[논문]GOMME: A Generic Ontology Modelling Methodology for Epics

Udaya Varadarajan; Mayukh Bagchi; Amit Tiwari; M.P. Satija

doi:10.1633/jistap.2023.11.1.5

GOMME: A Generic Ontology Modelling Methodology for Epics 원문보기

Journal of information science theory and practice : JISTaP, v.11 no.1, 2023년, pp.61 - 78

Udaya Varadarajan (Documentation Research and Training Centre (DRTC), Indian Statistical Institute, Bangalore Centre, Department of Library and Information Science, University of Calcutta) , Mayukh Bagchi (Department of Information Engineering and Computer Science (DISI), University of Trento) , Amit Tiwari (Documentation Research and Training Centre (DRTC), Indian Statistical Institute, Bangalore Centre, Department of Library and Information Science, University of Calcutta) , M.P. Satija (Department of Library and Information Science, GNDU)

Abstract ▼ AI-Helper

Ontological knowledge modelling of epic texts, though being an established research arena backed by concrete multilingual and multicultural works, still suffers from two key shortcomings. Firstly, all epic ontological models developed till date have been designed following ad-hoc methodologies, most often combining existing general purpose ontology development methodologies. Secondly, none of the ad-hoc methodologies consider the potential reuse of existing epic ontological models for enrichment, if available. This paper presents, as a unified solution to the above shortcomings, the design and development of GOMME - the first dedicated methodology for iterative ontological modelling of epics, potentially extensible to works in different research arenas of digital humanities in general. GOMME is grounded in transdisciplinary foundations of canonical norms for epics, knowledge modelling best practices, application satisfiability norms, and cognitive generative questions. It is also the first methodology (in epic modelling but also in general) to be flexible enough to integrate, in practice, the options of knowledge modelling via reuse or from scratch. The feasibility of GOMME is validated via a first brief implementation of ontological modelling of the Indian epic Mahabharata by reusing an existing ontology. The preliminary results are promising, with the GOMME-produced model being both ontologically thorough and competent performance-wise.

주제어

표/그림 (8)

표 Table 1. Selected ontology models for study
그림 Fig. 1. The GOMME methodology. KR, knowledge representation.
그림 Fig. 2. An example of a fragment of a KR template. KR, knowledge representation.
표 Table 2. Sample competency questions
표 Table 3. Satisfactory assessment (via CQs) for MO-IITM
그림 Fig. 3. An example of a relationship matrix.
그림 Fig. 4. Snapshot of MO-GOMME.
표 Table 4. Satisfactory assessment (via CQs) for MO-GOMME

AI 본문요약
AI-Helper

제안 방법

• External enrichment is where an in-depth conceptual familiarization of the epic is collaboratively arrived at and compared with both the ontological commitment modelled in the intermediate ontology and the project requirements. Subsequently, the difference in ontological commitment in terms of classes, object properties, data properties, and other relevant axioms are modelled and integrated in the intermediate ontology.
The assessment of the satisfiability was a curated process in which the coverage of the ontological commitment targeted for MO-GOMME (modelled via CQs as requirements) was examined visà-vis the axiomatization of the ontological commitment formalized in MO-IITM

대상 데이터

Table 4 presents the results from the re-checking exercise. The exercise included 203 questions and after elimination of 21 repeated and 14 descriptive questions, 168 questions were considered for the assessment. The PDT found that the enriched MO-IITM could answer 149 questions in total, i.

성능/효과

e. 49.4% of the questions, that was considerably close to the rough threshold of 50% that the PDT assumed. The PDT decided to proceed with the reuse and enrichment of the MO-IITM.
• The secondary relations among each such character in the epic, thus recording a major proportion of the actions on which the epic is built. A crucial observation is the fact that secondary relations among characters can be potentially many, but the primary definition is only one (all being determined in sync with the objectives of the PDT).
After the completion of the ontological review, the PDT, over six dedicated two-hour sessions, actively engaged to determine the satisfiability of MO-IITM and refine it, if and as necessary. The assessment of the satisfiability was a curated process in which the coverage of the ontological commitment targeted for MO-GOMME (modelled via CQs as requirements) was examined visà-vis the axiomatization of the ontological commitment formalized in MO-IITM.
It illustrates a glimpse of the classes, object properties, data properties, and instances of the enriched Mahabharata ontology MO-GOMME. As a further note, given the stress of the case study on practically implementing GOMME, the PDT did not venture for further iterations of GOMME (which would have obviously required modelling more fine-grained CQs).
around 89% of the CQs, thus exhibiting a major leap from the raw MO-IITM. Based on the t-test of the above sample, the PDT concluded that the curated enrichment is statistically significant. The t-value -5.
It incorporates the statistical summary of the questions asked by the three categories of survey participants: novices, aware, and experts. From all the participants a total of 203 questions were asked out of which 12 questions were descriptive and 15 were repeated. Therefore, 203-(12+15)=176 unique questions were considered for the ontology appraisal process.
The steps in the methodologies often start with the ontology population. Further investigation found that the ontology development methodologies for narrative/literary domains lack an iterative process and that these methodologies are mostly linear. A linear methodology is unidirectional and does not include traversing in a feedback loop, therefore leaving very little space for improvement.
If the reviewed ontology does not satisfy the examination, i.e., case of No, the intermediate ontology fails mostly in its alignment to the ontological commitment as expected from the project requirements. In this case, there are two routes prescribed by the GOMME methodology.
If the reviewed ontology partially satisfies the examination, i.e., in case of needing enrichment, the intermediate ontology is satisfactorily aligned as expected from the project requirements but still needs to be enriched in order to attain maximal coverage. The recommended modes of enrichment are external and internal.
No potential conflict of interest relevant to this article was reported.
Observations from the literature study indicate that the generic ontology development methodologies involve domain identification, term collection, relationship and attributes establishment, representation, and documentation. These methodologies are for ontology development from scratch.
The iteration continues till the ontological commitment of the enriched model maximally converges with that of the project requirements, as deemed appropriate by the PDT. Once the enriched model achieves maximal convergence, it is deemed as a FEKR which is application ready.
The PDT converged on the highlight that there was no dedicated methodology for modelling epics and GOMME was a proposed candidate solution. Secondly, the PDT decided to adopt GOMME not only as it is targeted to model epics, but also to test the intricacies concerning the key features of architectural agility and feasibility of repurposing and extensibility that it highlights.
Firstly, the PDT conducted a survey of major existing knowledge modelling methodologies for epic, narrative, and literary works and also examined the concrete conceptual/knowledge models produced (summarised in Section 2). The PDT converged on the highlight that there was no dedicated methodology for modelling epics and GOMME was a proposed candidate solution. Secondly, the PDT decided to adopt GOMME not only as it is targeted to model epics, but also to test the intricacies concerning the key features of architectural agility and feasibility of repurposing and extensibility that it highlights.
The exercise included 203 questions and after elimination of 21 repeated and 14 descriptive questions, 168 questions were considered for the assessment. The PDT found that the enriched MO-IITM could answer 149 questions in total, i.e. around 89% of the CQs, thus exhibiting a major leap from the raw MO-IITM.
The above-mentioned design concerns are indicative of generic aspects which might be considered in the initial scoping exercise and are completely flexible in allowing added tailored formulations for specific research contexts, for instance, if we extend for other categories of works in the digital humanities. The methodology recommends the involvement of the entire Project Development Team (PDT), by which we mean the team responsible for end-to-end coordination of the GOMME methodology for a particular project, for the initial scoping exercise.
Each of the questions was manually checked against MO-IITM. The results show that the ontology could answer 87 questions, i.e. 49.
1) to consider the suitability of the next ontology as decided by the PDT. The second is, in case this ontology was the single artefact considered in (3.4.1.1), the methodological flow shifts to the conceptual analysis step, which is a crucial step to design a new ontology of the chosen epic as per the expressed ontological commitments.
1. The second outcome is availability of ontologies (NO). This means that no formal ontological representation of the chosen epic is found; in that case we start activity as further detailed in Section 3.
00083, the curated result is significant. Thus, the PDT decided to rechristen the enriched MO-IITM as the outcome (FEKR) of the case study and renamed it MO-GOMME. Fig.

후속연구

1). Further, as a crucial aspect of the flexibility of GOMME, the PDT can venture for further iterations of (suitable fragments of) GOMME with respect to developing a more fine-grained representational model of the FEKR, which would not only entail modelling more fine-grained CQs but will also depend on the practical requirements and updates to the project that the PDT is contributing to.

