Skip to main content
SpringerLink
Log in

Understanding Author Intentions: Test Driven Knowledge Graph Construction

  • Chapter
  • First Online:
Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering (Reasoning Web 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9885))

Included in the following conference series:

  • 1330 Accesses

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.

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 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
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.

    https://googleblog.blogspot.co.uk/2012/05/introducing-knowledge-graph-things-not.html.

  2. 2.

    http://www.slideshare.net/jeffpan_sw/linked-data-and-knowledge-graphs-constructing-and-understanding-knowledge-graphs.

  3. 3.

    https://www.w3.org/TR/2004/REC-rdf-primer-20040210/.

  4. 4.

    https://www.w3.org/TR/owl2-overview/.

  5. 5.

    Without the use of blank notes (https://www.w3.org/TR/rdf11-concepts/#section-blank-nodes).

  6. 6.

    Although there are extensions of OWL for representing default knowledge, which is not included in the current version of OWL.

  7. 7.

    cf. Chap. 2 for an introduction of the DL syntax.

  8. 8.

    In fact, knowledge graphs can be written in some proprietary syntax, as long as such syntax can be mapped to RDF and OWL.

  9. 9.

    http://www.slideshare.net/jeffpan_sw/the-rise-of-approximate-ontology-reasoning-is-it-mainstream-yet.

  10. 10.

    http://www.slideshare.net/jeffpan_sw/the-maze-of-deletion-in-ontology-stream-reasoning.

  11. 11.

    http://www.semafora-systems.com/en/products/ontostudio/.

  12. 12.

    http://protege.stanford.edu.

  13. 13.

    Full negation is beyond EL.

  14. 14.

    https://github.com/matentzn/inference-inspector.

  15. 15.

    https://github.com/rsgoncalves/ecco.

  16. 16.

    http://aksw.org/Projects/ORE.html.

References

  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. 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: 0-521-78176-0

    MATH  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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 2006

    Google Scholar 

  6. Brachman, R.J., Levesque, H.J. (eds.): Readings in Knowledge Representation. Morgan Kaufmann Publishers Inc., San Francisco (1985). ISBN: 093461301X

    MATH  Google Scholar 

  7. Sowa, J.F.: Semantic networks. In: Encyclopedia of Artificial Intelligence. Wiley, New York (1987)

    Google Scholar 

  8. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Upper Saddle River (2010). ISBN: 978-0-13-604259-4

    MATH  Google Scholar 

  9. Quillian, M.R.: Word concepts: a theory and simulation of some basic semantic capabilities. Behav. Sci. 12(5), 410–430 (1967)

    Article  Google Scholar 

  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. 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. Brachman, R.J., Schmolze, J.G.: An overview of the KL-ONE knowledge representation system. Cogn. Sci. 9(2), 171 (1985)

    Article  Google Scholar 

  13. Hayes, P.J., Patel-Schneider, P.F.: RDF 1.1 semantics. W3C Recommendation, February 2014

    Google Scholar 

  14. Pan, J., Horrocks, I.: OWL-Eu: adding customised datatypes into OWL. J. Web Semant. 4(1), 29–39 (2006)

    Article  Google Scholar 

  15. Suchanek, F., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the WWW (2007)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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

    Chapter  Google Scholar 

  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. Pan, J.Z., Thomas, E., Zhao, Y.: Completeness guaranteed approximation for OWL DL query answering. In: Proceedings of the DL (2009)

    Google Scholar 

  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. 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. 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. Pan, J.Z., Ren, Y., Zhao, Y.: Tractable approximate deduction for OWL. Artif. Intell. 235, 95–155

    Google Scholar 

  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

    Chapter  Google Scholar 

  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

    Chapter  Google Scholar 

  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. 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. 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

    Chapter  Google Scholar 

  32. Ren, Y., Pan, J.Z., Lee, K.: Optimising parallel ABox reasoning of EL ontologies. In: Proceedings of the DL (2012)

    Google Scholar 

  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

    Chapter  Google Scholar 

  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. Kazakov, Y., Krtzsch, M., Simank, F.: The incredible ELK. J. Autom. Reason. 53(1), 1–61 (2014)

    Article  Google Scholar 

  36. Getoor, L.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)

    MATH  Google Scholar 

  37. De Raedt, L.: Logical and Relational Learning. Springer Science and Business Media, Heidelberg (2008)

    Book  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Chapter  Google Scholar 

