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Prioritising Lexical Patterns to Increase Axiomatisation in Biomedical OntologiesThe Role of Localisation and Modularity
17 June 2013
accepted: 07 May 2014
22 January 2018 (online)
Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.
Objectives: In previous work, we have defined methods for the extraction of lexical patterns from labels as an initial step towards semi-automatic ontology enrichment methods. Our previous findings revealed that many biomedical ontologies could benefit from enrichment methods using lexical patterns as a starting point. Here, we aim to identify which lexical patterns are appropriate for ontology enrichment, driving its analysis by metrics to prioritised the patterns.
Methods: We propose metrics for suggesting which lexical regularities should be the starting point to enrich complex ontologies. Our method determines the relevance of a lexical pattern by measuring its locality in the ontology, that is, the distance between the classes associated with the pattern, and the distribution of the pattern in a certain module of the ontology. The methods have been applied to four significant biomedical ontologies including the Gene Ontology and SNOMED CT.
Results: The metrics provide information about the engineering of the ontologies and the relevance of the patterns. Our method enables the suggestion of links between classes that are not made explicit in the ontology. We propose a prioritisation of the lexical patterns found in the analysed ontologies.
Conclusions: The locality and distribution of lexical patterns offer insights into the further engineering of the ontology. Developers can use this information to improve the axiomatisation of their ontologies.
- 1 Stroetman V, Kalra D, Lewalle P, Rector A, Rodrigues J, Stroetman K. et al. Semantic interoperability for better health and safer health- care [34 pages]. 2009. Available from. http:// www.semantichealth.org/DELIVERABLES/ SemanticHEALTH_D1_1_finalC.pdf.
- 2 Studer R, Benjamins VR, Fensel D. Knowledge engineering: principles and methods. Data & knowledge engineering 1998; 25 (01) 161-197.
- 3 Machado CM, Rebholz-Schuhmann D, Freitas AT, Couto FM. The semantic web in translational medicine: current applications and future directions. Brief Bioinform 2013 Nov 6; bbt079.
- 4 Noy NF, Shah NH, Whetzel PL, Dai B, Dorf M, Griffith N. et al. BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res 2009; 37 0Web Server issue W170-W173.
- 5 Third A. “Hidden semantics”: what can we learn from the names in an ontology?. Utica, IL, USA: 2012
- 6 Buitelaar P, Cimiano P, Magnini B. editors Ontology Learning from Text. Methods, Evaluation and Applications. Amsterdam: IOS Press; 2005
- 7 Liu K, Hogan WR, Crowley RS. Methodological Review: Natural Language Processing Methods and Systems for Biomedical Ontology Learning. J of Biomedical Informatics 2011; 44 (01) 163-179.
- 8 Liu K, Mitchell KJ, Chapman WW, Savova GK, Sioutos N, Rubin DL. et al. Formative Evaluation of Ontology Learning Methods for Entity Discovery by Using Existing Ontologies as Reference Standards. Methods Inf Med 2013; 52 (04) 308-316.
- 9 Navigli R, Velardi P. Structural semantic interconnections: a knowledge-based approach to word sense disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005; 27 (07) 1075-1086.
- 10 Friedman C, Borlawsky T, Shagina L, Xing HR, Lussier YA. Bio-Ontologies and Text: Bridging the Modeling Gap Between. Bioinformatics 2006; 22 (19) 2421-2429.
- 11 Hearst MA. Automatic Acquisition of Hyponyms from Large Text Corpora. Proceedings of the 14th Conference on Computational Linguistics - Volume 2. Stroudsburg, PA, USA: Association for Computational Linguistics; 1992: 539-545.
- 12 Liu K, Chapman WW, Savova G, Chute CG, Sioutos N, Crowley RS. Effectiveness of Lexico-syntactic Pattern Matching for Ontology Enrichment with Clinical Documents. Methods Inf Med 2010; 50 (05) 397-407.
- 13 Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM. et al. Gene Ontology: tool for the unification of biology. Nature Genetics 2000; 25 (01) 25-29.
- 14 Bada M, Hunter L. Enrichment of OBO ontologies. J Biomed Inform 2007; 40 (03) 300-315.