참고문헌 (72)

Abello, J., Broadwell, P., & Tangherlini, T. R. (2012). Computational folkloristics. Communications of the ACM, 55(7),？60-70. https://doi.org/10.1145/2209249.2209267

상세보기
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., &？Ives, Z. (2007, November 11-15). DBpedia: A nucleus for a？web of open data. In K. Aberer, K. S. Choi, N. Noy, D. Allemang, K. I. Lee, L. Nixon, J. Golbeck, P. Mika, D. Maynard, R.？Mizoguchi, G. Schreiber, & P. Cudre-Mauroux (Eds.), The？Semantic Web: 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference (pp. 722-735).？Springer.
Bagchi, M. (2019). A knowledge architecture using knowledge？graphs. (Master's thesis). Indian Statistical Institute, Bangalore, India.
Bagchi, M. (2020). Conceptualising a library chatbot using？Open source conversational artificial intelligence. DESIDOC Journal of Library & Information Technology, 40(6),？329-333. https://doi.org/10.14429/djlit.40.6.15611

상세보기
Bagchi, M. (2021a). A large scale, knowledge intensive domain？development methodology. Knowledge Organization,？48(1), 8-23. https://doi.org/10.5771/0943-7444-2021-1-8

상세보기
Bagchi, M. (2021b). Towards knowledge organization ecosystem (KOE). Cataloging & Classification Quarterly, 59(8),？740-756. https://doi.org/10.1080/01639374.2021.1998282

상세보기
Bagchi, M., & Madalli, D. P. (2019, February 26-28). Domain？visualization using knowledge cartography in the big data？era: A knowledge graph based alternative. In K. R. Patnaik,？& K. Satpathi (Eds.), International Conference on "Future？of Libraries" (pp. 1-22). Indian Institute of Management.
Berry, D. M. (2012). Introduction: Understanding the digital？humanities. In D. M. Berry (Ed.), Understanding digital？humanities (pp. 1-20). Palgrave Macmillan.
Branch, F., Arias, T., Kennah, J., Phillips, R., Windleharth, T., &？Lee, J. H. (2017). Representing transmedia fictional worlds？through ontology. Journal of the Association for Information Science and Technology, 68(12), 2771-2782. https://doi.org/10.1002/asi.23886

상세보기
Carriero, V. A., Daquino, M., Gangemi, A., Nuzzolese, A. G.,？Peroni, S., Presutti, V., & Tomasi, F. (2020). The landscape？of ontology reuse approaches. In G. Cota, M. Daquino, &？G. L. Pozzato (Eds.), Applications and practices in ontology？design, extraction, and reasoning (pp. 21-38). IOS Press.
Cooren, F. (2015). In medias res: Communication, existence,？and materiality. Communication Research and Practice,？1(4), 307-321. https://doi.org/10.1080/22041451.2015.1110075

상세보기
Damiano, R., & Lieto, A. (2013). Ontological representations？of narratives: A case study on stories and actions. In M. A.？Finlayson, B. Fisseni, B. Lowe, & J. C. Meister (Eds.), 2013？Workshop on Computational Models of Narrative (pp. 76-93). Schloss Dagstuhl.
Damiano, R., Lombardo, V., & Pizzo, A. (2019). The ontology？of drama. Applied Ontology, 14(1), 79-118. https://doi.org/10.3233/AO-190204

상세보기
Das, S., & Roy, S. (2016). Faceted ontological model for brain？tumour study. Knowledge Organization, 43(1), 3-12.？https://doi.org/10.5771/0943-7444-2016-1-3

상세보기
DuCharme, B. (2013). Learning SPARQL: Querying and updating with SPARQL 1.1. O'Reilly.
Dutta, B., Chatterjee, U., & Madalli, D. P. (2015). YAMO: Yet？another methodology for large-scale faceted ontology construction. Journal of Knowledge Management, 19(1), 6-24.？https://doi.org/10.1108/JKM-10-2014-0439

상세보기
Ehrlinger, L., & Woss, W. (2016, September 12-15). Towards a？definition of knowledge graphs. In M. Martin, M. Cuquet,？& E. Folmer (Eds.), SEMANTICS Leipzig 2016. Committee？of SEMANTICS 2016.
Espinoza-Arias, P., Poveda-Villalon, M., Garcia-Castro, R., &？Corcho, O. (2019). Ontological representation of smart？city data: From devices to cities. Applied Sciences, 9(1), 32.？https://doi.org/10.3390/app9010032

상세보기
Fernandez-Lopez, M., Gomez-Perez, A., & Juristo, N. (1997,？March 24-26). Methontology: From ontological art towards？ontological engineering. Proceedings of the Ontological？Engineering AAAI-97 Spring Symposium series (pp. 33-40). AAAI.
Frontini, F., Brando, C., & Ganascia, J. G. (2015, June 1). Semantic web based named entity linking for digital humanities？and heritage texts. In A. Zucker, I. Draelants, C. F. Zucker,？& A. Monnin (Eds.), Proceedings of the First International？Workshop Semantic Web for Scientific Heritage at the 12th？ESWC 2015 Conference (pp. 77-88). HAL Open Science.
Gajbe, S. B., Tiwari, A., Gopalji., & Singh, R. K. (2021). Evaluation and analysis of data management plan tools: A parametric approach. Information Processing & Management,？58(3), 102480. https://doi.org/10.1016/j.ipm.2020.102480

상세보기
Gangemi, A., Guarino, N., Masolo, C., Oltramari, A., & Schneider, L. (2002, October 1-4). Sweetening ontologies with？DOLCE. In A. Gomez-Perez, & V. R. Benjamins (Eds.),？EKAW 2002: Knowledge engineering and knowledge management: ontologies and the semantic web (pp. 166-181).？Springer.
Giunchiglia, F., & Bagchi, M. (2021, September 11-18). Millikan？+ Ranganathan - From perception to classification. In E. M.？Sanfilippo, O. Kutz, N. Troquard, T. Hahmann, C. Masolo,？R. Hoehndorf, & R. Vita (Eds.), CAOS 2021: 5th Workshop？on Cognition And OntologieS. Springer.
Giunchiglia, F., Bocca, S., Fumagalli, M., Bagchi, M., & Zamboni, A. (2021a). iTelos -- purpose driven knowledge？graph generation. arXiv, arXiv:2105.09418. https://doi.org/10.48550/arXiv.2105.09418
Giunchiglia, F., Dutta, B., & Maltese, V. (2014). From knowledge organization to knowledge representation. Knowledge？Organization, 41(1), 44-56. https://doi.org/10.5771/0943-7444-2014-1-44

상세보기
Giunchiglia, F., Zamboni, A., Bagchi, M., & Bocca, S. (2021b,？June 6). Stratified data integration. In D. Chaves-Fraga,？A. Dimou, P. Heyvaert, F. Priyatna, & J. Sequeda (Eds.),？Knowledge Graph Construction 2021. CEUR.
Gold, M. K. (2012). Debates in the digital humanities. University of Minnesota Press.
Gomez-Perez, A. (2004). Ontology evaluation. In S. Staab, &？R. Studer (Eds.), Handbook on ontologies (pp. 251-273).？Springer.
Gomez-Perez, A., Fernandez-Lopez, M., & Corcho, O. (2004).？Ontological Engineering: With examples from the areas of？knowledge management, e-commerce and the semantic？web. Springer.
Greene, T. (1961). The norms of epic. Comparative Literature,？13(3), 193-207. https://doi.org/10.2307/1768997

상세보기
Gruber, T. R. (1995). Toward principles for the design of ontologies used for knowledge sharing? International Journal？of Human-Computer Studies, 43(5-6), 907-928. https://doi.org/10.1006/ijhc.1995.1081

상세보기
Gruninger, M., & Fox, M. S. (1995a). Methodology for the？design and evaluation of ontologies. Paper presented at IJCAI'95, Montreal, Canada.
Gruninger, M., & Fox, M. S. (1995b). The role of competency？questions in enterprise engineering. In A. Rolstadas (Ed.),？Benchmarking - Theory and practice (pp. 22-31). Springer.
Guarino, N., & Welty, C. (2002). Evaluating ontological decisions with Ontoclean. Communications of the ACM, 45(2),？61-65. https://doi.org/10.1145/503124.503150

