Abstract
This paper deals with the problem of maintenance of semantic annotations produced based on domain ontologies. Many annotated texts have been produced and made available to end-users. If not reviewed regularly, the quality of these annotations tends to decrease over time due to the evolution of the domain ontologies. The quality of these annotations is critical for tools that exploit them (e.g., search engines and decision support systems) and need to ensure an acceptable level of performance. Although the recent advances for ontology-based annotation systems to annotate new documents, the maintenance of existing annotations remains under studied. In this work we present an analysis of the impact of ontology evolution on existing annotations. To do so, we used two well-known annotators to generate more than 66 million annotations from a pre-selected set of 5000 biomedical journal articles and standard ontologies covering a period ranging from 2004 to 2016. We highlight the correlation between changes in the ontologies and changes in the annotations and we discuss the necessity to improve existing annotation formalisms in order to include elements required to support (semi-) automatic annotation maintenance mechanisms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hodge, G.: Systems of Knowledge Organization for Digital Libraries: Beyond Traditional Authority Files. ERIC, Washington (2000)
Comeau, D., Doan, R., Ciccarese, P., Cohen, K., Krallinger, M., Leitner, F., Lu, Z., Peng, Y., Rinaldi, F., Torii, M., Valencia, A., Verspoor, K., Wiegers, T., Wu, C., Wilbur, W.: BioC: a minimalist approach to interoperability for biomedical text processing. Database: J. Biol. Databases Curation 2013, bat064 (2013)
Dos Reis, J.C., Pruski, C., Da Silveira, M., Reynaud-Delaître, C.: Understanding semantic mapping evolution by observing changes in biomedical ontologies. J. Biomed. Inf. 47, 71–82 (2014)
Hartung, M., Kirsten, T., Gross, A., Rahm, E.: OnEX: exploring changes in life science ontologies. BMC Bioinform. 10(1), 1 (2009)
Oren, E., Möller, K., Scerri, S., Handschuh, S., Sintek, M.: What are semantic annotations? Relatório técnico. DERI Galway 9, 62 (2006)
Luong, P.H., Dieng-Kuntz, R.: A rule-based approach for semantic annotation evolution in the coswem system. In: Kon, M., Lemire, D. (eds.) Canadian Semantic Web. Semantic Web and Beyond, vol. 2, pp. 103–120. Springer US, New York (2006)
Gross, A., Hartung, M., Kirsten, T., Rahm, E.: Estimating the quality of ontology-based annotations by considering evolutionary changes. In: Paton, N.W., Missier, P., Hedeler, C. (eds.) DILS 2009. LNCS, vol. 5647, pp. 71–87. Springer, Heidelberg (2009)
Hartung, M., Kirsten, T., Rahm, E.: Analyzing the evolution of life science ontologies and mappings. In: Bairoch, A., Cohen-Boulakia, S., Froidevaux, C. (eds.) DILS 2008. LNCS (LNBI), vol. 5109, pp. 11–27. Springer, Heidelberg (2008)
Da Silveira, M., Dos Reis, J.C., Pruski, C.: Management of dynamic biomedical terminologies: current status and future challenges. Yearb. Med. Inf. 10(1), 125–133 (2015)
Traverso-Ribón, I., Vidal, M.-E., Palma, G.: AnnEvol: an evolutionary framework to description ontology-based annotations. In: Ashish, N., Ambite, J.-L. (eds.) DILS 2015. LNCS, vol. 9162, pp. 87–103. Springer, Heidelberg (2015)
Groß, A., Hartung, M., Prfer, K., Kelso, J., Rahm, E.: Impact of ontology evolution on functional analyses. Bioinformatics 28(20), 2671–2677 (2012)
Frost, H.R., Moore, J.H.: Optimization of gene set annotations via entropy minimization over variable clusters (EMVC). Bioinformatics (Oxf. Engl.) 30(12), 1698–1706 (2014)
Cunningham, H.: GATE, a general architecture for text engineering. Comput. Humanit. 36(2), 223–254 (2002)
Whetzel, P.L., Noy, N.F., Shah, N.H., Alexander, P.R., Nyulas, C., Tudorache, T., Musen, M.A.: BioPortal: enhanced functionality via new web services from the national center for biomedical ontology to access and use ontologies in software applications. Nucleic Acids Res. 39(suppl 2), W541–W545 (2011)
Hartung, M., Groß, A., Rahm, E.: COnto-Diff: generation of complex evolution mappings for life science ontologies. J. Biomed. Inf. 46(1), 15–32 (2013)
Doğan, R.I., Leaman, R., Lu, Z.: NCBI disease corpus: a resource for disease name recognition and concept normalization. J. Biomed. Inf. 47, 1–10 (2014)
Acknowledgment
This work is supported by the National Research Fund (FNR) of Luxembourg and Deutsche Forschungsgemeinschaft (DFG) under the ELISA research project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Cardoso, S.D. et al. (2016). Leveraging the Impact of Ontology Evolution on Semantic Annotations. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10024. Springer, Cham. https://doi.org/10.1007/978-3-319-49004-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-49004-5_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49003-8
Online ISBN: 978-3-319-49004-5
eBook Packages: Computer ScienceComputer Science (R0)