Towards Measuring the Potential for Semantically Enriched Texts in Knowledge Working Environments

  • Gerald PetzEmail author
  • Dietmar Nedbal
  • Werner Wetzlinger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10923)


Knowledge work often requires people to read and comprehend documents in order to fulfill their tasks. To support knowledge workers in their real working environment semantically enriched texts can be leveraged. One technical basis is Named Entity Linking (NEL), which provides the capabilities to identify entities in a text and link them to a knowledge base that provides further information about them. This provides several opportunities to improve the outcome (e.g. text comprehension). In this paper, we lay the foundations for evaluating such semantic text enrichment environments that can be used in different business cases. The main result is an approach for measuring the effects of semantically enriched texts in the working environment comprising the five dimensions text, enrichment, reader, activity, and output.


Named Entity Linking Semantic enrichment NEL Reading comprehension Multimedia comprehension Use case 



This research was supported by HC Solutions GesmbH, Linz, Austria. We have to express our appreciation to Florian Wurzer, Reinhard Schwab and Manfred Kain for discussing these topics with us.

The TOMO Entity Linker is part of TOMO ® (, a big data platform for aggregating content, analyzing and visualizing content.


  1. 1.
    Duke, N.K., Pearson, P.D.: Effective practices for developing reading comprehension. J. Educ. 189, 107–122 (2008)CrossRefGoogle Scholar
  2. 2.
    Snow, C.: Reading for Understanding: Toward an R&D Program in Reading Comprehension. Rand Corporation (2002)Google Scholar
  3. 3.
    Cutting, L.E., Scarborough, H.S.: Prediction of reading comprehension: relative contributions of word recognition, language proficiency, and other cognitive skills can depend on how comprehension is measured. Sci. Stud. Read. 10, 277–299 (2006)CrossRefGoogle Scholar
  4. 4.
    Jenkins, J.R., Fuchs, L.S., van den Broek, P., Espin, C., Deno, S.L.: Sources of individual differences in reading comprehension and reading fluency. J. Educ. Psychol. 95, 719–729 (2003)CrossRefGoogle Scholar
  5. 5.
    Joshi, R.M., Aaron, P.G.: The component model of reading. simple view of reading made a little more complex. Read. Psychol. 21, 85–97 (2010)Google Scholar
  6. 6.
    Perfetti, C.A., Marron, M.A., Foltz, P.W.: Sources of comprehension failure: theoretical perspectives and case studies. In: Cornoldi, C., Oakhill, J.V. (eds.) Reading Comprehension Difficulties, pp. 137–166 (1996)Google Scholar
  7. 7.
    Cain, K., Oakhill, J.V., Barnes, M.A., Bryant, P.E.: Comprehension skill, inference-making ability, and their relation to knowledge. Mem. Cogn. 29, 850–859 (2001)CrossRefGoogle Scholar
  8. 8.
    Catts, H.W., Adlof, S.M., Weismer, S.E.: Language deficits in poor comprehenders. a case for the simple view of reading. J. Speech Lang. Hear. Res. 49, 278 (2006)CrossRefGoogle Scholar
  9. 9.
    Ghelani, K., Sidhu, R., Jain, U., Tannock, R.: Reading comprehension and reading related abilities in adolescents with reading disabilities and attention-deficit/hyperactivity disorder. Dyslexia 10, 364–384 (2004)CrossRefGoogle Scholar
  10. 10.
    McInnes, A., Humphries, T., Hogg-Johnson, S., Tannock, R.: Listening comprehension and working memory are impaired in attention-deficit hyperactivity disorder irrespective of language impairment. J. Abnorm. Child Psychol. 31, 427–443 (2003)CrossRefGoogle Scholar
  11. 11.
    Laufer, B.: What percentage of text-lexis is essential for comprehension. In: Special Language: From Humans Thinking to Thinking Machines (1989)Google Scholar
  12. 12.
    Hsueh-Chao, M.H., Nation, P.: Unknown vocabulary density and reading comprehension. Read. Foreign Lang. 13, 403–430 (2000)Google Scholar
  13. 13.
    Deane, P., Sheehan, K.M., Sabatini, J., Futagi, Y., Kostin, I.: Differences in text structure and its implications for assessment of struggling readers. Sci. Stud. Read. 10, 257–275 (2006)CrossRefGoogle Scholar
  14. 14.
    Francis, D.J., Snow, C.E., August, D., Carlson, C.D., Miller, J., Iglesias, A.: Measures of reading comprehension. a latent variable analysis of the diagnostic assessment of reading comprehension. Sci. Stud. Read. 10, 301–322 (2006)CrossRefGoogle Scholar
  15. 15.
    Fletcher, J.M.: Measuring reading comprehension. Sci. Stud. Read. 10, 323–330 (2006)CrossRefGoogle Scholar
  16. 16.
    Rayner, K., Chace, K.H., Slattery, T.J., Ashby, J.: Eye movements as reflections of comprehension processes in reading. Sci. Stud. Read. 10, 241–255 (2006)CrossRefGoogle Scholar
  17. 17.
    Hiebert, E.E.: Standards, assessments, and text difficulty. In: Farstrup, A.E., Samuels, S.J. (eds.) What Research Has to Say About Reading Instruction, pp. 337–369 (2002)Google Scholar
  18. 18.
    van Elst, L., Kiesel, M., Schwarz, S., Buscher, G., Lauer, A., Dengel, A.: Contextualized knowledge acquisition in a personal semantic Wiki. In: Gangemi, A. (eds.) EKAW 2008. LNCS (LNAI), vol. 5268, pp. 172–187. Springer, Heidelberg (2008). Scholar
  19. 19.
    Kintsch, W.: The role of knowledge in discourse comprehension: a construction-integration model. Psychol. Rev. 95, 163–182 (1988)CrossRefGoogle Scholar
  20. 20.
    Wallen, E., Plass, J.L., Brünken, R.: The function of annotations in the comprehension of scientific texts. Cognitive load effects and the impact of verbal ability. Educ. Tech. Res. Dev. 53, 59–71 (2005)CrossRefGoogle Scholar
  21. 21.
    Salmeron, L., Canas, J.J., Kintsch, W., Fajardo, I.: Reading strategies and hypertext comprehension. Discourse Process. 40, 171–191 (2005)CrossRefGoogle Scholar
  22. 22.
    Jones, L.C., Plass, J.L.: Supporting listening comprehension and vocabulary acquisition in french with multimedia annotations. Mod. Lang. J. 86, 546–561 (2002)CrossRefGoogle Scholar
  23. 23.
    Brünken, R., Plass, J.L., Leutner, D.: Assessment of cognitive load in multimedia learning with dual-task methodology. auditory load and modality effects. Instr. Sci. 32, 115–132 (2004)CrossRefGoogle Scholar
  24. 24.
    Mangen, A., Walgermo, B.R., Brønnick, K.: Reading linear texts on paper versus computer screen. Effects on reading comprehension. Int. J. Educ. Res. 58, 61–68 (2013)CrossRefGoogle Scholar
  25. 25.
    Sweller, J.: Cognitive load theory, learning difficulty, and instructional design. Learn. Instr. 4, 295–312 (1994)CrossRefGoogle Scholar
  26. 26.
    Sweller, J., van Merrienboer, J.J.G., Paas, F.G.W.C.: Cognitive architecture and instructional design. Educ. Psychol. Rev. 10, 251–296 (1998)CrossRefGoogle Scholar
  27. 27.
    Ayres, P., Sweller, J.: The split-attention principle in multimedia learning. In: Mayer, R.E. (ed.) The Cambridge Handbook of Multimedia Learning, vol. 2, pp. 206–226. Cambridge University Press, Cambridge (2014)Google Scholar
  28. 28.
    Zumbach, J., Mohraz, M.: Cognitive load in hypermedia reading comprehension. Influence of text type and linearity. Comput. Hum. Behav. 24, 875–887 (2008)CrossRefGoogle Scholar
  29. 29.
    Kim, Y.-S., Snow, C.E.: Text modification. Enhancing English language learners’ reading comprehension. In: Hiebert, E.H., Sailors, M. (eds.) Finding the Right Texts. What Works for Beginning and Struggling Readers, pp. 129–148. Guilford, New York (2009)Google Scholar
  30. 30.
    Antonenko, P.D., Niederhauser, D.S.: The influence of leads on cognitive load and learning in a hypertext environment. Comput. Hum. Behav. 26, 140–150 (2010)CrossRefGoogle Scholar
  31. 31.
    Maes, A., van Geel, A., Cozijn, R.: Signposts on the digital highway. The effect of semantic and pragmatic hyperlink previews. Interact. Comput. 18, 265–282 (2006)CrossRefGoogle Scholar
  32. 32.
    Thalhammer, A., Rettinger, A.: ELES: combining entity linking and entity summarization. In: Bozzon, A., Cudre-Maroux, P. (eds.) ICWE 2016. LNCS, vol. 9671, pp. 547–550. Springer, Cham (2016). Scholar
  33. 33.
    Petasis, G., Spiliotopoulos, D., Tsirakis, N., Tsantilas, P.: Large-scale sentiment analysis for reputation management. In: Gindl, S., Remus, R., Wiegand, M. (eds.) 2nd Workshop on Practice and Theory of Opinion Mining and Sentiment Analysis (2013)Google Scholar
  34. 34.
    Derczynski, L., Maynard, D., Rizzo, G., van Erp, M., Gorrell, G., Troncy, R., Petrak, J., Bontcheva, K.: Analysis of Named Entity Recognition and Linking for Tweets. Preprint Submitted to Elsevier (2014)Google Scholar
  35. 35.
    Rizzo, G., van Erp, M., Troncy, R.: Benchmarking the extraction and disambiguation of named entities on the semantic web. In: 9th International Conference on Language Resources and Evaluation (LREC 2014), pp. 4593–4600 (2014)Google Scholar
  36. 36.
    Holzinger, A.: Introduction to machine learning and knowledge extraction (MAKE). Mach. Learn. Knowl. Extr. 1, 1–20 (2017)CrossRefGoogle Scholar
  37. 37.
    Rizzo, G., Troncy, R., Hellmann, S., Brümmer, M.: NERD meets NIF: lifting NLP extraction results to the linked data cloud. In: LDOW, 5th Workshop on Linked Data on the Web, Lyon, France, 16 April 2012Google Scholar
  38. 38.
    Sasaki, F., Dojchinovski, M., Nehring, J.: Chainable and extendable knowledge integration web services. In: van Erp, M., et al. (eds.) ISWC 2016. LNCS, vol. 10579, pp. 89–101. Springer, Cham (2017). Scholar
  39. 39.
    Petz, G., Wetzlinger, W., Nedbal, D.: Improving language-dependent named entity detection. In: Holzinger, A., Kieseberg, P., Tjoa, A.M. (eds.) CD-MAKE 2017. LNCS, vol. 10410, pp. 330–345. Springer, Cham (2017). Scholar
  40. 40.
    Salmons, J.: How to Use Cases in Research Methods Teaching. An Author and Editor’s View. SAGE Publications, London (2014)CrossRefGoogle Scholar
  41. 41.
    Jacobson, I., Ng, P.-W.: Aspect-Oriented Software Development with Use Cases. Addison-Wesley, Boston (2005)Google Scholar
  42. 42.
    Dobing, B., Parsons, J.: The role of use cases in the UML: a review and research agenda. In: Siau, K. (ed.) Advanced Topics in Database Research, vol. 1, pp. 367–382. IGI Global, Hershey (2002)CrossRefGoogle Scholar
  43. 43.
    Graesser, A.C., McNamara, D.S., Louwerse, M.M., Cai, Z.: Coh-Metrix: analysis of text on cohesion and language. Behav. Res. Methods Instrum. Comput. 36, 193–202 (2004)CrossRefGoogle Scholar
  44. 44.
    Leow, R.P.: The effects of input enhancement and text length on adult L2 readers’ comprehension and intake in second language acquisition. Appl. Lang. Learn. 8, 151–182 (1997)Google Scholar
  45. 45.
    Mehrpour, S., Riazi, A.: The impact of text length on EFL students’ reading comprehension. Asian EFL J. 6, 1–13 (2004)Google Scholar