  40. Vlker, J., Niepert, M

    Google Scholar 

  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)

    Article  Google Scholar 

  42. Alexopoulos, P., Villazon-Terrazas, B., Pan, J.Z.: Towards vagueness-aware semantic data. In: Proceedings of the URSW (2013)

    Google Scholar 

  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. 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. 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. 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)

    Article  MathSciNet  MATH  Google Scholar 

  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. Lecue, F., Pan, J.Z.: Consistent knowledge discovery from evolving ontologies. In: Proceedings of the AAAI (2015)

    Google Scholar 

  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. 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

    Chapter  Google Scholar 

  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

    Chapter  Google Scholar 

  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

    Chapter  Google Scholar 

  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

    Chapter  Google Scholar 

  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

    Article  MathSciNet  MATH  Google Scholar 

  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. 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. 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

    Chapter  Google Scholar 

  58. Konev, B., Lutz, C., Walther, D., Wolter, F.: Model-theoretic inseparability and modularity of description logic ontologies. Artif. Intell. 203, 66–103 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  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. Lutz, C., Wolter, F.: Deciding inseparability and conservative extensions in the description logic EL. J. Symbolic Comput. 45(2), 194–228 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  61. Kostylev, E.V., Reutter, J.L., Vrgoč, D.: Containment of data graph queries. In: ICDT, pp. 131–142 (2014)

    Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  63. Libkin, L., Martens, W., VrgoÄŤ, D.: Querying graphs with data. J. ACM 63(2), 14 (2016)

    Article  MathSciNet  Google Scholar 

  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)

    MathSciNet  MATH  Google Scholar 

  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. 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. Lord, P.: The semantic web takes wing: programming ontologies with Tawny-OWL. In: OWLED 2013 (2013). http://www.russet.org.uk/blog/2366

  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

    Chapter  Google Scholar 

  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

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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. Uschold, M., Gruninger, M., et al.: Ontologies: principles, methods and applications. Knowl. Eng. Rev. 11(2), 93–136 (1996)

    Article  Google Scholar 

  73. Suárez-Figueroa, M.C., Gómez-Pérez, A., Motta, E., Gangemi, A.: Ontology Engineering in a Networked World. Springer, Heidelberg (2012)

    Book  Google Scholar 

  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. 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

    Chapter  Google Scholar 

  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. 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. 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. 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. 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. 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. 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. Grau, B.C., Halaschek-Wiener, C., Kazakov, Y., Suntisrivaraporn, B.: Incremental classification of description logics ontologies. J. Autom. Reason. 44(4), 337–369 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  84. Gonalves, R.S.: Impact analysis in description logic ontologies. Ph.D. thesis, University of Manchester (2014)

    Google Scholar 

  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

    Chapter  Google Scholar 

  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

Download references

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.

Author information

Authors and Affiliations

  1. Department of Computing Science, University of Aberdeen, Aberdeen, UK

    Jeff Z. Pan & Yuting Zhao

  2. School of Computer Science, University of Manchester, Manchester, UK

    Nico Matentzoglu, Caroline Jay & Markel Vigo

Authors
  1. Jeff Z. Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nico Matentzoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Caroline Jay
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Markel Vigo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuting Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeff Z. Pan .

Editor information

Editors and Affiliations

  1. University of Aberdeen , Aberdeen, United Kingdom

    Jeff Z. Pan

  2. Free University of Bozen-Bolzano , Bolzano, Italy

    Diego Calvanese

  3. University of Technology Vienna , Vienna, Austria

    Thomas Eiter

  4. University of Oxford , Oxford, United Kingdom

    Ian Horrocks

  5. Stony Brook University , Stony Brook, New York, USA

    Michael Kifer

  6. University of Science and Technology , Hong Kong, China

    Fangzhen Lin

  7. University of Aberdeen , Aberdeen, United Kingdom

    Yuting Zhao

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Pan, J.Z., Matentzoglu, N., Jay, C., Vigo, M., Zhao, Y. (2017). Understanding Author Intentions: Test Driven Knowledge Graph Construction. In: Pan, J., et al. Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering. Reasoning Web 2016. Lecture Notes in Computer Science(), vol 9885. Springer, Cham. https://doi.org/10.1007/978-3-319-49493-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-49493-7_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49492-0

  • Online ISBN: 978-3-319-49493-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation