The Interactive Behavior in the Relation between Simple and Complex Structure of Concept and Semantic Relations in an Agricultural Ontology (VocBench)

Maziar Amirhosseini

Abstract


The purpose of this empirical quantitative study is the measurement and evaluation of the relations between structural domains, including simple and complex structure of concepts and semantic relations. Our scientific guess is that there is a significant relation between the structure of concepts and the number of semantic relations. Moreover, there is the lack of investigation on assessing the behavioral interaction between structural domains to improve information retrieval (IR (performance for achieving cognitive results to generate theoretical argument. The mix-method of deductive and inductive approach is adapted in operating the research methodology, especially for data collection. The research data is selected from a complex and authoritative agricultural ontology (i.e., VocBench). Sample size out of 40000 concepts is around 1500 concepts, which were collected via stratified random sampling. The data analysis results were derived from SPSS and Excel software which employed proportional and inferential analysis. The expected relation is that an increase in the numbers of simple concepts causes the increase of semantic relations and vice versa.


Keywords


Ontology evaluation; Knowledge Organization Standards; Structural analysis; Concept structure; Semantic relations; Agricultural ontology (VocBench)

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References


Airio, E. (2006). Word normalization and decompounding in mono- and bilingual IR. Information Retrieval, 9 (3), 249–271.

Aitchison, J, Gilchrist, A & Bawden, D. (2000). Thesaurus construction and use: a practical manual. Fourth edition. London: The Association for Information Management (Aslib).

Alani, H. and Brewster, C. (2005). Ontology Ranking Based on The Analysis of Concept Structures. In Proc. of the 3rd International Conference on Knowledge Capture (K-Cap), Banff, Canada, 2005, 51–58.

Alghamdi, S. M., Sundberg, B. A., Sundberg, J. P., Schofield, P. N. and Hoehndorf, R. (2019). Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies. Scientific Reports, 9 (4025), 1-12.

Amirhosseini, M. (2007). Qualitative and quantitative evaluation of effective factors in information storage and retrieval in Persian thesaurus. Dissertation, Ph.D., Shiraz University, Library and information science department.

Amirhosseini, M. (2010). Theoretical base of quantitative evaluation of unity in thesaurus terms network: Base on Kant's epistemology. Knowledge Organization, 37 (3), 185-202.

Amirhosseini. M. (2016). Analysis of concept structure and semantic relations based on graph-independent structural analysis. Ph. D. Dissertation. Faculty of Information Sciences and Technology, Universiti Kebangsaan Malaysia, 388 p.

Amirhosseini, M. and Salim, J. (2010). Quantitative Evaluation of Simplicity Invisible Domain in Islamic Knowledge Organizations. In 2010 International Conference on Information Retrieval and Knowledge Management: CAMP 10, exploring the invisible word (17-18 March, 2010, Shah Alam, Malaysia). Institute of Electrical and Electronics Engineers, 119-124.

Amirhosseini, M. and Salim, J. (2011). OntoAbsolute as an Ontology Evaluation Methodology in Analysis of the Structural Domains in Upper, Middle and Lower Level Ontologies. In STAIR'11: International Conference on Semantic Technology and Information Retrieval28th to 29th June 2011, Putrajaya, Kuala Lumpur, Malaysia. Malaysia: Institute of Electrical and Electronics Engineers, 2011, 26-33.

Amirhosseini, M. and Salim, J. (2015). Quantitative evaluation of the movement from complexity toward simplicity in the structure of thesaurus descriptors. Malaysian Journal of Library & Information Science, 20 (3), 47-62.

Amirhosseini, M. and Salim, J. (2019). A Synthesis Survey of Ontology Evaluation Tools, Applications and Methods to Propose a Novel Branch in Evaluating the Structure of Ontologies: Graph-Independent Approach. International Journal of Computer, 33 (1), 46-68.

Amirhosseini, M. and Salim, J. (2019). Structural Analysis of Semantic Relations regarding Integration and Association of Semantic Network in VocBench as an Agricultural Ontology. International Journal of Engineering Technology and Management Research, 6 (3), 41-57.

Amith, M. and Tao, C. (2017). Modulated evaluation metrics for drug-based ontologies. Journal of Biomedical Semantics, 8 (17), 45-66.

