Formalization of Technological Knowledge in the Field of Metallurgy using Document Classification Tools Supported with Semantic Techniques

Open access

Abstract

The process of knowledge formalization is an essential part of decision support systems development. Creating a technological knowledge base in the field of metallurgy encountered problems in acquisition and codifying reusable computer artifacts based on text documents. The aim of the work was to adapt the algorithms for classification of documents and to develop a method of semantic integration of a created repository. Author used artificial intelligence tools: latent semantic indexing, rough sets, association rules learning and ontologies as a tool for integration. The developed methodology allowed for the creation of semantic knowledge base on the basis of documents in natural language in the field of metallurgy.

[1] E. Nawarecki, S. Kluska-Nawarecka, K. Regulski, Multi-aspect character of the man-computer relationship in a diagnostic-advisory system, Human-computer systems interaction: backgrounds and applications 2, eds. Z.S. Hippe, J.L. Kulikowski, T. Mroczek, 2012 Berlin; Heidelberg: Springer-Verlag.

[2] J. David, P. Svec, R. Frischer, R. Garzinova, The Computer Support of Diagnostics of Circle Crystallizers; Metalurgija 53 (2), 193-196 (2014).

[3] P. Malinowski, J.S. Suchy, J. Jakubski, Technological knowledge management system for foundry industry, Archives of Metallurgy and Materials 58 (3), 965-968 (2013).

[4] Z. Gronostajski, M. Hawryluk, M. Kaszuba, M. Marciniak, A. Niechajowicz, S. Polak, M. Zwierzchwoski, A. Adrian, B. Mrzyglod, J. Durak, The expert system supporting the assessment of the durability of forging tools, International Journal Of Advanced Manufacturing Technology 82 (9-12), 1973-1991 (2016). DOI: 10.1007/s00170-015-7522-3

[5] A. Maciol, P. Maciol, S. Jedrusik, J. Lelito, The new hybrid rule-based tool to evaluate processes in manufacturing, International Journal Of Advanced Manufacturing Technology 79 (9-12), 1733-1745 (2015).

[6] C.D. Manning, P. Raghavan, H. Schutze. An Introduction to Information Retrieval. 2008.

[7] D. Wilk-Kolodziejczyk, A. Opalinski, E. Nawarecki, S. Kluska-Nawarecka, Exploration of Web resources in the domain of metal processing technologies, Metalurgija 55 (1), 127-130 (2016).

[8] R. Agrawal, R. Srikant, Fast algorithms for mining association rules in large databases. in: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, p. 487-499, Santiago, Chile, (1994).

[9] S. Deerwester, S. Dumais, T. Landauer, G. Furnas, R. Harshman, Indexing by latent semantic analysis, Journal of the American Society of Information Science 41 (6), 391-407 (1990).

[10] D. Miao, Q. Duan, H. Zhang, N. Jiao, Rough set based hybrid algorithm for text classification, Expert Systems with Applications 36 (5), 9168-9174 (2009).

[11] Z. Pawlak, Rough set theory and its applications, Journal of Telecommunications and Information Technology 3, 7-10 (2002).

[12] Y. Leung, M. Fischer, W.Z Wu, J.S. Mi, A rough set approach for the discovery of classification rules in interval valued information systems, International Journal of Approximate Reasoning 47 (2), 433-246 (2008).

[13] R.K. Roul, S.K Sahay, An Effective Approach for Web Document Classification using the Concept of Association Analysis of Data Mining, International Journal of Computer Science Engineering and Technology 3 (10), 483 (2012).

[14] Y. Hirate, H. Yamana, Generalized Sequential Pattern Mining with Item Intervals, Journal Of Computers 1 (3) (2006).

[15] J. Jakubski, P. Malinowski, S.M. Dobosz, K. Major-Gabryś, ANN Modelling For The Analysis Of The Green Moulding Sands Properties, Archives of Metallurgy and Materials 58 (3), 961-964 (2013).

[16] K. Smyksy, E. Ziółkowski, R. Wrona, M. Brzeziński, Performance evaluation of rotary mixers through monitoring of power energy parameters, Archives of Metallurgy and Materials 58 (3), 911-914 (2013).

[17] I. Olejarczyk-Wożeńska, A. Adrian, H. Adrian, B. Mrzygłód, Parametric representation of TTT diagrams of ADI cast iron, Archives of Metallurgy and Materials 57 (2), 981-986, (2012).

[18] K. Regulski, D. Szeliga, J. Kusiak, Data Exploration Approach Versus Sensitivity Analysis for Optimization of Metal Forming Processes, Key Engineering Materials 611-612, 1390-1395 (2014).

