The development of European society is based on knowledge and the ability to implement it in all areas of human activity. Knowledge is, for example, also present in the recent EU grand strategy Europe 2020, aiming to achieve growth, which is also
We must know how to properly present our own or acquired knowledge and attract attention to individuals or/and organizations that can use this knowledge usefully in their work or field. It is the organizations themselves making sure the differences among them exist mainly due to ability to detect and absorb knowledge (Rončević and Valič 2019, 330).
Knowledge modeling provides a comprehensive overview of the issues under consideration or the domain being studied, so this paper focuses on the field of knowledge modeling and its presentation in the form of terminological ontologies.
The following organization of the data from the visible journal on web into the text analysis of published titles of articles, will be supported by a computer programme. This programme will enable semi-automatic construction of ontologies from texts. By modelling the domain knowledge in the form of ontologies based on the titles of articles, is intended to examine the main research areas, which can be reflected from the publications. The result of the analysis was performed as a picture of areas in which the research activities of the topic on regional industrial symbiosis and industrial symbiotic networks was the most outlined and representative. It was take into account that was selected only one Journal at this stage and a determinated nomber of titles in terms of publications during the examined period of the last 25 years.
A resource sharing among co-located firms referenced in the Industrial Ecology (IE) literature as Industrial Symbiosis (IS) is concerned with the cyclical flow of resources through networks of businesses. Industrial symbiosisis is mostly characterized with the cyclical flow of resources through networks of businesses as a means of cooperatively approaching ecologically sustainable industrial activity. As such it is motivated by economic considerations such as lowering costs for waste disposal, as well as by environmental ones (Beers et al. 2007). The main goal of industrial symbiosis is to aid in industrial organization by pushing firms to think beyond individual firm boundaries into a broader systems level (P7 Modelling industrial symbiosis to find the potentials and barriers in Aalborg, Denmark 2017). Firms belonging to an industrial ecology utilize industrial symbiosis as a collective approach to competitive advantage and simultaneously realize economic and environmental benefits, but also emerge social considerations, in our case Industrial Symbiotic Networks (ISNs).
Industrial symbiosis, in other words is a few dozen partners, means for trust-building include personal meetings, regular communication, sharing of information and knowledge, and stable rules of the game (Fric 2016).
In 1947, the term “industrial symbiosis” was first utilized by Renner in the economic geography literature to describe “organic relationship” between dissimilar industries, including the use of the waste products from one as input to another. The development of industrial symbiosis in an international perspective gas grown substantially since the late 40’s until now (Chertow 2000, 313–37; Džajić Uršič 2020).
When was tried to construct a proposed hierarchy in this paper, it has been “reduced” a perceived reality of industrial symbiosis systems so the research encompasses and gives importance to the social interactions as trust and willingness (Black Sea Industrial Symbiosis Platform 2017). For the success of industrial symbiosis (Schiller et al. 2014; GILG 2013) is firstly vital the exchange of material by-products and energy, secondly some forms of social exchange, in other words, inter-firm networking, trust and collaboration. Many attempts of regional projects to build industrial symbiosis failed because is it hard to find firms willing to co-locate and link their processes with other firms they do not yet know or trust. This is perceived as simply too great a risk to take. Trust is needed in exploring the possibilities for by-products exchange because firms need to share (possibly sensitive) information about their inputs and outputs (Gilg 2013; Fric 2016).
New economic sociology also pays a little attention to the influence of institutions; might be, that the institutional approaches play a secondary role in the new economic sociology in general, while networks and cultural explanations prevail. Even in studies that are informed by institutionalism, the concept of institution that is being used leans towards the new sociological institutionalism (CRESSI 2017; Kalundborg Symbiose 2017) (and focuses on questions of legitimacy and diffusion of institutional models). Although sociology has lagged behind other social sciences in appreciating new methodologies and a distinctive sociological contribution is evident here. Information about relative data of various titles can assist in the analysis of dozen of titles” texts and following hierarchical ontologies (Džajić Uršič 2020).
The development of industrial symbiotic networks is complex and can be, as a consequence of several factors influencing the evaluation process, uncertain and unclear. The complexity of the process has increased in the period. Even though the exchanges of waste were not called industrial symbiosis or synergies of networks, the early process began at that time. Even if the term of industrial symbiosis may appear to have fully grown onto the sustainability stage, resources exchange and processes of trading are basic aspects of the developing world (Chertow 2000, 313–37).
The importance of the social dimension of industrial symbiosis is very commonly acknowledged in the literature. Trust and willingness on some level is definitive to cooperate and it is seen as requirements for “industrial symbiosis proper” (exchange of materials and energy) to take place (Ehrenfeld 1997; Gibbs and Deutz 2005; Gibbs and Deutz 2007; Tudor et al. 2007). Many of those industrial symbiosis projects failed because is it hard to find firms willing to co-locate and link their processes with other firms they do not yet know or trust. This is perceived as simply too great a risk to take. Trust is needed before interdependencies through by-products exchange can be set up, because certainty and continuity of supply are extremely important to industrial firms (Tudor et al. 2007).
Data are chaotic, unprocessed essentials that tell us nothing in themselves until they are properly processed, but only when we process the data and/or compare between, we find out their meanings. Data doesn't tell us anything about motivation, the quality or characteristics of the analyses, by which they were obtained, but are a prerequisite for information obtained as a result of researches. The problem arises when we have too much data and we do not know what decisions make to reach the desired goal (Bizjak 2014, 9–11).
The knowledge is a base in this case because helps us at work, in everyday life and has several meanings depending where it is applied. The knowledge is not just information like even information is not just data and knowledge comes from information in the same way as information comes from data.
Awad and Ghaziri (2007) argue that information helps to understand relationships between them and give meaning to the unorganaized data, that can be later reorganized, statistically analyze, avoided from errors, or otherwise process them to obtain their meaning. Only when we collect enough information, we understand a phenomenon or problem and talk about knowledge (Girard and Girard 2015; Awad and Ghaziri 2007; Szilva et al. 2018).
Processes for discovering useful knowledge from data are usually based on data mining, which allows the use of special computer algorithms obtaining and displaying meaningful models from input data. Knowledge discovery processes can also be based on text collections and the procedure from an input text extracted structured information is called
Exploring data means looking for characteristics and display them for the group we are processing. This includes some important steps (Awad and Ghaziri 2007):
definition of primary hypotheses and prediction of further measures,
identification of outliers who are outside the expected crowd results,
display of key features and
selection of interesting data groups for further research.
For this case, we can use modern program tool that enable automatic or semi-automatic analysis displaying data. The tool called OntoGen in this research is used for textual analysis of dozen of titles of article where we can present results in the form of content
Data mining and machine learning have a goal to extract knowledge (in a human-understandable structure) from large quantities of data (for example to illustrate relations between observed variables). The automatic or semi-automatic data mining process analyzes data and discovers new outlines to extract previously unknown remarkable outlines such as simplifications that can predict values of a certain variable in previously unseen examples from the known values of other variables (inquiring groups of data records, rare records and needs in the data).
The process of data mining can be seen as a procedure where signify terms from the collection of input texts used
Firstly, what we need to do is to collect different groups of data and display from them statistically significant data to display characteristic for the group we are processing. At this stage are important some steps: (1) definition of initial hypotheses, (2) presentation of key attributes, (3) recognition of outliers outside of the crowd results (4) and selection of remarkable data for further studies.
An ontology is a presentation of a multitude of concepts on some field of human knowledge where those concepts can be categorized. The term
descriptive (defined by terms that are often used to present knowledge and describe a specific thematic area),
formal (consists only of formally accepted descriptive terminology ontologies, which denotes different aspects or types of a given area), or
formalized (contain formal specifications of a field in the strictest meaning of the formalization of the presentation (Poli and Seibt 2010).
Formalized ontologies are glossaries of terms (words and their synonyms) that semantically structure a particular area and at the same time allow to show the hierarchical structure of relations between concepts. In this sense, ontologies have great potential especially in areas of knowledge management, information gathering, integration of intelligent systems and e-commerce (Lavbič and Krisper 2005).
The philosophy of the regional industrial symbiosis is concerned with identifying the kinds of things that exist in industrial symbiotic networks so, ontological (and epistemological) ideas guide not only to the selection of the research focus but also to the methodology assumed and the expected outcomes. Industrial symbiotic networks are in this research observed as social structures that are simultaneously constructed and reflected upon by the researcher and the stakeholders that participate into the system – this approach understands their identification first as a singular phenomenon, and then as a sustainable instrument, part of the process of co-creation of meaning and social interaction (Džajić Uršič 2020).
This ontological (and epistemological) position explains the focus on a small part of this research on the social processes. These processes are behind the articulation of industrial symbiotic networks and the emphasis given to structural and discursive dimensions in the analysis. When trying to conceptualize a proposed research and “reduce” a perceived reality of industrial symbiosis systems, we encompasse and give importance to social interactions (Džajić Uršič 2020).
However, the new economic sociology pays just a little attention to the influence of the important
The data are obtained primarily from website
Semi-Automatic means that “
In this case, for this paper, somehow it is very difficult to manage a lot of web pages and textual documents and in this case ontologies play a very important role for them. Ontologies offer additional help to reduce overloaded information for a specific area.
