The aim of this work was to propose a methodology supporting the task of collecting the comparative data on studies of the mechanical properties of ADI. Collecting of research data is an important step in the process of finding the optimum design solutions for newly made products - experimental data allow us properly calibrate the manufacturing process of ADI to let the final product achieve the required properties. Parameters of the ADI production process, i.e. the time and temperature of austenitising and austempering, as well as the alloying elements added to ductile iron affect the ADI properties. The design process can use research data collected, among others, from the Web. As stated in the article, the process of data acquisition can be supported by semantic technologies, including ontologies which are descriptive logic formalism.
 P. Skoczylas, A. Krzyńska, M. Kaczorowski, The comparative studies of ADI versus Hadfield cast steel wear resistance, Archives of Foundry Engineering 11 (2), 123–126 (2011).
 J.R. Keough, Austempered Ductile Iron (ADI) - A Green Alternative. American Foundry Society, 2010, USA.
 J. Tybulczuk, A. Kowalski, ADI. The properties and application in industry. Castings Atlas, 2003, Instytut Odlewnictwa, Kraków (in Polish).
 D. Myszka, T. Babul, Preparation of ausferritic iron in a vacuum oven with gas cooling, Archiwum Technologii Maszyn i Automatyzacji 30 (3), 34-42 (2010) (in Polish).
 D. Wilk-Kołodziejczyk, B. Mrzygłód, K. Regulski, Influence of Process Parameters on the Properties of Austempered Ductile Iron (ADI) Examined with the Use of Data Mining Methods, Metalurgija 55 (4), 849-851 (2014).
 T. Szymczak, G. Gumienny, T. Pacyniak, Effect of Vanadium and Molybdenum on the Crystallization, Microstructure and Properties of Hypoeutectic Silumin, Archives of Foundry Engineering 15(4), 81-86 (2015).
 C. Zhin, S. Teng-Shih, Phase Transformation and Fatigue Properties of Alloyed and Unalloyed Austemperred Ductile Irons, World Conference on ADI: Conference on Austempored Ductile Iron (ADI) for Casting Producers, Suppliers and Design Engineers, 2002, Kentucky, USA.
 I. Olejarczyk-Wożeńska, A. Adrian, H. Adrian, B. Mrzygłód, Parametric representation of TTT diagrams of ADI cast iron, Archives of Metallurgy and Materials 57, 981-986 (2012).
 L. Bartosiewicz, I. Singh, F.A. Alberts, A.R. Krause, S.K. Putatunda, The Influence of Chromium on Mechanical Properties of Austempered Ductile Cast Iron, Journal of Materials Engineering and Performance 4 (1), 90-101 (1995).
  P.P. Rao, S.K. Patatunda, Dependence of Fracture Toughness of Austempered Ductile Iron on Austempering Temperature, Metallurgical and Materials Transactions A 29A, 3005-3016 (1998).
 K. Aslantas, S. Tasgetiren, Y. Yalcin, Austempering retards pitting failure in ductile iron spur gears, Engineering Failure Analysis 11, 935-941 (2004).
 S. Kluska-Nawarecka, D. Wilk-Kolodziejczyk, K. Regulski, G. Dobrowolski, Rough Sets Applied to the RoughCast System for Steel Castings, Lecture Notes in Computer Science, in: N. Nguyen, C.G. Kim, A. Janiak (Eds.), Intelligent Information and Database Systems, ACIIDS, Pt II, 6592, 52-61 (2011).
 S. Kluska-Nawarecka, B. Śnieżyński, W. Parada, M. Lustofin, D. Wilk-Kołodziejczyk, The use of LPR (logic of plausible reasoning) to obtain information on innovative casting technologies, Archives of Civil and Mechanical Engineering 14, 25-31 (2014).
 J. David, Z. Jancikova, R. Frischer, M. Vrozina, Crystallizer’s Desks Surface Diagnostics with Usage of Robotic System, Archives Of Metallurgy And Materials 58 (3) 907-910 (2013).
 A. Glowacz, Diagnostics of DC and Induction Motors Based on the Analysis of Acoustic Signals, Measurement Science Review 14 (5), 257-262 (2014).
 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).
 E. Nawarecki, S. Kluska-Nawarecka, K. Regulski K., Multi-aspect character of the man-computer relationship in a diagnostic-advisory system, in: Z.S. Hippe, J.L. Kulikowski, T. Mroczek (Eds.), Human – computer systems interaction: backgrounds and applications, Springer-Verlag, Berlin Heidelberg, 85-102, 2012.
 B. Mrzygłód, I. Olejarczyk-Wożeńska, A. Henryk, Studies of Phase Transformations in Ductile Iron with Additions of Ni, Cu, Mo, 24th International Conference on Metallurgy and Materials METAL Brno, Czech Republic, pp. 798-803, 2015.
 M. Gollapalli, X. Li, I. Wood, G. Governatori, Ontology Guided Data Linkage Framework for Discovering Meaningful Data Facts, in: Advanced Data Mining and Applications, Lecture Notes in Computer Science 7121, 252-265 (2011).
 J. Euzenat, P. Shvaiko, Ontology Matching, 2007, Berlin Heidelberg Springer, New York.
 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).
 A. Opalinski, W. Turek, K. Cetnarowicz, Scalable web monitoring system, Federated Conference on Computer Science and Information Systems (FedCSIS), 1261–1267, Kraków 2013.
 V. Gopalkrishnan, D. Steier, H. Lewis, J. Guszcza, Big Data, Big Business: Bridging the Gap, In Proc. of the 1st Intl. Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, 7-11, New York 2012.
 J. Fong, J. Indulska, R. Robinson, A Preference Modelling Approach to Support Intelligibility in Pervasive Applications, 8th IEEE Workshop on Context Modeling and Reasoning (CoMoRea’11) Proc. of the 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), 409-414, Seattle 2011.