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01-08-2015 | Transactional Processing Systems

An Approach for Learning Expressive Ontologies in Medical Domain

Authors: Ana B. Rios-Alvarado, Ivan Lopez-Arevalo, Edgar Tello-Leal, Victor J. Sosa-Sosa

Published in: Journal of Medical Systems | Issue 8/2015

Abstract

The access to medical information (journals, blogs, web-pages, dictionaries, and texts) has been increased due to availability of many digital media. In particular, finding an appropriate structure that represents the information contained in texts is not a trivial task. One of the structures for modeling the knowledge are ontologies. An ontology refers to a conceptualization of a specific domain of knowledge. Ontologies are especially useful because they support the exchange and sharing of information as well as reasoning tasks. The usage of ontologies in medicine is mainly focussed in the representation and organization of medical terminologies. Ontology learning techniques have emerged as a set of techniques to get ontologies from unstructured information. This paper describes a new ontology learning approach that consists of a method for the acquisition of concepts and its corresponding taxonomic relations, where also axioms disjointWith and equivalentClass are learned from text without human intervention. The source of knowledge involves files about medical domain. Our approach is divided into two stages, the first part corresponds to discover hierarchical relations and the second part to the axiom extraction. Our automatic ontology learning approach shows better results compared against previous work, giving rise to more expressive ontologies.

http://geneontology.org/page/download-ontology

A hyponym can be defined as a word of more specific meaning than a general or superordinate term applicable to it. By contrast, a hypernym is a word with a broad meaning constituting a category under which more specific words fall. For example, Apple, Banana, and Pear are hyponyms of Fruit whereas Fruit is a hypernym of Apple, Banana, and Pear.

http://www.nlm.nih.gov/mesh/MBrowser.html

http://protege.stanford.edu

http://www.semtalk.com/semnet\_files/POntoEdit.htm

http://www.neon-project.org

http://kaon.semanticweb.org

according to Web Ontology Language (OWL). http://www.w3.org/TR/owl-features/

Ontology for human disease http://www.berkeleybop.org/ontologies/doid.owl

http://webdocs.cs.ualberta.ca/\$sim\$lindek/minipar.htm

http://www.who.int/diabetes/en/

http://www.fountia.com/diabetes

http://www.worldhealthsciences.com/diabetes-statistics-in-developed-countries.html#ixzz1HvEMYakA

http://www.alchemyapi.com

http://www.opencalais.com

NE is a named entity and NP is a noun phrase.

Stroetman, V., Kalra, D., Lewalle, P., Rector, A., Rodrigues, J., Stroetman, K., Surjan, G., Ustun, B., Virtanen, M., and Zanstra, P., Semantic Interoperability for Better Health and Safer Healthcare. Technical report, European Commission, Directorate-General for Communications Networks, Content and Technology, 2009.

Zeshan, F., and Mohamad, R., Medical ontology in the dynamic healthcare environment. Proc. Comput. Sci. 10:340–348, 2012.CrossRef

Catarinucci, L., Colella, R., Esposito, A., Tarricone, L., and Zappatore, M., RFID sensor-tags feeding a context-aware rule-based healthcare monitoring system. J. Med. Syst. 36(6):3435–3449, 2012.PubMedCrossRef

Zhen, H., Li, J.-S., Zhou, T.-S., Yu, H.-Y., Suzuki, M., and Araki, K., Ontology-based clinical pathways with semantic rules. J. Med. Syst. 36(4):2203–2212, 2012.CrossRef

Horrocks, I., Tool support for ontology engineering. Foundations for the Web of Information and Services, pp. 103–112, 2011.

Maedche, A., and Staab, S., Ontology learning for the semantic web. Intell. Syst. IEEE 16(2):72–79, 2001.CrossRef

Hearst, M., Automatic acquisition of hyponyms from large text corpora. Proceedings of the 14th International Conference on Computational Linguistics, volume 2, pp. 539–545, 1992.

Snow, R., Jurafsky, D., and Ng, A., Learning syntactic patterns for automatic hypernym discovery. Adv. Neural Inf. Process. Syst. 17:1297–1304, 2004.

Rios-Alvarado, A. B., Lopez-Arevalo, I., and Sosa-Sosa, V. J., Learning concept hierarchies from textual resources for ontologies. Expert Syst. Appl. 1(40):5907–5915, 2013.CrossRef

10.

Sánchez, D., Domain ontology learning from the web. Knowl. Eng. Rev. 24(4):413, 2009.CrossRef

11.

Schutz, A., and Buitelaar, P., RelExt: a tool for relation extraction from text in ontology extension. In: Gil, Y., Motta, E., Benjamins, V. R., and Musen, M. A. (Eds.), The Semantic Web – ISWC 2005, vol. 3729. LNCS Springer, Berlin, pp. 593–606, 2005.CrossRef

12.

Del Vasto Terrientes, L., Moreno, A., and Sánchez, D., Discovery of relation axioms from the web. In: Bi, Y., and Williams, M.-A. (Eds.), Knowledge Science, Engineering and Management, vol. 6291. LNCS Springer, Berlin, pp. 222–233, 2010.CrossRef

13.

Völker, J., Hitzler, P., and Cimiano, P., Acquisition of OWL DL axioms from lexical resources. In: Franconi, E., Kifer, M., and May, W. (Eds.), The Semantic Web: Research and Applications, vol. 4519. LNCS Springer, Berlin, pp. 670–685, 2007.CrossRef

14.