상세보기
Guarino, N., Oberle, D., & Staab, S. (2009). What is an ontology? In S. Staab, & R. Studer (Eds.), Handbook on ontologies (pp. 1-17). Springer.
Guizzardi, G., Herre, H., & Wagner, G. (2002, October 7-11).？On the general ontological foundations of conceptual？modeling. In S. Spaccapietra, S. T. March, & Y. Kambayashi？(Eds.), Conceptual modeling - ER 2002 (pp. 65-78).？Springer.
Hyvonen, E., Valo, A., Komulainen, V., Seppala, K., Kauppinen,？T., Ruotsalo, T., Salminen, M., & Ylisalmi, A. (2005). Finnish national ontologies for the semantic web -- Towards a？content and service infrastructure. International Conference on Dublin Core and Metadata Applications (pp. 219-222). Dublin Core.
Iqbal, R., Mustapha, A., & Yusoff, Z. M. (2013). An experience？of developing Quran ontology with contextual information？support. Multicultural Education & Technology Journal,？7(4), 333-343. https://doi.org/10.1108/METJ-03-2013-0009

상세보기
Johnson-Laird, P. N. (1983). Mental models: Towards a cognitive science of language, inference, and consciousness. Harvard University Press.
Kent, R. E. (2000, July 10-13). The information flow foundation？for conceptual knowledge organization. In C. Beghtol, L.？Howarth, & N. J. Williamson (Eds.), Dynamism and stability in knowledge organization: Proceedings of the Sixth？International ISKO Conference (pp. 111-117). International？Society for Knowledge Organization.
Khan, A. F., Bellandi, A., Benotto, G., Frontini, F., Giovannetti,？E., & Reboul, M. (2016, September 18-20). Leveraging a？narrative ontology to query a literary text. In B. Miller, A.？Lieto, R. Ronfard, S. G. Ware, & M. A. Finlayson (Eds.),？The 7th Workshop on Computational Models of Narrative？(CMN 2016) (pp. 10:1-10:10). Schloss Dagstuhl.
Klarer, M. (2013). An introduction to literary studies. (3rd ed.).？Routledge.
Knoblock, C. A., Szekely, P., Ambite, J. L., Goel, A., Gupta,？S., Lerman, K., Muslea, M., Taheriyan, M., & Mallick, P.？(2012, May 27-31). Semi-automatically mapping structured？sources into the semantic web. In E. Simperl, P. Cimiano,？A. Polleres, O. Corcho, & V. Presutti (Eds.), ESWC 2012:？The semantic web: research and applications (pp. 375-390).？Springer.
Lassila, O., & McGuinness, D. (2001). The role of frame-based？representation on the semantic web. Linkoping University？Electronic Press.
Lehmann, F. (1992). Semantic networks. Computers & Mathematics with Applications, 23(2-5), 1-50. https://doi.org/10.1016/0898-1221(92)90135-5

상세보기
Lochtefeld, J. G. (2002). The illustrated encyclopedia of hinduism. Rosen Publishing.
Mahesh, K., Helmreich, S., & Wilson, L. (1996). Ontology development for machine translation: Ideology and methodology. New Mexico State University.
Masolo, C., Vieu, L., Bottazzi, E., Catenacci, C., Ferrario, R.,？Gangemi, A., & Guarino, N. (2004, June 2-5). Social roles？and their descriptions. In D. Dubois, C. Welty, & M. A. Williams (Eds.), Proceedings of the 9th International Conference on the Principles of Knowledge Representation and？Reasoning (pp. 267-277). AAAI Press.
Mulholland, P., Collins, T., & Zdrahal, Z. (2004, January 13-16).？Story fountain: Intelligent support for story research and？exploration. In J. Vanderdonckt (Ed.), Proceedings of the？9th International Conference on Intelligent User Interfaces？(pp. 62-69). ACM.
Nakasone, A., & Ishizuka, M. (2006, December 18-22). Storytelling ontology model using RST. In T. Nishida (Ed.), 2006？IEEE/WIC/ACM International Conference on Intelligent？Agent Technology (pp. 163-169). IEEE.
Noy, N. F., & McGuinness, D. L. (2001). Ontology development？101: A guide to creating your first ontology. Stanford University.
Parsons, V. (2014). Stratified sampling. In N. Balakrishnan, T.？Colton, B. Everitt, W. Piegorsch, F. Ruggeri, & J. Teugels？(Eds.), Wiley StatsRef: Statistics reference online. John Wiley & Sons.
Phoksawat, K., Mahmuddin, M., & Ta'a, A. (2019). Intercropping in rubber plantation ontology for a decision support？system. Journal of Information Science Theory and Practice, 7(4), 56-64. https://doi.org/10.1633/JISTaP.2019.7.4.5