Assal, H., Pohl, K. and Pohl, J. (2009). The Representation of Context in Computer Software, In Pre-Conference Proceedings, Focus Symposium on Knowledge Management Systems, InterSymp-2009, Baden-Baden, Germany, 4 August, 2009.

Blomqvist, E. and Ohgren, A. (2008). Constructing an enterprise ontology for an automotive supplier. Engineering Applications of Artificial Intelligence, 21, 386–397.

Braschler, M. and Ripplinger, B. (2004). How Effective is Stemming and Decompounding for German Text Retrieval?, Journal of Information Retrieval 7(34), 291-316.

Brewster, C., Alani, H., Dasmahapatra, S. & Wilks, Y. (2004). Data driven ontology evaluation. In Proc. of the 4th International Conference on Language Resources and Evaluation (LREC), Lisbon, Portugal, 2004. European Language Resources Association.

British Standards Institution. (1979) BS 5723:1979: Guidelines for the establishment and development of monolingual thesauri. London: British Standards Institution.

British Standards Institution. (1985) BS 6723:1985. Guide to establishment and development of multilingual thesauri. London: British Standards Institution.

British Standards Institution. (2005-8). BS 8723: Structured vocabularies for information retrieval - Guide. (Published in five separate parts between 2005 and 2008). London: British Standards Institution.

Burton-Jones, A., Storey, V. C., Sugumaran, V. and Ahluwalia, P. (2003). Assessing the Effectiveness of the DAML Ontologies for the Semantic. In Proc. of the 8th International Conference on Applications of Natural Language to Information Systems, Burg (Spreewald), Germany, 56-69.

Calbimonte, J. P., García-Castro, R. and Corcho, O. (2011). Evaluation of the Ontology-based Data Integration Service and the Ontologies. SemSorGrid4Env

Chmielewski M. and Stpor P. (2016). Medical Data Unification Using Ontology-Based Semantic Model Structural Analysis. witek J., Borzemski L., Grzech A., Wilimowska Z. (eds) In Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology – ISAT 2015 – Part III. Advances in Intelligent Systems and Computing, 431. Springer, Cham.

Chmielewski, M., Paciorkowska, M. and Kiedrowicz, M. (2017). Ontology similarity assessment based on lexical and structural model features extraction. Transactions on Information Science and Applications, 14, 134-144.

Chmielewski, M. and Stąpor, P. (2018). Hidden information retrieval and evaluation method and tools utilising ontology reasoning applied for financial fraud analysis. In 22nd International Conference on Circuits, Systems, Communications and Computers, MATEC Web (CSCC 2018). Majorca, Spain, July 14-17, 2018. Available at: https://doi.org/10.1051/matecconf/201821002019

Dextre Clarke, Stella G. 2011. ISO 25964: a standard in support of KOS interoperability. Emerald Group Publishing.

Dividino, R., Romanelli, M. and Sonntag, D. (2008). Semiotic-based Ontology Evaluation Tool S-OntoEval. In Proc. of the Sixth International Conference on Language Resources and Evaluation LREC'08. Marrakech, Morocco.

Eynard, D., Matteucci, M. and Marfa, F. A (2012). Modular Framework to Learn Seed Ontologies From Test. Semi-Automatic Ontology Development: Processes And Resources. Hershey, PA: Information Science Reference, 2012.

Furletti, B. (2009). Ontology-driven knowledge discovery. IMT Institute for Advanced Studies, Lucca. Italy.

Gomez-Perez A. (1994). Some ideas and examples to evaluate ontologies. KSL, Stanford University.

Gangemi, A., Catenacci, C., Ciaramita, M. and Lehmann, J. (2005). A theoretical framework for ontology evaluation and validation. In Proceedings of SWAP2005.

Gangemi, A., Catenacci, C., Ciaramita, M. and Lehmann, J. (2006). Modelling ontology evaluation and validation. In Proceedings of ESWC2006, Springer.

Hammar, K. (2013). Towards an Ontology Design Pattern Quality Model, Master Thesis, Department of Computer and Information Science, Link¨oping University.

Hedlund, T. (2002). Compounds in dictionary based cross language information retrieval. Information Research 7 (2), 2-7.