OntoGen is a “data-driven and semi-automatic” system which is used for generating topic ontologies. “
“
The next main steps with the semi-automatically managed OntoGen program are:
presentation of knowledge modeling, from when the data are captured to information acquisition and knowledge demonstrations;
definition of knowledge representation in the form of ontologies, classification of ontologies and the importance of terminological ontologies in knowledge modeling;
presentation of a case study with a more detailed description of the preparation of data, for text analysis and the sequence of text mining with the computer tool OntoGen;
presentation of the results of text analysis (Guarino 1995).
The data included in the study refers to publications from 2000 to the end of 2019 and contains the titles of scientific publications on the topic of industrial symbiosis and its networks. With the tool for word processing and analysis, are processed then the titles. Due to the needs of textual address analysis publications, is chosen the format of the XML output format. On this way, we got 267 titles of units.
With OntoGen is build a “topic
The data can be processed with the program OntoGen after we prepared a file format as
The program itself suggests keywords and automatically assigns examples allows text to match the content of the suggested keywords. It allows examples of texts combined in concepts and provides a hierarchical overview of
Figure 2 shows the OntoGen user interface that appears when the program is open and is in the starting point for further work. The starting window is divided into two smaller and one larger window. The larger window is for viewing ontologies and managing input texts, where are named documents in the program. In the upper left is a hierarchical tree with all the concepts and subconcepts of the ontology. In the lower left window, the user can add, change or edit concepts and subconcepts in further text analysis. The functionality of the system, which includes learning algorithms, analysis and text management, relies on a collection of programs called
In the user interface appears five components of ontologies:
•
The relations into OntoGen have two relations:
Building ontologies the OntoGen program use a sorting technique in groups with the
In the case study, we founded 267 titles, that had been obtained from articles containing a total of 4546 words. To the list of blocked words, we added conjunctions, numerators, pronouns, etc., that have no special substantive weight. Into OntoGen we can also construct three types of ontologies: visualization – typically in a 2D grouping of documents, method of k-means grouping – mainly used and the third construction of ontologies can be possible as active learning; that means that a user can help a computer program to learn the concepts.
When building ontologies with OntoGen, we decide to have a higher parameter k = 7 (number of text groups) relative to the input size of the files, which in our case contained 267 article titles. With this value, we then looked for intermediate settings that the program shows us as a homogeneous as possible and at the same time sufficiently representative groups of titles, which the program names with concepts or keywords. Then we built an ontology with seven automatically proposed concepts (parameter value k = 7) at the first level of classification of article titles and added three subconcepts each, that were automatically suggested from OntoGen. In the first concept were sorted 35 titles where the program assigned the following keywords:
The obtained results show that the largest group of publications use the general term of s
This feature in OntoGen is a controlled method for adding concepts, based on SVM (Support Vector Machine) active learning method. Enquiring and active learning is only applied to the cases from the selected concept. The idea of this function is that a user can tell/write the concept and some documents related to this concept and than OntoGen can learn the description of this concept and other related documents and discover if those documents are connected and related to the concrete concept.
In the feature of
With the function
From Figure 5 we can see that some words are repeated several times in certain documents. Those words are for example
Ontology in this research has seven concepts and content areas, each of which is divided into three subconcepts (see Figure 1). The first concept
The second concept
The third concept
The fourth concept:
The sixth concept is
In the first concept of ontology, denoted by the keywords
The second concept of the ontology includes 47 titles that include
The third concept of ontology:
In the fourth concept of ontology:
The fitfth concept with keywords: study,
The next concept – the sixth is composed of keywords:
The seventh concept within 27 titles:
In the construction of terminological ontologies based on the titles on a field of industrial symbios and industrial symbiotic networks, we found that the precision of modeling and demonstrations of domain knowledge mostly depends on the settings of the text analysis parameters as in this case (paper) is the value of the parameter “k”. K-means algorithm is used to sort input elements (article titles in our case) in “k groups” based on the substantive similarity of the elements. As the best choice in the case study was the division of input texts into seven groups (concepts) that was established at the first hierarchical level of the ontology and with three subgroups (subconcepts) at the second level. The size of the input file is also important and in present the number of titles covered in the primary reserch. Input data must be sufficient quantitatively, so the program tool would be able to analyze them and obtain meaningful analysis and representative results.
The fourth concept of ontology is numerically the most extensive, as it includes 52 titles. The articles that are mostly categorized in the fourth concept of ontology are:
OntoGen is recognised to be a useful tool for modeling domain knowledge. OntoGen is very logical in a detailed review of keywords that are automatically identified. Another plus of OntoGen is, that helps to recognize keywords displayed that could be manually improved or renamed (Bizjak 2014, 35−39). For an even more detailed implementation of domain knowledge it would also make sense to include summaries of articles from the period under consideration and thus obtain even more detailed demonstrations of publications about the field of industrial symbiosis and their networks.