The Semantics of COVID-19 Web Data: Ontology Learning and Population

Sumit      Sharma; Sarika      Jain

Abstract

Background: The acquisition and exchange of meaningful, integrated, and accurate information are at the forefront of the combat against COVID-19; still, there are many countries whose health systems are disrupted. Moreover, no one is adequately equipped for COVID-19 contingencies. Many organizations have established static information systems to manage the information. This fact presents numerous issues, including delays, inconsistencies, and inaccuracies in COVID-19 information collected for pandemic control and monitoring.

Objective: This paper presents a semantic representation of COVID-19 data, a domain ontology to facilitate measurement, clarification, linking, and sharing. We automatically generate a computer- intelligible knowledge base from COVID-19 case information, which contains machineunderstandable information. Furthermore, we have anticipated an ontology population algorithm from tabular data that delivers interoperable, consistent, and accurate content with COVID-19 information.

Methods: We utilized the tabula package to extract the tables from PDF files and user NLP libraries to sort and rearrange tables. The proposed algorithm was then applied to all instances to automatically add to the input ontology using the Owlready Python module. Moreover, to evaluate the performance, SPARQL queries were used to retrieve answers to competency questions.

Results: When there is an equivalence relationship, the suggested algorithm consistently finds the right alignments and performs at its best or very close to it in terms of precision. Moreover, a demonstration of algorithm performance and a case study on COVID-19 data to information management and visualization of the populated data are also presented.

Conclusion: This paper presents an ontology learning/matching tool for ontology and populating instances automatically to ontology by emphasizing the importance of a unit's distinguishing features by unit matching.

Keywords: Information extraction, ontology population, COVID-19 ontology, semantic web, ontology engineering, ontology reuse, ontology publishing.

Graphical Abstract

[1]
Maedche A, Pekar V, Staab S. Ontology learning part one—on discovering taxonomic relations from the web InWeb Intelligence.  Berlin, Heidelberg: Springer 2003; pp. 301-19.

[2]
Cimiano P. Ontology learning and population from text: algorithms, evaluation and applications. Springer Science & Business Media 2006.

[3]
Wohlgenannt G, Belk S, Schett M. A Prototype for Automating Ontology Learning and Ontology Evolution 2013.

[4]
Li G, Ooi BC, Feng J, Wang J, Zhou L. Ease: an effective 3-in-1 keyword search method for unstructured, semistructured and structured data. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data. 903-14. 

[5]
Babcock S, Beverley J, Cowell LG, Smith B. The infectious disease ontology in the age of COVID-19. J Biomed Semantics  2021; 12(1): 13.
 [http://dx.doi.org/10.1186/s13326-021-00245-1] [PMID: 34275487]

[6]
He Y, Yu H, Ong E, et al. Cido: The community-based coronavirus infectious disease ontology. 2021.

[7]
Xiao Y, Zheng X, Song W, et al. CIDO-COVID-19: An Ontology for COVID-19 Based on CIDO. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2119-22.

[8]
Patel A, Debnath NC, Mishra AK, Jain S. Covid19-IBO: a Covid-19 impact on Indian banking ontology along with an efficient sche-ma matching approach. New Gener Comput  2021; 39(3-4): 647-76.
 [http://dx.doi.org/10.1007/s00354-021-00136-0] [PMID: 34667368]

[9]
Beverley J, Babcock S, Cowell L, Smith B. The COVID 19 Infectious Disease Ontology. 2021.

[10]
Broekstra J, Klein M, Decker S, Fensel D, Van Harmelen F, Horrocks I. Enabling knowledge representation on the web by extending RDF schema. Proceedings of the 10th international conference on World Wide Web.  467-78.
 [http://dx.doi.org/10.1145/371920.372105]

[11]
Ozturk O. OPPCAT: Ontology population from tabular data. J Inf Sci  2020; 46(2): 161-75.
 [http://dx.doi.org/10.1177/0165551519827892]

[12]
Patel A, Jain S. A partition based framework for large scale ontology matching. Recent Pat Eng  2021; 14(3): 488-501.
 [http://dx.doi.org/10.2174/1872212113666190211141415]

[13]
Farquhar A, Fikes R, Pratt W, Rice J. Collaborative ontology construction for information integration In: Technical Report KSL-95-63 Stanford University Knowledge Systems Laboratory 1995.

[14]
Pinto HS, Staab S, Tempich C. DILIGENT: Towards a fine-grained methodology for DIstributed, Looselycontrolled and evolvInG Engineering of oNTologies. In: ECAI. 2004; 16: p. 393.

[15]
Fernández-López M, Gómez-Pérez A, Juristo N. Methontology: from ontological art towards ontological engineering  1997; 33-40.