His, I. (2005). Analyzing the Conceptual Integrity of Computing Applications through Ontological Excavation and Analysis, dissertation, Ph.D., Georgia Institute of Technology. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.85.3889&rep=rep1&type=pdf

Holst, T. (2014). Structural Analysis of Unknown RDF Datasets via SPARQL Endpoints, Master Thesis in der Fachrichtung Informatik, Frieie Universitat Berlin.

Houston, R. D. (2009). A model of compelled nonuse of information. Dissertation, Ph.D. The Austin: University of Texas at Austin, Faculty of the Graduate School.

International Organization for Standardization (ISO) (1985) ISO 5964:1985: Documentation — Guidelines for the establishment and development of multilingual thesauri. Geneva: International Organization for Standardization

International Standards Organization (ISO) (1986). ISO 2788: Guidelines for the establishment and development of monolingual thesauri (2nd. ed.). Geneva: International Standards Organization.

International Organization for Standardization (ISO) (2007). ISO/IEC 24707: Information technology-Common Logic (CL): a framework for a family of logic-based languages. Geneva: International Organization for Standardization. Available at: http://iso-commonlogic.org.

International Organization for Standardization (ISO) (2011). ISO/FDIS 25964-1: Information and documentation -thesauri and interoperability with other vocabularies - Part 1: Thesauri for information retrieval. Geneva: International Organization for Standardization; Final Draft circulated April 2011.

International Organization for Standardization (ISO) (2013). ISO/FDIS 25964-2: Information and documentation -thesauri and interoperability with other vocabularies - Part 2: Part 2: Interoperability with other vocabularies. Geneva: International Organization for Standardization; Final Draft circulated April 2011.

Jain, S. and Meyer, V. (2018). Evaluation and Refinement of Emergency Situation Ontology. International Journal of Information and Education Technology, 8 (10), 713-719.

Jiratthitikul, P., Nithisansawadikul, S., Tongphu, S. and Suntisrivaraporn, B. (2014) A similarity measuring service for SNOMED-CT: Structural analysis of concepts in ontology. In 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). Available at: https://ieeexplore.ieee.org/document/6839771

Kang, D., Xu, B., Lu, J. and Chu, W. (2004). A Complexity Measure for Ontology Based on UML. In Proceedings of the 10th IEEE International Workshop on Future Trends of Distributed Computing Systems (FTDCS’04 Suzhou, China), 222-228.

Kochen, Ma. and Tagliacozzo, R. (1968). A study of cross-referencing. Journal of documentation, 24, 173-91.

Krejcie, R. V., and Morgan, D. W. 1970. Determining sample size for research activities. Educational and Psychological Measurement, 30, 607-610.

Lancaster, F. W. (1986). Vocabulary control for information retrieval. Virginia: Information Resource Press.

Lazarinis, F., Vilares, J., Tait, J. and Efthimiadis, E.N. (2009). Current research issues and trends in non-English Web searching. Information Retrieval, 12 (3), 230-250.

Leveling, J., Magdy, W. & Jones, G. J.F. (2011). An investigation of decompounding for cross-language patent search. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information, July 24-28. Beijing, China, 1169-1170.

Liu, X., Barnaghi, P., Moessner, K. and Liao, J. (2010). Using concept and structure similarities for ontology integration. In Proc. of the 5th International Workshop on Ontology Matching (OM-2010), Shanghai, China, November 7, 2010.

Martín Chozas, P. (2018). Towards a Linked Open Data Cloud of Language Resources in the Legal Domain. Master Thesis, E.T.S. de Ingenieros Informáticos, Universidad Politécnica de Madrid (UPM), 2018.

Mayr, P., Petras, V. and Walter, A. (2007). Results from a German terminology mapping effort: intra- and interdisciplinary cross-concordances between controlled vocabularies. Available at:

Monz, C. and de Rijke, M. (2002). Shallow morphological analysis in monolingual retrieval for Dutch, German, and Italian. Accessing Multilingual Information Repositories, vol. 2406 of Lecture Notes in Computer Science, 262-277.

Mungall, C. (2005). Increased complexity in the GO. Available at: http://www.fruitfly.org/~cjm/obol/doc/go- complexity.html

Muñoz, A. (1997). Compound Key Word Generation from Document Databases Using a Hierarchical Clustering Art Model. Working paper (Universidad Carlos III de Madrid.Departamento de Estadística y Econometría), 96 (76).

