An Ontology-Based Approach for Knowledge Acquisition: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Agnieszka Konys

Selecting the right supplier is a critical decision in sustainable supply chain management. Sustainable supplier selection plays an important role in achieving a balance between the three pillars of a sustainable supply chain: economic, environmental, and social.

  • ontology
  • knowledge base
  • sustainable supplier selection

1. Introduction

The concept of sustainable development is based on the intersection of three dimensions: economic, environmental, and social. Each of them deals with different aspects, but together they focus on promoting sustainable development. Globalization forces global manufacturers to attach much importance to partnerships between suppliers. In general, a supply chain is a concept that links upstream, midstream, and downstream. The manufacturers’ aim is to reduce costs in this process. Moreover, supply chain management (SCM) receives the applicable information from downstream to improve the quality of the goods provided upstream and downstream [1]. Growing customer, non-governmental organization (NGO), and law enforcement concerns about environmental, social, and corporate responsibility have drawn industry academics and practitioners to the concept of sustainable supply chain management [2].
The assessment of sustainable development is an issue of growing importance among scientists and decision-makers. Sustainability assessment offers a large number of opportunities to measure and evaluate the level of its accomplishment. The search for effective methods of assessing sustainable development and its monitoring of development is now becoming one of the key factors determining the development of a sustainable society. The problem of assessing sustainable development applies to almost all areas. The international environmental policy, government, and people have stimulated enterprises to strictly adopt sustainable concepts in the supply chain networking to obtain a reactive, regulatory, proactive strategic, and competitive merit and abrade the non-sustainable challenges and factors against the world’s environment [3]. Due to globalization, sustainable supply chains are becoming more and more important. Hence, it is worth paying attention to ensuring sustainable supplier selection in this process. Sustainable supplier selection is a combined multi-dimensional problem that includes considering both qualitative and quantitative factors. The sustainability paradigm has been considered a comprehensive term in supplier selection, which includes a vital presence of three aspects (economic, environmental, and social) [4].
Ensuring sustainable supply chain complexity is one of the most difficult problems in today’s global supply chains and is assumed as the key impediment to business performance. It has a significant influence on competitiveness, costs, customer satisfaction, product innovation, and market share. Therefore the decision-makers must know the criteria causing sustainable supply chain efficiency. Proper identification and prioritizing of sustainable supplier criteria are required for effective monitoring and controlling of supply chain management [5]. Moreover, the timeliness of these criteria is also of great importance. The selection of a sustainable supplier depends on many factors. Thus, the crucial question is to find a reasonable approach between comprehensiveness and a manageable multi-dimensional knowledge base as well as up-to-date information exchange.

2. Sustainable Supplier Selection

The growing emphasis on supply chain management among manufacturing companies has made the suppliers’ role in the value-addition processes to become strategically significant [6]. The problem of assessing sustainable development applies to almost all areas. Supplier selection is a combined multi-dimensional problem that includes considering both qualitative and quantitative factors [7]. Due to globalization, sustainable supply chains are becoming more and more important. The fast globalization of doing business affects business competition, changing the model from “company versus company” to the model “supply chain versus supply chain” [8]. Therefore, choosing a good combination of suppliers to work with is critical to the success of conducting business [1]. Over the years, the importance of selecting suppliers has been appreciated and emphasized. Adding sustainability aspects to the supplier selection process highlights existing trends in environmental, economic, and social issues related to management and business processes. Moreover, the development of sustainable development allows the integration of environmental, economic, and social thinking with conventional supplier selection [9].
From a systematic point of view, the study of the problem of sustainable supplier selection can be divided into two parts, including criteria and methods [10]. The analysis of the literature provides a set of various methods exploiting different aspects and using single or mixed approaches, as well as examples of selection criteria [8][9][11]. Most of the studies on sustainable supplier selection use MCDM or fuzzy MCDM techniques with complex calculations [1]. A wide range of methods was applied to solve the problem of sustainable supplier selection. The literature reviews [9] point out that the main single and combined approaches used to solve this problem are mathematics methods and artificial intelligence approaches, especially including analytic hierarchy process [12][13], linear programming [12], multi-objective programming [14][15], goal programming [16], data envelopment analysis [10], heuristics [17], statistical [18], cluster analysis [19], multiple regression [20], discriminant analysis [21], neural networks [22], software agent [20], case-based reasoning [23], expert system [21], and fuzzy set theory [11] as well as combinations of selected pairs.
As it is a multi-dimensional concept, the selection of sustainable suppliers is not based on a single criterion but on a set of criteria, which are mostly focused on economic, social, and environmental issues. In general, most companies need to focus on their supply chains to enhance sustainability to meet customer demands and comply with environmental legislation. In order to achieve these goals, companies must focus on criteria that include carbon footprint and toxic emissions, energy use and efficiency, waste generation, and worker health and safety [24]. Therefore, to analyze interrelationships among sustainability criteria, it is necessary to identify the most important ones for a given decision problem and then evaluate suppliers according to these criteria. Since the knowledge about criteria is scattered, a set of hybrid information aggregation is required to provide practical evaluation and link this set of information to the proposed knowledge base. The literature analysis provides many multi-criteria methods to support a balanced selection of suppliers and multiple cuttings of criteria sets, often suited for a given area (e.g., food, industry, and others). There are many comparable approaches; Table 1 shows a small piece of them. However, little attention has been paid to building a complex solution that allows gathering the selection criteria for sustainable suppliers, and there is almost no systemic and structured knowledge-based approach that could be used to evaluate the sustainability of suppliers.
Table 1. Examples of multi-criteria methods to support a selection of sustainable suppliers.
The Used MCDA Method Domain Source
AHP urban water reuse, energy landscape [25][26]
Multi-attribute Value Theory (MAVT) electricity system, RES, urban regeneration [27][28]
PROMETHEE logistics and distribution, agriculture [29][30]
TOPSIS air pollution, transportation sector [31][32]
MULTIMOORA energy policy [33]

3. Information Extraction

The information extraction (IE) process is based on the automatic extraction of certain types of information from natural language text. IE is the process of extracting information from unstructured text sources to enable entities to be searched, classified, and stored in a knowledge base [34]. The general aim is to parse text in natural language and look for instances of a certain class of objects or events and the instances of relationships between them. Another definition describes information extraction as a form of natural language processing in which certain types of information must be recognized and extracted from a text. Extracting information uses various algorithms and methods for finding information [35]. IE deals with the collection of texts in order to transform them into information that can be easily understood and analyzed [36]. Semantically enhanced information extraction (also known as semantic annotation) links these units to their semantic descriptions and connections from the knowledge graph. Because is much information available on the Internet these days, and the amount of it is constantly growing, this results in information overload. However, the real problem is not the sheer amount of information but the inability to filter it properly [34][37]. IE helps in the automatic detection of new, previously unknown information by automatically extracting information from various unstructured resources [38]. Therefore, the key element is linking the extracted information together to formulate new facts or new knowledge. In other words, in IE, the goal is to discover previously unknown information. Figure 1 displays an illustrative example of how information extraction works in practice.
Electronics 11 04012 g001
Figure 1. An example of information extraction.

Natural Language Processing (NLP)

NLP aims to analyze, identify and solve problems related to the automatic generation and understanding of human language. NLP aims to perform, decode and understand unstructured information [39]. NLP allows for the following:
  • Sorting the data to remove the rubbish from the interesting parts;
  • Extracting the relevant pieces of information;
  • Linking the extracted information to other sources of information;
  • Aggregating the information according to potential new categories;
  • Querying the (aggregated) information;
  • Visualizing the results of the query.
It is composed of several tasks:
  • Text pre-processing—the text is prepared for processing using computational linguistics tools such as tokenization, sentence sharing, morphological analysis, etc.;
  • Finding and classifying concepts—the various types of concepts are detected and classified;
  • Connecting concepts—this task aims to identify the relationship between the extracted concepts;
  • Unify—this task presents the extracted data in a standard form;
  • Remove information noise—this task eliminates duplicate data;
  • Enriching the knowledge base—the extracted knowledge is processed in the knowledge base for further use.
Overall, the combination of NLP and information extraction extracts new knowledge from the raw data. Finally, unknown information is obtained by automatically extracting information from various unstructured resources.