  46. 46.
    McNamara, D.S., Graesser, A.C., McCarthy, P.M., Cai, Z.: Automated Evaluation of Text and Discourse with Coh-Metrix. Cambridge University Press, New York (2014)CrossRefGoogle Scholar
  47. 47.
    Crossley, S., McNamara, D.: Cohesion, coherence, and expert evaluations of writing proficiency. In: Proceedings of the 32nd Annual Conference of the Cognitive Science Society, Austrin, Texas, pp. 984–989 (2010)Google Scholar
  48. 48.
    Gernsbacher, M.A.: Language Comprehension as Structure Building. L. Erlbaum, Hillsdale (1990)CrossRefGoogle Scholar
  49. 49.
    McNamara, D., Kintsch, E., Songer, N.B., Kintsch, W.: Are good texts always better? Interactions of text coherence, background knowledge, and levels of understanding in learning from text. Cogn. Instr. 14, 1–43 (1996)CrossRefGoogle Scholar
  50. 50.
    O’Reilly, T., McNamara, D.S.: The impact of science knowledge, reading skill, and reading strategy knowledge on more traditional “high-stakes” measures of high school students’ science achievement. Am. Educ. Res. J. 44, 161–196 (2007)CrossRefGoogle Scholar
  51. 51.
    Kulkarni, S., Singh, A., Ramakrishnan, G., Chakrabarti, S.: Collective annotation of Wikipedia entities in web text. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 457–466. ACM, New York (2009)Google Scholar
  52. 52.
    Meij, E., Weerkamp, W., de Rijke, M.: Adding semantics to microblog posts. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 563–572. ACM, New York (2012)Google Scholar
  53. 53.
    Piccinno, F., Ferragina, P.: From TagME to WAT: a new entity annotator. In: Proceedings of the First International Workshop on Entity Recognition & Disambiguation, pp. 55–62. ACM, New York (2014)Google Scholar
  54. 54.
    Hachey, B., Radford, W., Nothman, J., Honnibal, M., Curran, J.R.: Evaluating entity linking with Wikipedia. Artif. Intell. 194, 130–150 (2013)MathSciNetCrossRefGoogle Scholar
  55. 55.
    Madrid, I.R., van Oostendorp, H., Puerta Melguizo, M.C.: The effects of the number of links and navigation support on cognitive load and learning with hypertext. The mediating role of reading order. Comput. Hum. Behav. 25, 66–75 (2009)CrossRefGoogle Scholar
  56. 56.
    Kendeou, P., van den Broek, P.: The effects of prior knowledge and text structure on comprehension processes during reading of scientific texts. Mem. Cogn. 35, 1567–1577 (2007)CrossRefGoogle Scholar
  57. 57.
    Amadieu, F., Tricot, A., Mariné, C.: Prior knowledge in learning from a non-linear electronic document. Disorientation and coherence of the reading sequences. Comput. Hum. Behav. 25, 381–388 (2009)CrossRefGoogle Scholar
  58. 58.
    Overbeek, S.J., van Bommel, P., Proper, H.A.: Statics and dynamics of cognitive and qualitative matchmaking in task fulfillment. Inf. Sci. 181, 129–149 (2011)CrossRefGoogle Scholar
  59. 59.
    Ouellette, G.P.: What’s meaning got to do with it. The role of vocabulary in word reading and reading comprehension. J. Educ. Psychol. 98, 554–566 (2006)CrossRefGoogle Scholar
  60. 60.
    Pearson, P.D., Hamm, D.N.: The assessment of reading comprehension: a review of practices - past, present, and future. In: Paris, S.G., Stahl, S.A. (eds.) Children’s Reading Comprehension and Assessment, pp. 13–69 (2005)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Gerald Petz
    • 1
    Email author
  • Dietmar Nedbal
    • 1
  • Werner Wetzlinger
    • 1
  1. 1.University of Applied Sciences Upper AustriaSteyrAustria

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