National Information Standards Organization (NISO) (2005). Guidelines for the construction, format, and management of monolingual controlled vocabularies: ANSI/NISO Z39.19-2005, Bethesda Md., NISO Press.

Navigli, R., Velardi, P., Cucchiarelli, A. and Neri, F. (2004). Quantitative and Qualitative Evaluation of the OntoLearn Ontology Learning System. In proceeding of the ECAI 2004 Workshop on Ontology Learning and Population. Valencia, Spain, August 2004.

Obrst, L., Ashpole, B., Ceusters, W., Mani, I., Steve, R. and Smith, B. (2007). The evaluation of ontologies: Toward improved semantic interoperability. Semantic Web, Berlin: Springer, 139-158.

Park, J., Cho, W. & Rho, S. (2007). Evaluation Framework for Automatic Ontology Extraction Tools: An Experiment. On the Move to Meaningful Internet Systems 2007: OTM 2007 Workshops, Berlin, Heidelberg: Springer, 511-521.

Pohlmann, R. and Kraaij, W. (1997). The effect of syntactic phrase indexing on retrieval performance for Dutch texts. In Proceedings of RIAO 97. 176–187.

Rogers, JE. (2006). Quality assurance of medical ontologies. Methods Inf Med , 45 (3), 267-74.

Soergel, D., Lauser, B., Liang, A., Fisseha, F., Keizer, J. and Katz, S. (2004). Reengineering Thesauri for New Applications: the AGROVOC Example. Journal of Digital Information, 4 (4).

Sabou, M. (2007). Methods for Selection and Integration of Reusable Components from Formal or Informal User Specifications, Open University (OU), 2007. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?rep=rep1&type=pdf&doi=10.1.1.122.8144

Stellato, A. (2015). Collaborative Development of Multilingual Thesauri with Vocbench (System Description and Demonstrator). In The Semantic Web: ESWC 2015 Satellite Events, Portorož, Slovenia, May 31 – June 4, 2015, Cham: Springer International Publishing, 149–153.

TESE - Thesaurus for Education Systems in Europe. 2006. Available at: http://www.eurydice.org/ressources/Eurydice/TESE/pdf/TESEEN_002_intro.pdf

Velardi, P., Navigli, R., Cucchiarelli, A., Neri, F., Buitelaar, P., Cimiano, P. and Magnini, B. (ed.) (2005). Evaluation of OntoLearn, a methodology for automatic learning of domain ontologies. Ontology Learning from Text: Methods, Evaluation and Applications. IOS Press.

Villalon, M. P. (2016). Ontology Evaluation: a pitfall-based approach to ontology diagnosis. Ph.D. Thesis. E.T.S. de Ingenieros Informáticos (UPM), Department Inteligencia Artificial.

Vrandecic, D. (2010). Ontology Evaluation. PhD thesis, Karlsruher Instituts für Technologie (KIT).

Yang, Z., Zhang, D. and Ye, C. (2006). Ontology Analysis on Complexity and Evolution Based on Conceptual Model. U. Leser, F. Naumann, and B. Eckman (Eds.): DILS 2006, LNBI 4075. 216-223.

Yves, J. (2011). VocBench: Vocabulary Editing and Workflow Management. In SemTech, 2011: The Semantic technology conference. Available at: http://semtech2011.semanticweb.com/uploads/handouts/MON_600_Jaques_3910.pdf

Xian, G. and Zhao, R. A (2012). Review and Prospects on Collaborative Ontology Editing Tools. Journal of Integrative Agriculture, 11 (5), 731-740.

Xamena E., Brignole N. B., and Maguitman A. (2017). Structural Analysis of topic ontologies. Information Sciences. 421, 15–29. doi: https://doi.org/10.1016/j.ins.2017.08.081

Zhang, D., Ye, C. and Yang, Z. (2006). An Evaluation Method for Ontology Complexity Analysis in Ontology Evolution. S. Staab and V. Svatek (Eds.). EKAW 2006, LNAI 4248, International Conference on Knowledge Engineering and Knowledge Management No15, Podebrady , TCHEQUE, REPUBLIQUE. 4248, 214-221.

Zhanga, G. Q, Xingd, G. and Cuia,L. (2018) An efficient, large-scale, non-lattice-detection algorithm for exhaustive structural auditing of biomedical ontologies. Journal of Biomedical Informatics, 80, 106–119


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