Skip to main content
SpringerLink
Log in

Review of Approaches for Linked Data Ontology Enrichment

  • Conference paper
  • First Online:
Distributed Computing and Internet Technology (ICDCIT 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10722))

Abstract

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/LinkingOpenData.

  2. 2.

    http://lod-cloud.net/state/state_2014/.

  3. 3.

    http://neilpatel.com/blog/the-beginners-guide-to-the-googles-knowledge-graph/.

  4. 4.

    http://www.obitko.com/tutorials/ontologies-semantic-web/rdf-graph-and-syntax.html.

  5. 5.

    http://wiki.dbpedia.org/downloads-2016-10#dbpedia-ontology.

  6. 6.

    http://wiki.dbpedia.org/datasets/dbpedia-version-2016-10.

  7. 7.

    https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/.

  8. 8.

    http://www.linkedmdb.org/.

  9. 9.

    owl:sameAs is a built-in OWL property which links an individual to another individual denoting that the two resources represent the same real-world entity.

  10. 10.

    The term Knowledge Graph was coined by Google in 2012, referring to their use of semantic knowledge in Web Search. The term is recently being used in a broader sense: any graph-based representation of some knowledge could be considered a knowledge graph.

  11. 11.

    http://dbpedia.org/resource.

  12. 12.

    http://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/statistics/ - totally there are 60 object properties, but 28 of them connect the domain class to the class http://dbpedia.org/class/yago/YagoLiteral.

  13. 13.

    http://rtw.ml.cmu.edu/rtw/.

References

  1. Linked Data - Connect Distributed Data across the Web. http://linkeddata.org/

  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. 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)

    MATH  Google Scholar 

  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. Barati, M., Bai, Q., Liu, Q.: Mining semantic association rules from RDF data. Knowl. Based Syst. 133, 183–196 (2017)

    Article  Google Scholar 

  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 2014

    Google Scholar 

  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. 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. Borgelt, C., Kruse, R.: Induction of association rules: apriori implementation. In: Härdle, W., Rönz, B. (eds.) Compstat, pp. 395–400. Springer, Heidelberg (2002). https://doi.org/10.1007/978-3-642-57489-4_59

    Chapter  Google Scholar 

  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). https://doi.org/10.1007/978-3-319-13704-9_4

    Google Scholar 

  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). https://doi.org/10.1007/978-3-642-41335-3_3

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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. 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. 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). https://doi.org/10.1007/978-3-642-25106-1_20

    Chapter  Google Scholar 

  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). https://doi.org/10.1007/978-3-642-33615-7_18

    Chapter  Google Scholar 

  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. 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. 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. 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. 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 2011

    Google Scholar 

  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. 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. 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. 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). https://doi.org/10.1007/978-3-319-58068-5_10

    Chapter  Google Scholar 

  29. Lehmann, J., Auer, S., Bühmann, L., Tramp, S.: Class expression learning for ontology engineering. J. Web Semant. 9(1), 71–81 (2011)

    Article  Google Scholar 

  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). https://doi.org/10.1007/978-3-642-13840-9_8

    Chapter  Google Scholar 

  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). https://doi.org/10.1007/978-3-540-78469-2_18

    Chapter  Google Scholar 

  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. Li, H., Sima, Q.: Parallel mining of OWL 2 EL ontology from large linked datasets. Knowl. Based Syst. 84, 10–17 (2015)

    Article  Google Scholar 

  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. 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. 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. 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. 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. 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. 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. Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8(3), 489–508 (2017)

    Article  Google Scholar 

  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). https://doi.org/10.1007/978-3-319-49004-5_31

    Chapter  Google Scholar 

  43. Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013)

    Article  Google Scholar 

  44. Subhashree, S., Kumar, P.S.: Enriching linked datasets with new object properties. CoRR abs/1606.07572 (2016). http://arxiv.org/abs/1606.07572

  45. Suchanek, F.M., Abiteboul, S., Senellart, P.: PARIS: probabilistic alignment of relations, instances, and schema. Proc. VLDB Endow. 5(3), 157–168 (2011)

    Article  Google Scholar 

  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. 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). https://doi.org/10.1007/978-3-642-25073-6_45

    Chapter  Google Scholar 

  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. 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. 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). https://doi.org/10.1007/978-3-642-32689-9_25

    Chapter  Google Scholar 

  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). https://doi.org/10.1007/978-3-642-21034-1_9

    Chapter  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology - Madras, Chennai, India

    S. Subhashree, Rajeev Irny & P. Sreenivasa Kumar

Authors
  1. S. Subhashree
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajeev Irny
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. Sreenivasa Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Sreenivasa Kumar .

Editor information

Editors and Affiliations

  1. University of Hyderabad, Hyderabad, India

    Atul Negi

  2. University of Cincinnati, Cincinnati, Ohio, USA

    Raj Bhatnagar

  3. IBM Thomas J. Watson Research Center, Yorktown Heights, New York, USA

    Laxmi Parida

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Subhashree, S., Irny, R., Sreenivasa Kumar, P. (2018). Review of Approaches for Linked Data Ontology Enrichment. In: Negi, A., Bhatnagar, R., Parida, L. (eds) Distributed Computing and Internet Technology. ICDCIT 2018. Lecture Notes in Computer Science(), vol 10722. Springer, Cham. https://doi.org/10.1007/978-3-319-72344-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-72344-0_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72343-3

  • Online ISBN: 978-3-319-72344-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation