Advertisement

Understanding Author Intentions: Test Driven Knowledge Graph Construction

  • Jeff Z. PanEmail author
  • Nico Matentzoglu
  • Caroline Jay
  • Markel Vigo
  • Yuting Zhao
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9885)

Abstract

This chapter presents some state of the arts techniques on understanding authors’ intentions during the knowledge graph construction process. In addition, we provide the reader with an overview of the book, as well as a brief introduction of the history and the concept of Knowledge Graph.

We will introduce the notions of explicit author intention and implicit author intention, discuss some approaches for understanding each type of author intentions and show how such understanding can be used in reasoning-based test-driven knowledge graph construction and can help design guidelines for bulk editing, efficient reasoning and increased situational awareness. We will discuss extensively the implications of test driven knowledge graph construction to ontology reasoning.

Keywords

Resource Description Framework Description Logic Semantic Network Conjunctive Query Knowledge Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research has been partially funded by the EPSRC WhatIf project (EP/J014176/1) and the EU Marie Curie IAPP K-Drive project (286348). In particular, we would like to thank our colleagues Yuan Ren, Artemis Parvizi, Chris Mellish and Kees van Deemter from the University of Aberdeen and Robert Stevens from the University of Manchester for their joint work on ontology authoring.

References

  1. 1.
    Pan, J.Z., Vetere, G., Gomez-Perez, J.M., Wu, H.: Exploiting Linked Data and Knowledge Graphs for Large Organisations. Springer, Heidelberg (2016)Google Scholar
  2. 2.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.: The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, Cambridge (2003). ISBN: 0-521-78176-0zbMATHGoogle Scholar
  3. 3.
    Stearns, M.Q., Price, C., Spackman, K.A., Wang, A.Y.: SNOMED clinical terms: overview of the development process and project status. In: Proceedings of the AMIA Symposium, p. 662. American Medical Informatics Association (2001)Google Scholar
  4. 4.
    Rector, A., Drummond, N., Horridge, M., Rogers, J., Knublauch, H., Stevens, R., Wang, H., Wroe, C.: OWL pizzas: practical experience of teaching OWL-DL: common errors & common patterns. In: Motta, E., Shadbolt, N.R., Stutt, A., Gibbins, N. (eds.) EKAW 2004. LNCS, vol. 3257, pp. 63–81. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Dzbor, M., Motta, E., Gomez, J.M., Buil, C., Dellschaft, K., Görlitz, O., Lewen, H.: D4.1.1 analysis of user needs, behaviours & requirements wrt user interfaces for ontology engineering. Technical report, August 2006Google Scholar
  6. 6.
    Brachman, R.J., Levesque, H.J. (eds.): Readings in Knowledge Representation. Morgan Kaufmann Publishers Inc., San Francisco (1985). ISBN: 093461301XzbMATHGoogle Scholar
  7. 7.
    Sowa, J.F.: Semantic networks. In: Encyclopedia of Artificial Intelligence. Wiley, New York (1987)Google Scholar
  8. 8.
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Upper Saddle River (2010). ISBN: 978-0-13-604259-4zbMATHGoogle Scholar
  9. 9.
    Quillian, M.R.: Word concepts: a theory and simulation of some basic semantic capabilities. Behav. Sci. 12(5), 410–430 (1967)CrossRefGoogle Scholar
  10. 10.
    Minsky, M.: A framework for representing knowledge. In: MIT-AI Laboratory Memo 306 (1974). Reprinted in the Winston, P. (ed.) Psychology of Computer Vision. McGraw-Hill (1975)Google Scholar
  11. 11.
    Hayes, P.J.: The logic of frames. In: Metzing, D. (ed.) Frame Conceptions and Text Understanding, pp. 46–61. Walter de Gruyter and Co. (1979)Google Scholar
  12. 12.
    Brachman, R.J., Schmolze, J.G.: An overview of the KL-ONE knowledge representation system. Cogn. Sci. 9(2), 171 (1985)CrossRefGoogle Scholar
  13. 13.
    Hayes, P.J., Patel-Schneider, P.F.: RDF 1.1 semantics. W3C Recommendation, February 2014Google Scholar
  14. 14.
    Pan, J., Horrocks, I.: OWL-Eu: adding customised datatypes into OWL. J. Web Semant. 4(1), 29–39 (2006)CrossRefGoogle Scholar
  15. 15.
    Suchanek, F., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the WWW (2007)Google Scholar
  16. 16.
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: Dbpedia-a crystallization point for the web of data. J. Web Semant. 7(3), 154–165 (2009)CrossRefGoogle Scholar
  17. 17.
    Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E., Mitchell, T.: Toward an architecture for never-ending language learning. In: Proceedings of the AAAI (2010)Google Scholar
  18. 18.
    Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Tractable reasoning and efficient query answering in description logics: the DL-Lite family. J. Autom. Reason. 39(3), 385–429 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Baader, F., Brandt, S., Lutz, C.: Pushing the EL envelope further. In: Clark, K., Patel-Schneider, P.F. (eds.) Proceedings of the OWLED 2008 DC Workshop on OWL: Experiences and Directions (2008)Google Scholar
  20. 20.
    Pan, J.Z., Horrocks, I.: RDFS(FA) and RDF MT: two semantics for RDFS. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 30–46. Springer, Heidelberg (2003). doi: 10.1007/978-3-540-39718-2_3 CrossRefGoogle Scholar
  21. 21.
    Pan, J.Z., Thomas, E.: Approximating OWL-DL ontologies. In: The Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI 2007), pp. 1434–1439 (2007)Google Scholar
  22. 22.
    Pan, J.Z., Thomas, E., Zhao, Y.: Completeness guaranteed approximation for OWL DL query answering. In: Proceedings of the DL (2009)Google Scholar
  23. 23.
    Ren, Y., Pan, J.Z., Zhao, Y.: Towards scalable reasoning on ontology streams via syntactic approximation. In: The Proceedings of IWOD2010 (2010)Google Scholar
  24. 24.
    Console, M., Mora, J., Rosati, R., Santarelli, V., Savo, D.F.: Effective computation of maximal sound approximations of description logic ontologies. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8797, pp. 164–179. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-11915-1_11 Google Scholar
  25. 25.
    Zhou, Y., Nenov, Y., Grau, B., Horrocks, I.: Pay-as-you-go OWL query answering using a triple store. In: Proceedings of the AAAI (2014)Google Scholar
  26. 26.
    Pan, J.Z., Ren, Y., Zhao, Y.: Tractable approximate deduction for OWL. Artif. Intell. 235, 95–155Google Scholar
  27. 27.
    Hogan, A., Pan, J.Z., Polleres, A., Decker, S.: SAOR: template rule optimisations for distributed reasoning over 1 billion linked data triples. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010. LNCS, vol. 6496, pp. 337–353. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-17746-0_22 CrossRefGoogle Scholar
  28. 28.
    Urbani, J., Kotoulas, S., Maassen, J., Harmelen, F., Bal, H.: OWL reasoning with WebPIE: calculating the closure of 100 billion triples. In: Aroyo, L., Antoniou, G., Hyvönen, E., Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010. LNCS, vol. 6088, pp. 213–227. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-13486-9_15 CrossRefGoogle Scholar
  29. 29.
    Ren, Y., Pan, J.Z., Lee, K.: Parallel ABox reasoning of EL ontologies. In: Proceedings of the First Joint International Conference of Semantic Technology (JIST 2011) (2011)Google Scholar
  30. 30.
    Du, J., Guilin Qi, Y.-D.S., Pan, J.Z.: A decomposition-based approach to OWL DL ontology diagnosis. In: Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2011) (2011)Google Scholar
  31. 31.
    Urbani, J., Harmelen, F., Schlobach, S., Bal, H.: QueryPIE: backward reasoning for OWL horst over very large knowledge bases. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 730–745. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-25073-6_46 CrossRefGoogle Scholar
  32. 32.
    Ren, Y., Pan, J.Z., Lee, K.: Optimising parallel ABox reasoning of EL ontologies. In: Proceedings of the DL (2012)Google Scholar
  33. 33.
    Heino, N., Pan, J.Z.: RDFS reasoning on massively parallel hardware. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012. LNCS, vol. 7649, pp. 133–148. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-35176-1_9 CrossRefGoogle Scholar
  34. 34.
    Fokoue, A., Meneguzzi, F., Sensoy, M., Pan, J.Z.: Querying linked ontological data through distributed summarization. In: Proceedings of the AAAI (2012)Google Scholar
  35. 35.
    Kazakov, Y., Krtzsch, M., Simank, F.: The incredible ELK. J. Autom. Reason. 53(1), 1–61 (2014)CrossRefGoogle Scholar
  36. 36.
    Getoor, L.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)zbMATHGoogle Scholar
  37. 37.
    De Raedt, L.: Logical and Relational Learning. Springer Science and Business Media, Heidelberg (2008)CrossRefzbMATHGoogle Scholar
  38. 38.
    Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11–33 (2016)CrossRefGoogle Scholar
  39. 39.
    Lehmann, J., Hitzler, P.: A refinement operator based learning algorithm for the \(\cal{ALC}\) description logic. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 147–160. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-78469-2_17 CrossRefGoogle Scholar
  40. 40.
    Vlker, J., Niepert, MGoogle Scholar
  41. 41.
    Pan, J.Z., Zhao, Y., Xu, Y., Quan, Z., Zhu, M., Gao, Z.: TBox learning from incomplete data by inference in BelNet+. Knowl. Based Syst. 75, 30–40 (2015)CrossRefGoogle Scholar
  42. 42.
    Alexopoulos, P., Villazon-Terrazas, B., Pan, J.Z.: Towards vagueness-aware semantic data. In: Proceedings of the URSW (2013)Google Scholar
  43. 43.
    Alexopoulos, P., Peroni, S., Villazon-Terrazas, B., Pan, J.Z.: Annotating ontologies with descriptions of vagueness. In: Proceedings of the ESWC (2014)Google Scholar
  44. 44.
    Jekjantuk, N., Pan, J.Z., Alexopoulos, P.: Towards a meta-reasoning framework for reasoning about vagueness in OWL ontologies. In: Proceedings of the ICSC (2016)Google Scholar
  45. 45.
    Sensoy, M., Fokoue, A., Pan, J.Z., Norman, T., Tang, Y., Oren, N., Sycara, K.: Reasoning about uncertain information and conflict resolution through trust revision. In: Proceedings of the AAMAS (2013)Google Scholar
  46. 46.
    Stoilos, G., Stamou, G., Pan, J.Z.: Fuzzy extensions of OWL: logical properties and reduction to fuzzy description logics. Int. J. Approx. Reason. 51(6), 656–679 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  47. 47.
    Lécué, F., Pan, J.Z.: Predicting knowledge in an ontology stream. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013, Beijing, China, 3–9 August 2013 (2013). http://www.aaai.org/ocs/index.php/IJCAI/IJCAI13/paper/view/6608
  48. 48.
    Lecue, F., Pan, J.Z.: Consistent knowledge discovery from evolving ontologies. In: Proceedings of the AAAI (2015)Google Scholar
  49. 49.
    Ren, Y., Pan, J.Z.: Optimising ontology stream reasoning with truth maintenance system. In: Proceedings of the ACM Conference on Information and Knowledge Management (CIKM 2011) (2011)Google Scholar
  50. 50.
    Kazakov, Y., Klinov, P.: Incremental reasoning in OWL EL without bookkeeping. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 232–247. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41335-3_15 CrossRefGoogle Scholar
  51. 51.
    Urbani, J., Margara, A., Jacobs, C., Harmelen, F., Bal, H.: DynamiTE: parallel materialization of dynamic RDF data. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 657–672. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41335-3_41 CrossRefGoogle Scholar
  52. 52.
    Ren, Y., Pan, J.Z., Guclu, I., Kollingbaum, M.: A combined approach to incremental reasoning for EL ontologies. In: Ortiz, M., Schlobach, S. (eds.) RR 2016. LNCS, vol. 9898, pp. 167–183. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-45276-0_13 CrossRefGoogle Scholar
  53. 53.
    Nguyen, H.H., Beel, D., Webster, G., Mellish, C., Pan, J.Z., Wallace, C.: CURIOS mobile: linked data exploitation for tourist mobile apps in rural areas. In: Supnithi, T., Yamaguchi, T., Pan, J.Z., Wuwongse, V., Buranarach, M. (eds.) JIST 2014. LNCS, vol. 8943, pp. 129–145. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-15615-6_10 CrossRefGoogle Scholar
  54. 54.
    Botoeva, E., Kontchakov, R., Ryzhikov, V., Wolter, F., Zakharyaschev, M.: Games for query inseparability of description logic knowledge bases. Artif. Intell. 234, 78–119 (2016). doi: 10.1016/j.artint.2016.01.010. http://www.sciencedirect.com/science/article/pii/S0004370216300017 MathSciNetCrossRefzbMATHGoogle Scholar
  55. 55.
    Botoeva, E., Lutz, C., Ryzhikov, V., Wolter, F., Zakharyaschev, M.: Query-based entailment and inseparability for ALC ontologies. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), pp. 1001–1007 (2016)Google Scholar
  56. 56.
    Nguyen, H., Valincius, E., Pan, J.Z.: A power consumption benchmark framework for ontology reasoning on android devices. In: Proceedings of the 4th OWL Reasoner Evaluation Workshop (ORE) (2015)Google Scholar
  57. 57.
    Guclu, I., Li, Y.-F., Pan, J.Z., Kollingbaum, M.J.: Predicting energy consumption of ontology reasoning over mobile devices. In: Groth, P., Simperl, E., Gray, A., Sabou, M., Krötzsch, M., Lecue, F., Flöck, F., Gil, Y. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 289–304. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-46523-4_18 CrossRefGoogle Scholar
  58. 58.
    Konev, B., Lutz, C., Walther, D., Wolter, F.: Model-theoretic inseparability and modularity of description logic ontologies. Artif. Intell. 203, 66–103 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  59. 59.
    Konev, B., Lutz, C., Wolter, F., Zakharyaschev, M.: Conservative rewritability of description logic TBoxes. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016) (2016)Google Scholar
  60. 60.
    Lutz, C., Wolter, F.: Deciding inseparability and conservative extensions in the description logic EL. J. Symbolic Comput. 45(2), 194–228 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  61. 61.
    Kostylev, E.V., Reutter, J.L., Vrgoč, D.: Containment of data graph queries. In: ICDT, pp. 131–142 (2014)Google Scholar
  62. 62.
    Kostylev, E.V., Reutter, J.L., Vrgoč, D.: Static analysis of navigational XPath over graph databases. Inf. Process. Lett. 116(7), 467–474 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  63. 63.
    Libkin, L., Martens, W., Vrgoč, D.: Querying graphs with data. J. ACM 63(2), 14 (2016)MathSciNetCrossRefGoogle Scholar
  64. 64.
    Baader, F., Bienvenu, M., Lutz, C., Wolter, F.: Query and predicate emptiness in ontology-based data access. J. Artif. Intell. Res. (JAIR) 56, 1–59 (2016)MathSciNetzbMATHGoogle Scholar
  65. 65.
    Bienvenu, M., Bourgaux, C., Goasdoué, F.: Explaining inconsistency-tolerant query answering over description logic knowledge bases. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI) (2016)Google Scholar
  66. 66.
    Bienvenu, M., Bourgaux, C., Goasdoué, F.: Query-driven repairing of inconsistent DL-Lite knowledge bases. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI) (2016)Google Scholar
  67. 67.
    Lord, P.: The semantic web takes wing: programming ontologies with Tawny-OWL. In: OWLED 2013 (2013). http://www.russet.org.uk/blog/2366
  68. 68.
    Denaux, R., Dimitrova, V., Cohn, A.G., Dolbear, C., Hart, G.: Rabbit to OWL: ontology authoring with a CNL-based tool. In: Fuchs, N.E. (ed.) CNL 2009. LNCS (LNAI), vol. 5972, pp. 246–264. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-14418-9_15 CrossRefGoogle Scholar
  69. 69.
    Power, R.: OWL simplified english: a finite-state language for ontology editing. In: Kuhn, T., Fuchs, N.E. (eds.) CNL 2012. LNCS (LNAI), vol. 7427, pp. 44–60. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-32612-7_4 CrossRefGoogle Scholar
  70. 70.
    Liebig, T., Noppens, O.: OntoTrack: a semantic approach for ontology authoring. Web Semant. Sci. Serv. Agents World Wide Web 3(2), 116–131 (2005)CrossRefGoogle Scholar
  71. 71.
    Denaux, R., Thakker, D., Dimitrova, V., Cohn, A.G.: Interactive semantic feedback for intuitive ontology authoring. In: FOIS, pp. 160–173 (2012)Google Scholar
  72. 72.
    Uschold, M., Gruninger, M., et al.: Ontologies: principles, methods and applications. Knowl. Eng. Rev. 11(2), 93–136 (1996)CrossRefGoogle Scholar
  73. 73.
    Suárez-Figueroa, M.C., Gómez-Pérez, A., Motta, E., Gangemi, A.: Ontology Engineering in a Networked World. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  74. 74.
    Fernandes, P.C.B., Guizzardi, R.S., Guizzardi, G.: Using goal modeling to capture competency questions in ontology-based systems. J. Inf. Data Manag. 2(3), 527 (2011)Google Scholar
  75. 75.
    Ren, Y., Parvizi, A., Mellish, C., Pan, J.Z., Deemter, K., Stevens, R.: Towards competency question-driven ontology authoring. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 752–767. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-07443-6_50 CrossRefGoogle Scholar
  76. 76.
    Zemmouchi-Ghomari, L., Ghomari, A.R.: Translating natural language competency questions into SPARQL queries: a case study. In: WEB 2013, pp. 81–86 (2013)Google Scholar
  77. 77.
    Malheiros, Y., Freitas, F.: A method to develop description logic ontologies iteratively based on competency questions: an implementation. In: ONTOBRAS, pp. 142–153 (2013)Google Scholar
  78. 78.
    Beaver, D.: Presupposition. In: van Benthem, J., ter Meulen, A. (eds.) The Handbook of Logic and Language, pp. 939–1008. Elsevier (1997)Google Scholar
  79. 79.
    Vigo, M., Jay, C., Stevens, R.: Design insights for the next wave ontology authoring tools. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2014, pp. 1555–1558 (2014). ISBN: 978-1-4503-2473-1. doi: 10.1145/2556288.2557284
  80. 80.
    Vigo, M., Bail, S., Jay, C., Stevens, R.: Overcoming the pitfalls of ontology authoring: strategies and implications for tool design. Int. J. Hum.-Comput. Stud. 72(12), 835–845 (2014). ISSN: 1071–5819. doi: 10.1016/j.ijhcs.2014.07.005, http://www.sciencedirect.com/science/article/pii/S1071581914001013
  81. 81.
    Vigo, M., Jay, C., Stevens, R.: Constructing conceptual knowledge artefacts: activity patterns in the ontology authoring process. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015, pp. 3385–3394 (2015). ISBN: 978-1-4503-3145-6. doi: 10.1145/2702123.2702495
  82. 82.
    Vigo, M., Jay, C., Stevens, R.: Protégé4US: harvesting ontology authoring data with Protégé. In: Presutti, V., Blomqvist, E., Troncy, R., Sack, H., Papadakis, I., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8798, pp. 86–99. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-11955-7_8 Google Scholar
  83. 83.
    Grau, B.C., Halaschek-Wiener, C., Kazakov, Y., Suntisrivaraporn, B.: Incremental classification of description logics ontologies. J. Autom. Reason. 44(4), 337–369 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  84. 84.
    Gonalves, R.S.: Impact analysis in description logic ontologies. Ph.D. thesis, University of Manchester (2014)Google Scholar
  85. 85.
    Matentzoglu, N., Vigo, M., Jay, C., Stevens, R.: Making entailment set changes explicit improves the understanding of consequences of ontology authoring actions. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 432–446. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-49004-5_28 CrossRefGoogle Scholar
  86. 86.
    Parvizi, A., Mellish, C., van Deemter, K., Ren, Y., Pan, J.Z.: Selecting ontology entailments for presentation to users. In: Proceedings of the International Conference on Knowledge Engineering and Ontology Development, KEOD 2014, Rome, Italy, 21–24 October 2014, pp. 382–387 (2014). doi: 10.5220/0005136203820387

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jeff Z. Pan
    • 1
    Email author
  • Nico Matentzoglu
    • 2
  • Caroline Jay
    • 2
  • Markel Vigo
    • 2
  • Yuting Zhao
    • 1
  1. 1.Department of Computing ScienceUniversity of AberdeenAberdeenUK
  2. 2.School of Computer ScienceUniversity of ManchesterManchesterUK

Personalised recommendations