nimishakavi, M., Saini, U.S., Talukdar, P.P.: Relation schema induction using tensor factorization with side information. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, 1–4 November 2016, pp. 414–423 (2016)Google Scholar
  40. 40.
    Papadakis, G., Ioannou, E., Niederée, C., Fankhauser, P.: Efficient entity resolution for large heterogeneous information spaces. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 535–544. ACM (2011)Google Scholar
  41. 41.
    Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8(3), 489–508 (2017)CrossRefGoogle Scholar
  42. 42.
    Petrucci, G., Ghidini, C., Rospocher, M.: Ontology learning in the deep. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 480–495. Springer, Cham (2016). CrossRefGoogle Scholar
  43. 43.
    Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013)CrossRefGoogle Scholar
  44. 44.
    Subhashree, S., Kumar, P.S.: Enriching linked datasets with new object properties. CoRR abs/1606.07572 (2016).
  45. 45.
    Suchanek, F.M., Abiteboul, S., Senellart, P.: PARIS: probabilistic alignment of relations, instances, and schema. Proc. VLDB Endow. 5(3), 157–168 (2011)CrossRefGoogle Scholar
  46. 46.
    Suchanek, F.M., Sozio, M., Weikum, G.: SOFIE: a self-organizing framework for information extraction. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, New York, pp. 631–640. ACM (2009)Google Scholar
  47. 47.
    Thor, A., Anderson, P., Raschid, L., Navlakha, S., Saha, B., Khuller, S., Zhang, X.-N.: Link prediction for annotation graphs using graph summarization. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 714–729. Springer, Heidelberg (2011). CrossRefGoogle Scholar
  48. 48.
    Tonon, A., Catasta, M., Demartini, G., Cudré-Mauroux, P.: Fixing the domain and range of properties in Linked Data by context disambiguation. In: LDOW@ WWW (2015)Google Scholar
  49. 49.
    Töpper, G., Knuth, M., Sack, H.: DBpedia ontology enrichment for inconsistency detection. In: Proceedings of the 8th International Conference on Semantic Systems, pp. 33–40. ACM (2012)Google Scholar
  50. 50.
    Tran, A.C., Dietrich, J., Guesgen, H.W., Marsland, S.: An approach to parallel class expression learning. In: Bikakis, A., Giurca, A. (eds.) RuleML 2012. LNCS, vol. 7438, pp. 302–316. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  51. 51.
    Völker, J., Niepert, M.: Statistical schema induction. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011. LNCS, vol. 6643, pp. 124–138. Springer, Heidelberg (2011). CrossRefGoogle Scholar
  52. 52.
    Wijaya, D., Talukdar, P.P., Mitchell, T.: PIDGIN: ontology alignment using web text as interlingua. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 589–598. ACM (2013)Google Scholar
  53. 53.
    Wu, F., Weld, D.S.: Open information extraction using Wikipedia. In: ACL 2010, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 11–16 July 2010, Uppsala, Sweden, pp. 118–127 (2010)Google Scholar
  54. 54.
    Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S.: Quality assessment for Linked Data: a survey. Semant. Web 7(1), 63–93 (2016)CrossRefGoogle Scholar
  55. 55.
    Zheng, W., Zou, L., Peng, W., Yan, X., Song, S., Zhao, D.: Semantic SPARQL similarity search over RDF knowledge graphs. Proc. VLDB Endow. 9(11), 840–851 (2016)CrossRefGoogle Scholar
  56. 56.
    Zimmermann, A., Gravier, C., Subercaze, J., Cruzille, Q.: Nell2rdf: read the web, and turn it into RDF. In: Proceedings of the Second International Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data, Montpellier, France, 26 May 2013, pp. 2–8 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • S. Subhashree
    • 1
  • Rajeev Irny
    • 1
  • P. Sreenivasa Kumar
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology - MadrasChennaiIndia

Personalised recommendations