- 15 Mungall CJ, Bada M, Berardini TZ, Deegan J, Ireland A, Harris MA. et al. Cross-product extensions of the Gene Ontology. Journal Biomed Inform 2011; 44 (01) 80-86.
- 16 Quesada-Martínez M, Fernández-Breis JT, Stevens R. Extraction and analysis of the structure of labels in biomedical ontologies. Proceedings of the 2nd international workshop on Managing interoperability and compleXity in health systems. New York, NY, USA: ACM; 2012: 7-16.
- 17 Rector AL, Brandt S, Schneider T. Getting the foot out of the pelvis: modeling problems affecting use of SNOMED CT hierarchies in practical applications. JAMIA 2011; 18 (04) 432-440.
- 18 Rector A, Iannone L. Lexically suggest, logically define: Quality assurance of the use of qualifiers and expected results of post-coordination in SNOMED CT. J Biomed Inform 2012; 45 (02) 199-209.
- 19 Mikroyannidi E, Iannone L, Stevens R, Rector A. Inspecting regularities in ontology design using clustering. Proceedings of the 10th international conference on The semantic web - Volume Part I. Berlin, Heidelberg: Springer-Verlag; 2011: 438-453.
- 20 Poveda-Villalón M, Suárez-Figueroa MC, Gómez-Pérez A. Validating Ontologies with OOPS! In. Teije A, ten Völker J, Handschuh S, Stuckenschmidt H, d’Acquin M, Nikolov A. et al. editors Knowledge Engineering and Knowledge Management. Berlin Heidelberg: Springer; 2012: 267-281.
- 21 Lozano-Tello A, Gomez-Perez A. ONTOMETRIC: A Method to Choose the Appropriate Ontology. Journal of Database Management 2004; 15 (02) 1-18.
- 22 Tartir S, Arpinar IB, Moore M, Sheth AP, Aleman-meza B. OntoQA: Metric-based ontology quality analysis. IEEE Workshop on Knowledge Acquisition from Distributed, Autonomous, Semantically Heterogeneous Data and Knowledge Sources. 2005
- 23 García J, García-Peñalvo FJ, Therón R. A Survey on Ontology Metrics. In. Lytras MD, Pablos POD, Ziderman A, Roulstone A, Maurer H, Imber JB. editors Knowledge Management, Information Systems, E-Learning, and Sustainability Research. Berlin Heidelberg: Springer; 2010. [cited 2013 Nov 22] 22-27.
- 24 Pesquita C, Faria D, Falcão AO, Lord P, Couto FM. Semantic Similarity in Biomedical Ontologies. PLoS Comput Biol 2009; 5 (07) e1000443.
- 25 Quesada-Martínez M, Fernandez-Breis JT, Stevens R. Enrichment of OWL Ontologies: a method for defining axioms from labels. In. Moss L, Sleeman D. editors Proceedings of the International Workshop on Capturing and Refining Knowledge in the Medical Domain (KMED’2012). Galway, Ireland: 2012: 5-10.
- 26 Rada R, Mili H, Bicknell E, Blettner M. Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics 1989; 19 (01) 17-30.
- 27 Lord PW, Stevens RD, Brass A, Goble CA. Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics 2003; 19 (10) 1275-1283.
- 28 Gentleman R. Visualizing and distances using GO. URL. http://www bioconductor org/docs/ vignettes html 2005
- 29 Legaz-García M del C, Martínez-Costa C, Menárguez-Tortosa M, Fernández-Breis JT. Recommendation of standardized health learning contents using archetypes and semantic web technologies. Studies in health technology and informatics 2012; 180: 963-967.
- 30 Smith B, Ashburner M, Rosse C, Bard J, Bug W, Ceusters W. et al. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology 2007; 25 (11) 1251-1255.
- 31 Egaña M, Rector A, Stevens R, Antezana E. Applying Ontology Design Patterns in Bio-ontologies. Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns [Internet]. Berlin, Heidelberg: Springer-Verlag; 2008. [cited 2012 Apr 17] 7-16. Available from. http://dx.doi.org/10.1007/978-3-540-87696-0_4.
- 32 Ferreira JD, Hastings J, Couto FM. Exploiting disjointness axioms to improve semantic similarity measures. Bioinformatics: 2013. Sep 3 btt491.