Gleaning Types for Literals in RDF Triples with Application to Entity Summarization

  • Kalpa Gunaratna
  • Krishnaprasad Thirunarayan
  • Amit Sheth
  • Gong Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)


Associating meaning with data in a machine-readable format is at the core of the Semantic Web vision, and typing is one such process. Typing (assigning a class selected from schema) information can be attached to URI resources in RDF/S knowledge graphs and datasets to improve quality, reliability, and analysis. There are two types of properties: object properties, and datatype properties. Type information can be made available for object properties as their object values are URIs. Typed object properties allow richer semantic analysis compared to datatype properties, whose object values are literals. In fact, many datatype properties can be analyzed to suggest types selected from a schema similar to object properties, enabling their wider use in applications. In this paper, we propose an approach to glean types for datatype properties by processing their object values. We show the usefulness of generated types by utilizing them to group facts on the basis of their semantics in computing diversified entity summaries by extending a state-of-the-art summarization algorithm.


Type inference Datatype properties RDF triples Feature grouping and ranking Entity summarization Dataset enrichment 



We acknowledge partial support from the National Science Foundation (NSF) award: EAR 1520870: Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response. Any opinions, findings, and conclusions/recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.


  1. 1.
    Cheng, G., Tran, T., Qu, Y.: RELIN: relatedness and informativeness-based centrality for entity summarization. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 114–129. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Collins, M.: Head-driven statistical models for natural language parsing. Comput. Linguist. 29(4), 589–637 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Ell, B., Vrandečić, D., Simperl, E.: Labels in the web of data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 162–176. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Fang, Y., Si, L., Somasundaram, N., Al-Ansari, S., Yu, Z., Xian, Y.: Purdue at TREC 2010 entity track: a probabilistic framework for matching types between candidate and target entities. In: TREC (2010)Google Scholar
  5. 5.
    Gunaratna, K., Lalithsena, S., Sheth, A.: Alignment and dataset identification of linked data in semantic web. Wiley Interdisc. Rev.: Data Min. Knowl. Disc. 4(2), 139–151 (2014)Google Scholar
  6. 6.
    Gunaratna, K., Thirunarayan, K., Sheth, A.: FACES: Diversity-aware entity summarization using incremental hierarchical conceptual clustering. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 116–123. AAAI (2015).
  7. 7.
    Gunaratna, K., Thirunarayan, K., Jain, P., Sheth, A., Wijeratne, S.: A statistical and schema independent approach to identify equivalent properties on linked data. In: Proceedings of the 9th International Conference on Semantic Systems, pp. 33–40. ACM (2013)Google Scholar
  8. 8.
    Hachey, B., Radford, W., Nothman, J., Honnibal, M., Curran, J.R.: Evaluating entity linking with wikipedia. Artif. Intell. 194, 130–150 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Han, L., Kashyap, A., Finin, T., Mayfield, J., Weese, J.: UMBC ebiquity-core: semantic textual similarity systems. In: Proceedings of the Second Joint Conference on Lexical and Computational Semantics, vol. 1, pp. 44–52 (2013)Google Scholar
  10. 10.
    Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., et al.: Dbpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Seman. Web J. 5, 1–29 (2014)Google Scholar
  11. 11.
    Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems, pp. 1–8. ACM (2011)Google Scholar
  12. 12.
    Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1), 3–26 (2007)CrossRefGoogle Scholar
  13. 13.
    Paulheim, H., Bizer, C.: Type inference on noisy RDF data. In: Alani, H., et al. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 510–525. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Sleeman, J., Finin, T.: Type prediction for efficient coreference resolution in heterogeneous semantic graphs. In: 2013 IEEE Seventh International Conference on Semantic Computing (ICSC), pp. 78–85. IEEE (2013)Google Scholar
  15. 15.
    Thalhammer, A., Knuth, M., Sack, H.: Evaluating entity summarization using a game-based ground truth. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part II. LNCS, vol. 7650, pp. 350–361. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Thalhammer, A., Rettinger, A.: Browsing DBpedia entities with summaries. In: Presutti, V., Blomqvist, E., Troncy, R., Sack, H., Papadakis, I., Tordai, A. (eds.) ESWC Satellite Events 2014. LNCS, vol. 8798, pp. 511–515. Springer, Heidelberg (2014)Google Scholar
  17. 17.
    Tonon, A., Catasta, M., Demartini, G., Cudré-Mauroux, P., Aberer, K.: TRank: ranking entity types using the web of data. In: Alani, H., et al. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 640–656. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  18. 18.
    Tylenda, T., Sozio, M., Weikum, G.: Einstein: physicist or vegetarian? summarizing semantic type graphs for knowledge discovery. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 273–276. ACM (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Kalpa Gunaratna
    • 1
  • Krishnaprasad Thirunarayan
    • 1
  • Amit Sheth
    • 1
  • Gong Cheng
    • 2
  1. 1.Kno.e.sisWright State UniversityDaytonUSA
  2. 2.National Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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