Have a personal or library account? Click to login
A Method for Extending Ontologies with Application to the Materials Science Domain Cover

A Method for Extending Ontologies with Application to the Materials Science Domain

Open Access
|Oct 2019

References

  1. 1Agrawal, A and Choudhary, A. 2016. Perspective: materials informatics and big data: realization of the Fourth paradigm of science in materials science. APL Materials, 4: 053208, 1–10. DOI: 10.1063/1.4946894
  2. 2Arnold, P and Rahm, E. 2013. Semantic enrichment of ontology mappings: A linguisticbased approach. In: Catania, B, Guerrini, G and Pokorny, J (eds.), 17th East European Conference on Advances in Databases and Information Systems, 4255. DOI: 10.1007/978-3-642-40683-6
  3. 3Arp, R, Smith, B and Spear, AD. 2015. Building Ontologies with Basic Formal Ontology. The MIT Press. DOI: 10.7551/mitpress/9780262527811.001.0001
  4. 4Ashino, T. 2010. Materials Ontology: An Infrastructure for Exchanging Materials Information and Knowledge. Data Science Journal, 9: 5461. DOI: 10.2481/dsj.008-041
  5. 5Asim, MN, Wasim, M, Khan, MUG, Mahmood, W and Abbasi, HM. 2018. A survey of ontology learning techniques and applications. Database, 2018: bay101, 1–24. DOI: 10.1093/database/bay101
  6. 6Austin, T. 2016. Towards a digital infrastructure for engineering materials data. Materials Discovery, 3: 112. DOI: 10.1016/j.md.2015.12.003
  7. 7Baader, F, Calvanese, D, McGuinness, DL, Nardi, D and Patel-Schneider, PF. 2010. The Description Logic Handbook: Theory, Implementation and Applications. 2nd edn. Cambridge University Press.
  8. 8Bhat, M, Shah, S, Das, P, Kumar, P, Kulkarni, N, Ghaisas, SS and Reddy, SS. 2013. Premlp: knowledge driven design of materials and engineering process. ICoRD’13, 13151329. Springer. DOI: 10.1007/978-81-322-1050-4_105
  9. 9Buitelaar, P, Cimiano, P and Magnini, B. 2005. Ontology Learning from Text: Methods, Evaluation and Applications. IOS Press.
  10. 10Cheng, X, Hu, C and Li, Y. 2014. A semantic-driven knowledge representation model for the materials engineering application. Data Science Journal, 13: 2644. DOI: 10.2481/dsj.13-061
  11. 11Cheung, K, Drennan, J and Hunter, J. 2008. Towards an Ontology for Data-driven Discovery of New Materials. Semantic Scientific Knowledge Integration AAAI/SSS Workshop, 914.
  12. 12Cimiano, P, Hotho, A and Staab, S. 2005. Learning concept hierarchies from text corpora using formal concept analysis. Journal of Artificial Intelligence Research, 24: 305339. DOI: 10.1613/jair.1648
  13. 13Cimiano, P and Völker, J. 2005. Text2Onto. In: Montoyo, A, Muñoz, R and Métais, E (eds.), Natural Language Processing and Information Systems, 10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005, Alicante, Spain, June 15–17, 2005, Proceedings, 227238.
  14. 14de Matos, P, Dekker, A, Ennis, M, Hastings, J, Haug, K, Turner, S and Steinbeck, C. 2010. ChEBI: a chemistry ontology and database. Journal of cheminformatics, 2(S1): P6, 1. DOI: 10.1186/1758-2946-2-S1-P6
  15. 15Dos Reis, J, Dinh, D, Pruski, C, Da Silveira, M and Reynaud-Delaitre, C. 2013. Mapping adaptation actions for the automatic reconciliation of dynamic ontologies. 22nd ACM International Conference on Information and Knowledge Management, 599608. DOI: 10.1145/2505515.2505564
  16. 16Draxl, C and Scheffler, M. 2018. Nomad: The fair concept for big data-driven materials science. MRS Bulletin, 43(9): 676682. DOI: 10.1557/mrs.2018.208
  17. 17Drymonas, E, Zervanou, K and Petrakis, EG. 2010. Unsupervised ontology acquisition from plain texts: the OntoGain system. International Conference on Application of Natural Language to Information Systems, 277287. DOI: 10.1007/978-3-642-13881-2_29
  18. 18El-Kishky, A, Song, Y, Wang, C, Voss, CR and Han, J. 2014. Scalable topical phrase mining from text corpora. Proceedings of the VLDB Endowment, 8(3): 305316. DOI: 10.14778/2735508.2735519
  19. 19European Committee for Standardization. 2010. A guide to the development and use of standards compliant data formats for engineering materials test data.
  20. 20European Materials Modelling Council. 2017. Report on workshop on interoperability in materials modelling.
  21. 21Faure, D and Poibeau, T. 2000. First experiments of using semantic knowledge learned by ASIUM for information extraction task using INTEX. ECAI-2000 Ontology Learning Workshop, 712.
  22. 22Galke, L, Mai, F, Schelten, A, Brunsch, D and Scherp, A. 2017. Using titles vs. full-text as source for automated semantic document annotation. In: Corcho, Ó, Janowicz, K, Rizzo, G, Tiddi, I and Garijo, D (eds.), Proceedings of the Knowledge Capture Conference, K-CAP 2017, Austin, TX, USA, December 4–6, 2017, 20: 14. DOI: 10.1145/3148011.3148039
  23. 23Ganter, B and Wille, R. 2012. Formal concept analysis: mathematical foundations. Springer Science & Business Media.
  24. 24Ghiringhelli, LM, Carbogno, C, Levchenko, S, Mohamed, F, Huhs, G, Lueders, M, Oliveira, M and Scheffler, M. 2016. Towards a Common Format for Computational Materials Science Data. PSI-K Scientific Highlights. July.
  25. 25Hartung, M, Terwilliger, J and Rahm, E. 2011. Recent advances in schema and ontology evolution. In: Bellahsene, Z, Bonifati, A and Rahm, E (eds.), Schema Matching and Mapping, 149190. DOI: 10.1007/978-3-642-16518-4
  26. 26Hastings, J, Jeliazkova, N, Owen, G, Tsiliki, G, Munteanu, CR, Steinbeck, C and Willighagen, E. 2015. eNanoMapper: harnessing ontologies to enable data integration for nanomaterial risk assessment. Journal of Biomedical Semantics, 6: 10, 115. DOI: 10.1186/s13326-015-0005-5
  27. 27Hearst, MA. 1992. Automatic acquisition of hyponyms from large text corpora. 14th International Conference on Computational Linguistics, 539545. DOI: 10.3115/992133.992154
  28. 28Ivanova, V, Bergman, JL, Hammerling, U and Lambrix, P. 2012. Debugging Taxonomies and their Alignments: the ToxOntology – MeSH Use Case. In: Lambrix, P, Qi, G and Horridge, M (eds.), Proceedings of the First International Workshop on Debugging Ontologies and Ontology Mappings, WoDOOM 2012, Galway, Ireland, October 8, 2012, 2536.
  29. 29Ivanova, V and Lambrix, P. 2013. A unified approach for aligning taxonomies and debugging taxonomies and their alignments. In: Cimiano, P, Corcho, Ó, Presutti, V, Hollink, L and Rudolph, S (eds.), The Semantic Web: Semantics and Big Data, 10th International Conference, ESWC 2013, Montpellier, France, May 26–30, 2013, Proceedings, 115. DOI: 10.1007/978-3-642-38288-8
  30. 30Jiang, X and Tan, AH. 2010. CRCTOL: A semantic-based domain ontology learning system. Journal of the American Society for Information Science and Technology, 61(1): 150168. DOI: 10.1002/asi.21231
  31. 31Kalidindi, SR and De Graef, M. 2015. Materials data science: current status and future outlook. Annual Review of Materials Research, 45: 171193. DOI: 10.1146/annurev-matsci-070214-020844
  32. 32Karcher, S, Willighagen, EL, Rumble, J, Ehrhart, F, Evelo, CT, Fritts, M, Gaheen, S, Harper, SL, Hoover, MD, Jeliazkova, N, Lewinski, N, Robinson, RLM, Mills, KC, Mustad, AP, Thomas, DG, Tsiliki, G and Hendren, CO. 2018. Integration among databases and data sets to support productive nanotechnology: Challenges and recommendations. NanoImpact, 9: 85101. DOI: 10.1016/j.impact.2017.11.002
  33. 33Lambrix, P. 2005. Towards a semantic web for bioinformatics using ontology-based annotation. 14th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprise (WETICE’05), 37. DOI: 10.1109/WETICE.2005.58
  34. 34Lambrix, P. 2019. Completing and debugging ontologies: state of the art and challenges. arXiv: 1908.03171.
