Identifying Pairs of Terms with Strong Semantic Connections in a Textbook Index

James Geller, Shmuel T. Klein, Yuriy Polyakov

Abstract

Semantic relationships are important components of ontologies. Specifying these relationships is work-intensive and error-prone when done by experts. Discovering domain concepts and strongly related pairs of concepts in a completely automated way from English text is an unresolved problem. This paper uses index terms from a textbook as domain concepts and suggests pairs of concepts that are likely to be connected by strong semantic relationships. Two textbooks on Cyber Security were used as testbeds. To show the generality of the approach, the index terms from one of the books were used to generate suggestions for where to place semantic relationships using the bodies of both textbooks. A good overlap was found.

References

  1. An, Y. J., Geller, J., Wu, Y., Chun, S. A.. 2007, Automatic Generation of Ontology from the Deep Web. Proc. DEXA 7807, Regensburg, Germany.
  2. BioPortal, 2015. http://bioportal.bioontology.org/.
  3. Bookstein A., Klein S.T., 1990. Information Retrieval Tools for Literary Analysis, in Database and Expert Systems Applications, edited by A M. Tjoa, Springer Verlag, Vienna 1-7.
  4. Caracciolo C., 2006. Designing and Implementing an Ontology for Logic and Linguistics, Literary & Linguistic Computing, vol. 21, pp. 29-39.
  5. Choueka Y., Klein S.T., Neuvitz E., 1983. Automatic Retrieval of Frequent Idiomatic and Collocational Expressions in a Large Corpus, Journal Assoc. Literary and Linguistic Computing, Vol. 4, 34-38.
  6. Cimiano P., Hotho A., Staab S., 2005. Learning concept hierarchies from text corpora using formal concept analysis, J. Artif. Int. Res., vol. 24, pp. 305-339.
  7. Cohen J., 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20 (1): 37-46. doi: 10.1177/001316446002000104.
  8. Fenz S., Ekelhart A., 2009. Formalizing information security knowledge, in Proc. of the 4th Int. Symposium on Information, Computer, and Communications Security, Sydney, Australia: ACM, 183 - 194.
  9. Geller, J., Chun, S., and Wali, A., 2014. A Hybrid Approach to Developing a Cyber Security Ontology. In: Proc. of the 3rd Int. Conf. on Data Management Technol. and Applicat., pp. 377-384, Vienna, Austria.
  10. Geneiatakis D., Lambrinoudakis C., 2007. An ontology description for SIP security flaws, Comput. Commun., vol. 30, pp. 1367-1374.
  11. Goodrich M. T., Tamassia R., 2011. Introduction to Computer Security, Addison Wesley.
  12. Hearst M. A., 1992. Automatic acquisition of hyponyms from large text corpora, in Proceedings of the 14th conference on Computational linguistics - Vol. 2 Nantes, France: Assoc. for Computational Linguistics.
  13. Herzog A., Shahmehri N., Duma C.. 2007. An Ontology of Information Security, Information Security and Privacy. 1(4), pp. 1-23.
  14. Hindle D., 1990. Noun classification from predicateargument structures, in Proc. of the 28th Ann. Meeting of Association for Computational Linguistics Pittsburgh, Pennsylvania: Ass. for Comp. Linguistics.
  15. Jain P., Hitzler P., Sheth A. P., Verma K., Yeh P. Z., 2013. Ontology alignment for linked open data, in Proc. of the 9th Int. Conference on the Semantic Web - Volume Part I Shanghai, China: Springer-Verlag.
  16. Katsurai M., Ogawa T., Haseyama M., 2014. A CrossModal Approach for Extracting Semantic Relationships Between Concepts Using Tagged Images. IEEE Trans. on Multimedia 16(4):1059-1074.
  17. Kullback S., Leibler R. A., 1951. On information and sufficiency. Annals of Math. Statistics 22 (1): 79-86.
  18. Lee C. H., Koo C., Na J. C., 2004. Automatic Identification of Treatment Relations for Medical Ontology Learning: An Exploratory Study. In Knowledge Organization and the Global Information Society: Proc. of the Eighth International ISKO Conference. Ergon Verlag, Wurzburg, Germany, pp. 245-250.
  19. Maedche A., Staab S., 2000. Mining Ontologies from Text, in Knowledge Engineering and Knowledge Management Methods, Models, and Tools, 12th International Conference, EKAW 2000. Lecture Notes in Computer Science, Volume 1937, pp. 189-202.
  20. Meersman R., Tari Z., Kim A., Luo J., Kang M., 2005. Security Ontology for Annotating Resources, in On the Move to Meaningful Internet Systems 2005: CoopIS, DOA, and ODBASE. vol. 3761: Springer, pp. 1483-99.
  21. Musen M. A., Noy N.F., Shah N. H., Whetzel P. L., Chute C.G., Story M.A., Smith B., NCBO team, 2012. The National Center for Biomedical Ontology. J Am Med Inform Assoc. Mar-Apr;19(2):190-5.
  22. Novalija I., Mladenic D., BradeŇ°ko L., 2011, OntoPlus: Text-driven ontology extension using ontology content, structure and co-occurrence information. Knowledge-Based Systems, 24(8), pp. 1261-1276.
  23. Pattanasri N., Jatowt A., Tanaka K., 2007. Context-aware search inside e-learning materials using textbook ontologies, in Proc. of the Joint 9th Asia-Pacific Web and 8th Int. Conf. on Web-age Inform. Management, appeared as: Advances in data and web management, LNCS 4505, Springer-Verlag, pp 658-669.
  24. Salomon D., 2006. Foundations of Computer Security, Springer Verlag, London, ISBN 978-1-8462-8193-8.
  25. Turpin A., Scholer F., 2006. User performance versus precision measures for simple search tasks, in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, Seattle, WA, 2006, pp. 11-18.
  26. Vigna G., Kruegel C., Jonsson E., Undercoffer J., Joshi A., Pinkston J., 2003. Modeling Computer Attacks: An Ontology for Intrusion Detection, in Recent Advances in Intrusion Detection. vol. 2820: Springer Berlin Heidelberg, pp. 113-135.
  27. Wali, A., Chun, S. A., Geller, J. 2013. A Bootstrapping Approach for Developing Cyber Security Ontology Using Textbook Index Terms. Proc. International Conference on Availability, Reliability and Security (ARES), University of Regensburg, Germany.
  28. Wiebke P., 2004. A Set-Theoretical Approach for the Induction of Inheritance Hierarchies, Electron Notes Theor Comput Sci, vol. 53, pp. 13-13.
Download


Paper Citation


in Harvard Style

Geller J., Klein S. and Polyakov Y. (2015). Identifying Pairs of Terms with Strong Semantic Connections in a Textbook Index . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015) ISBN 978-989-758-158-8, pages 307-315. DOI: 10.5220/0005615403070315


in Bibtex Style

@conference{keod15,
author={James Geller and Shmuel T. Klein and Yuriy Polyakov},
title={Identifying Pairs of Terms with Strong Semantic Connections in a Textbook Index},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)},
year={2015},
pages={307-315},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005615403070315},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)
TI - Identifying Pairs of Terms with Strong Semantic Connections in a Textbook Index
SN - 978-989-758-158-8
AU - Geller J.
AU - Klein S.
AU - Polyakov Y.
PY - 2015
SP - 307
EP - 315
DO - 10.5220/0005615403070315