[19] Z. Glowacz, Recognition of Acoustic Signals of Loaded Synchronous Motor Using FFT, MSAF-5 and LSVM, Archives Of Acoustics 40 (2), 197-20, (2015). DOI:10.1515/aoa-2015-0022

[20] A. Glowacz, Z. Glowacz, Recognition of thermal images of direct current motor with application of Area Perimeter Vector and Bayes Classifier, Measurement Science Review 15 (3), 119-126 (2015). DOI: 10.1515/msr-2015-0018

[21] M. Berry, S. Dumais, G. O’Brien, Using Linear Algebra for Intelligent Information Retrieval, SIAM Review 37 (4), 573-595 (1995). DOI: 10.1137/1037127.

[22] S. Kluska-Nawarecka, K. Regulski, M. Krzyżak, G. Leśniak, M. Gurda, System of semantic integration of non-structuralized documents in natural language in the domain of metallurgy, Archives of Metallurgy and Materials 58 (3), 927-930 (2013). DOI: 10.2478/amm-2013-0103.

[23] R.W. Świdniarski, Rough sets methods in feature reduction and classification, International Journal of Applied Mathematics and Computer Science 11 (3), 565-582 (2001).

[24] R. Colomo-Palacios, I. González-Carrasco, J.L. López-Cuadrado, A. García-Crespo, ReSySTER: A hybrid recommender system for Scrum team roles based on fuzzy and rough sets, International Journal of Applied Mathematics and Computer Science 22 (4), 801-816 (2012).

[25] S. Kluska-Nawarecka, D. Wilk-Kołodziejczyk, K. Regulski, G. Dobrowolski, Rough sets applied to the RoughCast system for steel castings, Intelligent Information and Database Systems. in: Springer Lecture Notes in Computer Science, 6592/2011, 52-61, (2011). DOI: 10.1007/978-3-642-20042-7-6

[26] M.J. Zaki, SPADE: An efficient algorithm for mining frequent sequences, Machine Learning 42, 31-60, (2001).

[27] S. Staab, R. Studer, Handbook on Ontologies, 2004 Springer-Verlag, Berlin.

[28] V. Avram, D. Rizescu, A Domain Ontology For Data Collections Of The Accounting System, in: International Conference On Informatics In Economy, Eds. Boja C., Batagan L., Doinea M., et al., 347-351, Romania 2013.

[29] J. Broekstra, M. Klein, S. Decker, D. Fensel, F. Harmelen, I. Horrocks, Enabling knowledge representation on the Web by extending RDF Schema, Stanford University, 2000 USA.

[30] S. Grimm, P. Hitzler, A. Abecker, Knowledge Representation and Ontologies, Semantic Web Services: Concepts, Technologies and Applications 3, 51-106 (2007).

[31] A. Maciol, R. Wrona, A. Stawowy, P. Maciol, An attempt at formulation of ontology for technological knowledge comprised in technical standards, Archives of Metallurgy And Materials 52 (3), 381-388 (2007).

[32] M.N. Ahmad, R.M. Colomb, M.S. Abdullah, Ontology-Based Applications for Enterprise Systems and Knowledge Management, 294-320 (2013). DOI: 10.4018/978-1-4666-1993-7

[33] N. Konstantinou, D.E. Spanos, M. Chalas, E. Solidakis, N. Mitrou, VisAVis: An Approach to an Intermediate Layer between Ontologies and Relational Database Contents, in: Web Information Systems Modeling, Luxemburg 2006.

[34] R. Poli R, Theory and Applications of Ontology, Computer Applications, 3-12 (2010).

[35] Z. Xu, S. Zhang, Y. Dong, Mapping between Relational Database Schema and OWL Ontology for Deep Annotation, in: 2006 IEEE/WIC/ACM International Conference on Web Intelligence, 548-552, Washington, DC, USA 2006.

[36] S. Kluska-Nawarecka, D. Wilk-Kołodziejczyk, K. Regulski, Practical aspects of knowledge integration using attribute tables generated from relational databases, in: Semantic Methods for Knowledge Management and Communication, 381, 13-22, Springer 2011, Berlin.

Archives of Metallurgy and Materials

The Journal of Institute of Metallurgy and Materials Science and Commitee on Metallurgy of Polish Academy of Sciences

Journal Information


IMPACT FACTOR 2016: 0.571
5-year IMPACT FACTOR: 0.776

CiteScore 2016: 0.85

SCImago Journal Rank (SJR) 2016: 0.347
Source Normalized Impact per Paper (SNIP) 2016: 0.740

Cited By

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 164 164 7
PDF Downloads 67 67 1