  35. 35Lambrix, P, Armiento, R, Delin, A and Li, H. 2019. Big semantic data processing in the materials design domain. In: Sakr, S and Zomaya, AY (eds.), Encyclopedia of Big Data Technologies. DOI: 10.1007/978-3-319-63962-8
  36. 36Lambrix, P, Wei-Kleiner, F and Dragisic, Z. 2015. Completing the is-a structure in light-weight ontologies. Journal of Biomedical Semantics, 6: 12, 126. DOI: 10.1186/s13326-015-0002-8
  37. 37Lin, Z, Lu, R, Xiong, Y and Zhu, Y. (2012). Learning ontology automatically using topic model. IEEE International Conference on Biomedical Engineering and Biotechnology, 360363. DOI: 10.1109/iCBEB.2012.263
  38. 38Liu, Q and Lambrix, P. 2010. A System for Debugging Missing Is-a Structure in Networked Ontologies. In: Lambrix, P and Kemp, G (eds.), Data Integration in the Life Sciences, 7th International Conference, DILS 2010, Gothenburg, Sweden, August 25–27, 2010, Proceedings, 5057. DOI: 10.1007/978-3-642-15120-0
  39. 39Maedche, A, Pekar, V and Staab, S. 2003. Ontology learning part one – on discovering taxonomic relations from the web. In: Zhong, N, Liu, J and Yao, Y (eds.), Web Intelligence, 301320. Springer. DOI: 10.1007/978-3-662-05320-1
  40. 40Maedche, A and Staab, S. 2000. Discovering conceptual relations from text. 14th European Conference on Arti_cial Intelligence, 321325.
  41. 41Navigli, R and Velardi, P. 2004. Learning domain ontologies from document warehouses and dedicated web sites. Computational Linguistics, 30(2): 151179. DOI: 10.1162/089120104323093276
  42. 42Navigli, R, Velardi, P, Cucchiarelli, A, Neri, F and Cucchiarelli, R. 2004. Extending and enriching WordNet with OntoLearn. Proc. 2nd Global WordNet Conf. (GWC), 279284.
  43. 43Radinger, A, Rodriguez-Castro, B, Stolz, A and Hepp, M. 2013. Baudataweb: the Austrian building and construction materials market as linked data. 9th International Conference on Semantic Systems, 2532. ACM. DOI: 10.1145/2506182.2506186
  44. 44Rani, M, Dhar, AK and Vyas, OP. 2017. Semi-automatic terminology ontology learning based on topic modeling. Engineering Applications of Artificial Intelligence, 63: 108125. DOI: 10.1016/j.engappai.2017.05.006
  45. 45Rumble, J, Broome, J and Hodson, S. 2019. Building an international consensus on multi-disciplinary metadata standards: A codata case history in nanotechnology. Data Science Journal, 8: 12: 111. DOI: 10.5334/dsj-2019-012
  46. 46Schaal, M, Müller, RM, Brunzel, M and Spiliopoulou, M. 2005. RELFIN – topic discovery for ontology enhancement and annotation. In: Gómez-Pérez, A and Euzenat, J (eds.), The Semantic Web: Research and Applications, Second European Semantic Web Conference, ESWC 2005, Heraklion, Crete, Greece, May 29 – June 1, 2005, Proceedings, 608622. DOI: 10.1007/11431053_41
  47. 47Spiliopoulos, V, Vouros, GA and Karkaletsis, V. 2010. On the discovery of subsumption relations for the alignment of ontologies. Journal of Web Semantics, 8: 6988. DOI: 10.1016/j.websem.2010.01.001
  48. 48Stevens, R, Goble, CA and Bechhofer, S. 2000. Ontology-based knowledge representation for bioinformatics. Briefings in Bioinformatics, 1(4): 398414. DOI: 10.1093/bib/1.4.398
  49. 49Steyvers, M and Griffiths, T. 2007. Probabilistic topic models. In: Landauer, TK, McNamara, DS, Dennis, S and Kintsch, W (eds.), Latent semantic analysis: A road to meaning.