원문보기 상세보기
Pinto, H. S., & Martins, J. P. (2000, March 20-22). Reusing？ontologies. AAAI 2000 Spring Symposium on Bringing？Knowledge to Business Processes (pp. 77-84). AAAI.
Plirdpring, P., & Ruangrajitpakorn, T. (2022). Using ontology to？represent cultural aspects of local products for supporting？local community enterprise in Thailand. Journal of Information Science Theory and Practice, 10(1), 45-58. https://doi.org/10.1633/JISTaP.2022.10.1.4

원문보기 상세보기
Rajagopalachari, C. (1970). Mahabharata. Diamond Pocket？Books.
Ranganathan, S. R. (1967). Prolegomena to library classification. (3rd ed.). Ess Ess Publications.
Satija, M. P. (2017). Colon Classification (CC). Knowledge Organization, 44(4), 291-307. https://doi.org/10.5771/0943-7444-2017-4-291
Satija, M. P., Bagchi, M., & Martinez-Avila, D. (2020). Metadata？management and application. Library Herald, 58(4), 84-107. https://doi.org/10.5958/0976-2469.2020.00030.2
Simperl, E. (2009). Reusing ontologies on the semantic web: A？feasibility study. Data & Knowledge Engineering, 68(10),？905-925. https://doi.org/10.1016/j.datak.2009.02.002

상세보기
Suarez-Figueroa, M. C., Gomez-Perez, A., & Fernandez-Lopez,？M. (2015). The NeOn methodology framework: A scenario-based methodology for ontology development. Applied？Ontology, 10(2), 107-145. https://doi.org/10.3233/AO-150145

상세보기
Swartjes, I., & Theune, M. (2006). A fabula model for emergent narrative. In S. Gobel, R. Malkewitz, & I. Iurgel (Eds.),？Technologies for interactive digital storytelling and entertainment (pp. 49-60). Springer.
Syamili, C., & Rekha, R. V. (2018). Developing an ontology？for Greek mythology. Electronic Library, 36(1), 119-132.？https://doi.org/10.1108/EL-02-2017-0030

상세보기
Tiwari, A., & Madalli, D. P. (2021). Maturity models in LIS？study and practice. Library & Information Science Research,？43(1), 101069. https://doi.org/10.1016/j.lisr.2020.101069

상세보기
Uschold, M., & King, M. (1995). Towards a methodology for？building ontologies. University of Edinburgh.
Varadarajan, U., & Dutta, B. (2021a, September 18-20). Towards development of knowledge graph for narrative？information in medicine. In B. Villazon-Terrazas, F. Ortiz-Rodriguez, S. Tiwari, A. Goyal, & M. Jabbar (Eds.), KGSWC 2021: Knowledge graphs and semantic web (pp. 290-307). Springer.
Varadarajan, U., & Dutta, B. (2021b). Models for narrative？information: A study. arXiv, arXiv:2110.02084. https://doi.org/10.48550/arXiv.2110.02084
Vosniadou, S. (2002). Mental models in conceptual development. In L. Magnani, & N. J. Nersessian (Eds.), Modelbased reasoning: Science, technology, values (pp. 353-368).？Springer.
Vrandecic, D., & Krotzsch, M. (2014). Wikidata: A free collaborative knowledge base. Communications of the ACM,？57(10), 78-85. https://doi.org/10.1145/2629489

상세보기
Vrandecic, D., Pinto, S., Tempich, C., & Sure, Y. (2005). The？DILIGENT knowledge processes. Journal of Knowledge Management, 9(5), 85-96. https://doi.org/10.1108/13673270510622474

상세보기
Yeh, J. H. (2017, May 13-17). StoryTeller: An event-based story？ontology composition system for biographical history. In T.？H. Meen, S. D. Prior, & A. D. K. T. Lam (Eds.), Proceeding？of the 2017 International Conference on Applied System？Innovation (ICASI) (pp. 1934-1937). IEEE.
Zaihrayeu, I., Sun, L., Giunchiglia, F., Pan, W., Ju, Q., Chi, M., &？Huang, X. (2007, November 11-15). From web directories？to ontologies: Natural language processing challenges. In K.？Aberer, K. S. Choi, N. Noy, D. Allemang, K. I. Lee, L. Nixon, J.？Golbeck, P. Mika, D. Maynard, R. Mizoguchi, G. Schreiber,？& P. Cudre-Mauroux (Eds.), The semantic web: 6th International Semantic Web Conference, 2nd Asian Semantic？Web Conference (pp. 623-636). Springer.

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증