[16]
Salomoni V, Majorana C, Cristani M. Knowledge representation in civil and structural engineering. Recent Pat Comput Sci  2008; 1(3): 162-81.
 [http://dx.doi.org/10.2174/2213275910801030162]

[17]
Doan A, Ramakrishnan R, Vaithyanathan S. Managing information extraction: state of the art and research directions. In Proceedings of the 2006 ACM SIGMOD international conference on Management of data. 799-800. Proceedings of the 
 [http://dx.doi.org/10.1145/1142473.1142595]

[18]
Freitag D, McCallum A. Information extraction with HMMs and shrinkage. In Proceedings of the AAAI-99 workshop on machine learning for information extraction.  31-6.

[19]
Al-Moslmi T, Ocaña MG, Opdahl AL, Veres C. Named entity extraction for knowledge graphs: A literature overview IEEE Access  2020; 8: 32862-81.
 [http://dx.doi.org/10.1109/ACCESS.2020.2973928]

[20]
Mirończuk MM. Information extraction system for transforming unstructured text data in fire reports into structured forms: a Polish case study. Fire Technol  2020; 56(2): 545-81.
 [http://dx.doi.org/10.1007/s10694-019-00891-z]

[21]
Ding L, Finin T, Joshi A, et al. Swoogle: a search and metadata engine for the semantic web.  In  Proceedings of the thirteenth ACM international conference on Information and knowledge management.  652-9.
 [http://dx.doi.org/10.1145/1031171.1031289]

[22]
Lei Y, Uren V, Motta E. Semsearch: A search engine for the semantic web. In: International conference on knowledge engineering and knowledge management. Berlin, Heidelberg: Springer 2006; 238-45.
 [http://dx.doi.org/10.1007/11891451_22]

[23]
Gruber TR. A translation approach to portable ontology specifications. Knowl Acquis  1993; 5(2): 199-220.
 [http://dx.doi.org/10.1006/knac.1993.1008]

[24]
Noy NF, Crubézy M, Fergerson RW, et al. Protégé-2000: an open-source ontology-development and knowledge-acquisition environ-ment. AMIA Symposium.  953-3.

[25]
Pietriga E.  Isaviz: A visual authoring tool for rdf 2003. Available from: http://www.w3.org/2001/11/IsaViz

[26]
Apollo CH.  Manual, Apollo 2004. Available from: http://apollo.open.ac.uk/index.html

[27]
Kalyanpur A, Parsia B, Sirin E, Grau BC, Hendler J. Swoop: A web ontology editing browser. J Web Semant  2006; 4(2): 144-53.
 [http://dx.doi.org/10.1016/j.websem.2005.10.001]

[28]
Kapoor B, Sharma S. A comparative study ontology building tools for semantic web applications. Int J Web Semantic Technol  2010; 1(3): 1-13.
 [http://dx.doi.org/10.5121/ijwest.2010.1301]

[29]
Dash S, Chakravarty S, Mohanty SN, Pattanaik CR, Jain S. A deep learning method to forecast COVID-19 outbreak. New Gener Comput  2021; 39(3-4): 515-39.
 [http://dx.doi.org/10.1007/s00354-021-00129-z] [PMID: 34305259]

[30]
Aussenac-Gilles N, Despres S, Szulman S. The TERMINAE Method and Platform for Ontology Engineering from Texts  2008; pp. 199-23.

[31]
Liang H, Sun X, Sun Y, Gao Y. Text feature extraction based on deep learning: a review. EURASIP J Wirel Commun Netw  2017; 2017(1): 211.
 [http://dx.doi.org/10.1186/s13638-017-0993-1] [PMID: 29263717]

[32]
Ibrahim Z, Noah SA, Noor MM. Rules for ontology population from text of Malaysia medicinal herbs domain nternational Conference on Rough Sets and Knowledge Technology 386-94 
 [http://dx.doi.org/10.1007/978-3-642-16248-0_55]

[33]
Finkelstein-Landau M, Morin E. Extracting semantic relationships between terms: Supervised vs. unsupervised methods. Interna-tional Workshop on Ontological Engineering on the Global Information Infrastructure.  71-80.

[34]
Makki J, Alquier AM, Prince Vs. Int J Humanit Soc Sci  2009; 3(3): 212-7.

[35]
Eftimov T, Koroušić Seljak B, Korošec P. A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations. PLoS One  2017; 12(6)e0179488
 [http://dx.doi.org/10.1371/journal.pone.0179488] [PMID: 28644863]

[36]
Jain S, Sharma S, Natterbrede JM, Hamada M. Rule-based actionable intelligence for disaster situation management. Int J Knowl Syst Sci  2020; 11(3): 17-32.
 [http://dx.doi.org/10.4018/IJKSS.2020070102]

[37]
Crowder RM, Wilson ML, Fowler D, Shadbolt N, Wills G, Wong S. Navigation over a large ontology for industrial web applications. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Vol. 48999: 1333-40. 
 [http://dx.doi.org/10.1115/DETC2009-86544]

[38]
Celjuska D, Vargas-Vera M. Ontosophie: A semi-automatic system for ontology population from text. In  International Conference on Natural Language Processing (ICON).  60

[39]
Ruiz-Martínez JM, Valencia-García R, Martínez-Béjar R, Hoffmann A. BioOntoVerb: A top level ontology based framework to popu-late biomedical ontologies from texts. Knowl Base Syst  2012; 36: 68-80.
 [http://dx.doi.org/10.1016/j.knosys.2012.06.002]

[40]
Tanev H, Magnini B. Weakly supervised approaches for ontology population. In 11th Conference of the European Chapter of the Association for Computational Linguistics. 2006; pp 17-24.