This entry is adapted from the peer-reviewed paper 10.3390/electronics11234012

References

  1. Hoseini, S.A.; Fallahpour, A.; Wong, K.Y.; Mahdiyar, A.; Saberi, M.; Durdyev, S. Sustainable Supplier Selection in Construction Industry through Hybrid Fuzzy-Based Approaches. Sustainability 2021, 13, 1413.
  2. Govindan, K.; Khodaverdi, R.; Jafarian, A. A Fuzzy Multi Criteria Approach for Measuring Sustainability Performance of a Supplier Based on Triple Bottom Line Approach. J. Clean. Prod. 2013, 47, 345–354.
  3. Saeed, M.; Kersten, W. Drivers of Sustainable Supply Chain Management: Identification and Classification. Sustainability 2019, 11, 1137.
  4. Amindoust, A. A Resilient-Sustainable Based Supplier Selection Model Using a Hybrid Intelligent Method. Comput. Ind. Eng. 2018, 126, 122–135.
  5. Chand, P.; Thakkar, J.J.; Ghosh, K.K. Analysis of Supply Chain Complexity Drivers for Indian Mining Equipment Manufacturing Companies Combining SAP-LAP and AHP. Resour. Policy 2018, 59, 389–410.
  6. Kumar, C.V.S.; Routroy, S. Developing the Preferred Supplier Relationships—A Case Study. Int. J. Intell. Enterp. 2018, 5, 50.
  7. Ware, N.R.; Singh, S.P.; Banwet, D.K. Supplier Selection Problem: A State-of-the-Art Review. Manag. Sci. Lett. 2012, 2, 1465–1490.
  8. Awasthi, A.; Govindan, K.; Gold, S. Multi-Tier Sustainable Global Supplier Selection Using a Fuzzy AHP-VIKOR Based Approach. Int. J. Prod. Econ. 2018, 195, 106–117.
  9. Konys, A. Methods Supporting Supplier Selection Processes–Knowledge-Based Approach. Procedia Comput. Sci. 2019, 159, 1629–1641.
  10. Ramanathan, R. Supplier Selection Problem: Integrating DEA with the Approaches of Total Cost of Ownership and AHP. Supply Chain Manag. Int. J. 2007, 12, 258–261.
  11. Arabsheybani, A.; Paydar, M.M.; Safaei, A.S. An Integrated Fuzzy MOORA Method and FMEA Technique for Sustainable Supplier Selection Considering Quantity Discounts and Supplier’s Risk. J. Clean. Prod. 2018, 190, 577–591.
  12. Shaw, K.; Shankar, R.; Yadav, S.S.; Thakur, L.S. Global Supplier Selection Considering Sustainability and Carbon Footprint Issue: AHP Multi-Objective Fuzzy Linear Programming Approach. Int. J. Oper. Res. 2013, 17, 215.
  13. Kahraman, C.; Topcu, Y.I. Operations Research Applications in Health Care Management; Springer International Publishing: New York, NY, USA; Heidelberg, Germany; Dordrecht, The Netherlands; London, UK, 2018; ISBN 978-3-319-65455-3.
  14. Weber, C.A.; Current, J.R.; Benton, W.C. Vendor Selection Criteria and Methods. Eur. J. Oper. Res. 1991, 50, 2–18.
  15. Büyüközkan, G.; Feyzioğlu, O.; Havle, C.A. Analysis of Success Factors in Aviation 4.0 Using Integrated Intuitionistic Fuzzy MCDM Methods. In Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making; Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U., Eds.; Springer International Publishing: Cham, Switzerland, 2020; Volume 1029, pp. 598–606. ISBN 978-3-030-23755-4.
  16. Singh, R.K.; Murty, H.R.; Gupta, S.K.; Dikshit, A.K. An Overview of Sustainability Assessment Methodologies. Ecol. Indic. 2012, 15, 281–299.
  17. Burki, U.; Ersoy, P.; Dahlstrom, R. Achieving Triple Bottom Line Performance in Manufacturer-Customer Supply Chains: Evidence from an Emerging Economy. J. Clean. Prod. 2018, 197, 1307–1316.
  18. Sarkis, J.; Dhavale, D.G. Supplier Selection for Sustainable Operations: A Triple-Bottom-Line Approach Using a Bayesian Framework. Int. J. Prod. Econ. 2015, 166, 177–191.
  19. Lee, J.; Jung, K.; Kim, B.H.; Peng, Y.; Cho, H. Semantic Web-Based Supplier Discovery System for Building a Long-Term Supply Chain. Int. J. Comput. Integr. Manuf. 2015, 28, 155–169.
  20. Yu, C.; Wong, T.N. An Agent-Based Negotiation Model for Supplier Selection of Multiple Products with Synergy Effect. Expert Syst. Appl. 2015, 42, 223–237.
  21. Kumar, A.; Jain, V.; Kumar, S. A Comprehensive Environment Friendly Approach for Supplier Selection. Omega 2014, 42, 109–123.
  22. Kuo, R.J.; Wang, Y.C.; Tien, F.C. Integration of Artificial Neural Network and MADA Methods for Green Supplier Selection. J. Clean. Prod. 2010, 18, 1161–1170.
  23. Zhao, H.; Guo, S. Selecting Green Supplier of Thermal Power Equipment by Using a Hybrid MCDM Method for Sustainability. Sustainability 2014, 6, 217–235.
  24. Wang, C.-N.; Nguyen, V.T.; Thai, H.T.N.; Tran, N.N.; Tran, T.L.A. Sustainable Supplier Selection Process in Edible Oil Production by a Hybrid Fuzzy Analytical Hierarchy Process and Green Data Envelopment Analysis for the SMEs Food Processing Industry. Mathematics 2018, 6, 302.
  25. Opher, T.; Friedler, E.; Shapira, A. Comparative Life Cycle Sustainability Assessment of Urban Water Reuse at Various Centralization Scales. Int. J. Life Cycle Assess. 2019, 24, 1319–1332.
  26. Shaaban, M.; Scheffran, J.; Böhner, J.; Elsobki, M.S. A Dynamic Sustainability Analysis of Energy Landscapes in Egypt: A Spatial Agent-Based Model Combined with Multi-Criteria Decision Analysis. J. Artif. Soc. Soc. Simul. 2019, 22.
  27. Roinioti, A.; Koroneos, C. Integrated Life Cycle Sustainability Assessment of the Greek Interconnected Electricity System. Sustain. Energy Technol. Assess. 2019, 32, 29–46.
  28. Bottero, M.; Oppio, A.; Bonardo, M.; Quaglia, G. Hybrid Evaluation Approaches for Urban Regeneration Processes of Landfills and Industrial Sites: The Case of the Kwun Tong Area in Hong Kong. Land Use Policy 2019, 82, 585–594.
  29. Talukder, B.; Hipel, K.W. The PROMETHEE Framework for Comparing the Sustainability of Agricultural Systems. Resources 2018, 7, 74.
  30. Melkonyan, A.; Gruchmann, T.; Lohmar, F.; Kamath, V.; Spinler, S. Sustainability Assessment of Last-Mile Logistics and Distribution Strategies: The Case of Local Food Networks. Int. J. Prod. Econ. 2020, 228, 107746.
  31. Cai, M.; Zhang, W.Y.; Zhang, K. ManuHub: A Semantic Web System for Ontology-Based Service Management in Distributed Manufacturing Environments. IEEE Trans. Syst. Man Cybern.-Part A Syst. Hum. 2011, 41, 574–582.
  32. Balasbaneh, A.T.; Yeoh, D.; Zainal Abidin, A.R. Life Cycle Sustainability Assessment of Window Renovations in Schools against Noise Pollution in Tropical Climates. J. Build. Eng. 2020, 32, 101784.
  33. Siksnelyte, I.; Zavadskas, E.K.; Bausys, R.; Streimikiene, D. Implementation of EU Energy Policy Priorities in the Baltic Sea Region Countries: Sustainability Assessment Based on Neutrosophic MULTIMOORA Method. Energy Policy 2019, 125, 90–102.
  34. Maedche, A. The Text-to-Onto Ontology Extraction and Maintenance System. In Proceedings of the ICDM-Workshop on Integrating Data Mining and Knowledge Management, San Jose, CA, USA, 29 November–2 December 2001.
  35. Konys, A. Towards Knowledge Handling in Ontology-Based Information Extraction Systems. Procedia Comput. Sci. 2018, 126, 2208–2218.
  36. Jain, V.; Singh, M. Ontology Based Information Retrieval in Semantic Web: A Survey. Int. J. Inf. Technol. Comput. Sci. 2013, 5, 62–69.
  37. Konys, A. A Tool Supporting Mining Based Approach Selection to Automatic Ontology Construction. IADIS J. Comput. Sci. Inf. Syst. 2015, 3–10.
  38. Boufrida, A.; Boufaida, Z. Rule Extraction from Scientific Texts: Evaluation in the Specialty of Gynecology. J. King Saud Univ.-Comput. Inf. Sci. 2022, 34, 1150–1160.
  39. Zhang, Z.; Gentile, A.L.; Ciravegna, F. Recent Advances in Methods of Lexical Semantic Relatedness—A Survey. Nat. Lang. Eng. 2013, 19, 411–479.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
Video Production Service
Feedback