Völker, J., and Rudolph, S., Lexico-logical acquisition of OWL—DL axioms. In: Medina, R., and Obiedkov, S. (Eds.), Formal Concept Analysis, vol. 4933. LNCS Springer, Berlin, pp. 62–77, 2008.CrossRef

15.

Buitelaar, P., Cimiano, P., and Magnini, B., Ontology Learning from Text: Methods, Evaluation and Applications/ Frontiers in Artificial Intelligence and Applications. Vol. 123. IOSPress, 2005.

16.

Mangold, C., A survey and classification of semantic search approaches. Int. J. Metadata Semant. Ontol. 2(1):23–34, 2007.CrossRef

17.

Gruber, T., A translation approach to portable ontology specifications. Knowl. Acquis. 5(2):199–220, 1993.CrossRef

18.

Sánchez, D., and Moreno, A., Learning medical ontologies from the web. In: Riaño, D. (Ed.), Knowledge Management for Health Care Procedures, vol. 4924. LNCS Springer, Berlin, pp. 32–45, 2008.CrossRef

19.

Lee, C., Khoo, C., and Na, J., Automatic identification of treatment relations for medical ontology learning: an exploratory study. Proceedings of the Eighth International ISKO Conference, Knowledge Organization and the Global Information Society. Wurzburg, Germany, pp. 245–250, 2004.

20.

Caraballo, S., Automatic construction of a hypernym-labeled noun hierarchy from text. Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, ACL. pp. 120–126, 1999.

21.

Pantel, P., and Lin, D., Discovering word senses from text. Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM. New York, NY, USA, pp. 613–619, 2002.

22.

Ritter, A., Soderland, S., and Etzioni O., What is this, anyway: automatic hypernym discovery. Proceedings of AAAI-09 Spring Symposium on Learning by Reading and Learning to Read, 2009

23.

Cimiano, P., and Staab, S., Learning by googling. SIGKDD Explor. Newsl. 6(2):24–33, 2004.CrossRef

24.

Ortega-Mendoza, R., Villaseñor-Pineda, L., and Montes-y-Gómez, M., Using lexical patterns for extracting hyponyms from the web. In: Gelbukh, A., and Kuri Morales, A. F. (Eds.), MICAI 2007: Advances in Artificial Intelligence, vol. 4827. LNCS Springer, Berlin, pp. 904–911, 2007.CrossRef

25.

Sang, E., Extracting hypernym pairs from the web. Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, Prague, Czech Republic, pp. 165–168, 2007.

26.

Lin, D., and Pantel, P., Discovery of inference rules for question-answering. Nat. Lang. Eng. 7(4):343–360, 2001.CrossRef

27.

Harris, Z., Distributional structure. Word 10(23):46–162, 1954.

28.

Pantel, P., Clustering by committee. Phd Thesis. University of Alberta, Alberta, Canada, 2003.

29.

Turney, P. D., The latent relation mapping engine: Algorithm and experiments. J. Artif. Intell. Res. 33(1):615–655, 2008.

30.

Turney, P. D., and Pantel, P., From frequency to meaning: Vector space models of semantics. J. Artif. Intell. Res. 37(1):141–188, 2010.

31.

Church, K. W., and Hanks, P., Word association norms, mutual information, and lexicography. Comput. Linguist. 16(1):22–29, 1990.

32.

Hindle, D., Noun classification from predicate-argument structures. Proceedings of the 28th annual meeting on Association for Computational Linguistics, Pittsburgh, Pennsylvania, pp. 268–275, 1990.

33.

Giuliano, C., and Gliozzo, A., Instance-based ontology population exploiting named-entity substitution. Proceedings of the 22nd International Conference on Computational Linguistics (COLING’08) Manchester, United Kingdom, pp. 265–272, 2008.

34.

Brank, J., Madenic, D., and Groblenik, M., Gold standard based ontology evaluation using instance assignment. Proceedings of the 4th Workshop on Evaluating Ontologies for the Web (EON2006), Edinburgh, Scotland, 2006.

35.

Dellschaft, K., and Staab, S., On how to perform a gold standard based evaluation of ontology learning. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., and Aroyo, L. M. (Eds.), The Semantic Web—ISWC 2006, vol. 4273. LNCS Springer, Berlin, pp. 228–241, 2006.CrossRef

36.

Zavitsanos, E., Paliouras, G., and Vouros, G. A., Gold standard evaluation of ontology learning methods through ontology transformation and alignment. IEEE Trans. Knowl. Data Eng. 23(11):1635–1648, 2011.CrossRef

37.

Cimiano, P., Hotho, A., and Staab, S., Learning concept hierarchies from text corpora using formal concept analysis. J. Artif. Intell. Res. 24(1):305–339, 2005.

38.

Jiang, X., and Tan, A.-H., CRCTOL: A semantic-based domain ontology learning system. J. Am. Soc. Inf. Sci. Technol. 61(1):150–168, 2010.CrossRef

39.

Volker, J., Vrandcic, D., Sure, Y., and Hotho, A., Learning disjointness. In: Franconi, E., Kifer, M., and May, W. (Eds.), The Semantic Web: Research and Applications, vol. 4519. LNCS Springer, Berlin, pp. 175–189, 2007.CrossRef

Title: An Approach for Learning Expressive Ontologies in Medical Domain
Authors: Ana B. Rios-Alvarado
Ivan Lopez-Arevalo
Edgar Tello-Leal
Victor J. Sosa-Sosa
Publication date: 01-08-2015
Publisher: Springer US
Published in: Journal of Medical Systems / Issue 8/2015
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-015-0261-z

At a glance: The STEP trials

Springer Medicine

An Approach for Learning Expressive Ontologies in Medical Domain

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

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