[41]
Jiang M, Chen Y, Liu M, et al. A study of machine-learning-based approaches to extract clinical entities and their assertions from dis-charge summaries. J Am Med Inform Assoc  2011; 18(5): 601-6.
 [http://dx.doi.org/10.1136/amiajnl-2011-000163] [PMID: 21508414]

[42]
Magnini B, Pianta E, Popescu O, Speranza M. Ontology population from textual mentions: Task definition and benchmark. In Proceedings of the 2nd Workshop on Ontology Learning and Population: Bridging the Gap between Text and Knowledge. 26-32. 

[43]
Yoon HG, Han YJ, Park SB, Park SY. Ontology population from unstructured and semi-structured texts Sixth International Conference on Advanced Language Processing and Web Information Technology (ALPIT 2007) 135-9 
 [http://dx.doi.org/10.1109/ALPIT.2007.30]

[44]
Maynard D, Li Y, Peters W. NLP Techniques for Term Extraction and Ontology Population 

[45]
Patel C, Supekar K, Lee Y. OntoGenie: Extracting ontology instances from WWW Human Language Technology for the Semantic Web and Web Services. ISWC 2003.

[46]
Holzinger W, Krüpl B, Herzog M. Using ontologies for extracting product features from web pages. In: International semantic web conference. Berlin, Heidelberg: Springer 2006; pp. 286-99.
 [http://dx.doi.org/10.1007/11926078_21]

[47]
Embley DW, Tao C, Liddle SW. Automatically extracting ontologically specified data from HTML tables of unknown structure. In  International Conference on Conceptual Modeling.  Berlin, Heidelberg. 2002; pp. 322-7.
 [http://dx.doi.org/10.1007/3-540-45816-6_32]

[48]
Buitelaar P, Cimiano P, Frank A, Racioppa S. SOBA: Smartweb ontology-based annotation. In  Demo Session at the International Semantic Web Conference (ISWC). 

[49]
Hurst M. Layout and language: Challenges for table understanding on the web. In  Proceedings of the International Workshop on Web Document Analysis.  27-30.

[50]
Uschold M, Gruninger M. Ontologies: principles, methods and applications. Knowl Eng Rev  1996; 11(2): 93-136.
 [http://dx.doi.org/10.1017/S0269888900007797]

[51]
Bauer F, Kaltenböck M. Linked open data: The essentials Edition mono/monochrom. Vienna 2011; 710.

[52]
Auer S, Bryl V, Tramp S, Eds. Linked Open Data-- Creating Knowledge Out of Interlinked Data: Results of the LOD2 Project. Springer 2014.
 [http://dx.doi.org/10.1007/978-3-319-09846-3]

[53]
Sharma S, Jain S. Comprehensive Study of Semantic Annotation: Variant and Praxis. In: Advances in Computational Intelligence, its Concepts & Applications (ACI). 2021; pp. 102-16.

[54]
Miller S, Guinness J, Zamanian A. Name tagging with word clusters and discriminative training. In: In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004. 2004; pp. 337- 42.

[55]
Makki J. OntoPRiMa: A prototype for automating ontology population. (IJWesT) 2017; 8(4): 1-11.
 [http://dx.doi.org/10.5121/ijwest.2017.8401]

[56]
Lamy JB. Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies. Artif Intell Med  2017; 80: 11-28.
 [http://dx.doi.org/10.1016/j.artmed.2017.07.002] [PMID: 28818520]

[57]
Nederstigt LJ, Aanen SS, Vandic D, Frasincar F. FLOPPIES: a framework for large-scale ontology population of product information from tabular data in E-commerce stores. Decis Support Syst  2014; 59: 296-311.
 [http://dx.doi.org/10.1016/j.dss.2014.01.001]

[58]
Ghawi R, Cullot N. Database-to-ontology mapping generation for semantic interoperability. Third international workshop on database interoperability (InterDB 2007). 91 

[59]
Stojanovic L, Stojanovic N, Volz R. Migrating data-intensive web sites into the semantic web. In Proceedings of the 2002 ACM symposium on Applied computing. 1100-7. 
 [http://dx.doi.org/10.1145/508791.509008]

Cite As

Current Materials Science

The Semantics of COVID-19 Web Data: Ontology Learning and Population

Abstract

Graphical Abstract