  50. 50Thomas, DG, Pappu, RV and Baker, NA. 2011. Nanoparticle ontology for cancer nanotechnology research. Journal of Biomedical Informatics, 44(1): 5974. DOI: 10.1016/j.jbi.2010.03.001
  51. 51Tropsha, A, Mills, KC and Hickey, AJ. 2017. Reproducibility, sharing and progress in nanomaterial databases. Nature nanotechnology, 12: 11111114. DOI: 10.1038/nnano.2017.233
  52. 52Vardeman, C, II, Krisnadhi, A, Cheatham, M, Janowicz, K, Ferguson, H, Hitzler, P and Buccellato, A. 2017. An ontology design pattern and its use case for modeling material transformation. Semantic Web, 8(5): 719731. DOI: 10.3233/SW-160231
  53. 53Wächter, T, Tan, H, Wobst, A, Lambrix, P and Schroeder, M. 2006. A corpus-driven approach for design, evolution and alignment of ontologies. Winter Simulation Conference, 15951602. DOI: 10.1109/WSC.2006.322932
  54. 54Wilkinson, MD, Dumontier, M, Aalbersberg, IJ, Appleton, G, Axton, M, Baak, A, Blomberg, N, Boiten, JW, da Silva Santos, LB, Bourne, PE, Bouwman, J, Brookes, AJ, Clark, T, Crosas, M, Dillo, I, Dumon, O, Edmunds, S, Evelo, CT, Finkers, R, Gonzalez-Beltran, A, Gray, AJ, Groth, P, Goble, C, Grethe, JS, Heringa, J, ‘t Hoen, PA, Hooft, R, Kuhn, T, Kok, R, Kok, J, Lusher, SJ, Martone, ME, Mons, A, Packer, AL, Persson, B, Rocca-Serra, P, Roos, M, van Schaik, R, Sansone, SA, Schultes, E, Sengstag, T, Slater, T, Strawn, G, Swertz, MA, Thompson, M, van der Lei, J, van Mulligen, E, Velterop, J, Waagmeester, A, Wittenburg, P, Wolstencroft, K, Zhao, J and Mons, B. 2016. The FAIR guiding principles for scientific data management and stewardship. Scientific data, 3: 160018, 19. DOI: 10.1038/sdata.2016.18
  55. 55Wong, W, Liu, W and Bennamoun, M. 2012. Ontology learning from text: A look back and into the future. ACM Computing Surveys (CSUR), 44(4): 20. DOI: 10.1145/2333112.2333115
  56. 56Zavitsanos, E, Paliouras, G, Vouros, GA and Petridis, S. (2007). Discovering subsumption hierarchies of ontology concepts from text corpora. IEEE/WIC/ACM International Conference on Web Intelligence, 402408. DOI: 10.1109/WI.2007.55
  57. 57Zhang, X, Chen, H, Ruan, Y, Pan, D and Zhao, C. 2017. MATVIZ: a semantic query and visualization approach for metallic materials data. International Journal of Web Information Systems, 13: 260280. DOI: 10.1108/IJWIS-11-2016-0065
  58. 58Zhang, X, Zhao, C and Wang, X. 2015. A survey on knowledge representation in materials science and engineering: An ontological perspective. Computers in Industry, 73: 822. DOI: 10.1016/j.compind.2015.07.005
  59. 59Zhang, Y, Luo, X, Zhao, Y and Zhang, HC. 2015. An ontology-based knowledge framework for engineering material selection. Advanced Engineering Informatics, 29: 9851000. DOI: 10.1016/j.aei.2015.09.002
Language: English
Submitted on: Jun 22, 2019
Accepted on: Sep 23, 2019
Published on: Oct 3, 2019
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Huanyu Li, Rickard Armiento, Patrick Lambrix, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.