Knowledge Granularity and Representation of Knowledge: Towards Knowledge Grid

Mach, Maria; Owoc, Mieczysław L.

doi:10.1007/978-3-642-16327-2_31

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…The ultimate objective is to transform information into knowledge and later even into wisdom. Knowledge, in tandem with business intelligence tools, shows out to be effective in supporting activities connected to the quality of the decision-making process (Mach & Owoc, 2010;Wieder & Ossimitz, 2015). Still, additional processing of information is not the aim of this study.…”

Section: Figure 22 Six Steps Of Conversion Of Data Into Informationmentioning

confidence: 93%

Reduction of information asymmetry in e-commerce: the web scraping approach

Hadasik

2024

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The realm of science and its progress have always been built on research, and ultimately on the experience that precedes it. In order to arrive at scientific results appropriately, investigations must be conducted and those are done when data is accessible. In the modern era of digital world and society, data is gathered much simpler than before the widespread availability of computers and broadband networks. Sadly, it is a challenge for a beginner researcher to access databases gathered by various organizations since they are safeguarded and available only to a small audience (sometimes for an additional price). As data collecting becomes much simpler when we have access to IT solutions of the 21st century, it is even more convenient with a utilization of an application that automatically gathers and organizes data. Such an automized database building technique may become notably beneficial when we have a desire to collect unstructured data from a given period and from a specific website, in- dependently from the industry. This is where web scraping – a strategy that includes obtaining data from websites, is handy. In actuality, data extraction (especially approaches linked to the very web scraping) comprises of a large variety of distinct methods and technologies, such as data analysis, natural language syntax analysis, and information security. To get the most out of their advantages, it is of paramount importance to understand how they function. The role of information in the purchasing process has been extensively de- scribed in the literature. In doing so, attention was often drawn to the problem of information asymmetry – when the individual customer is informationally in a weaker position than the seller. This problem becomes particularly important in online shopping. The purpose of this work is to create an automated tool based on the web scraping technique that is designed to reduce the infor- mation asymmetry occurring in the buyer-seller relationship. The plane for de- picting the phenomenon of information asymmetry and the established web scraping tool is the automotive sector, with a particular focus on the essence of classifieds portal as a platform for matching buyers with sellers. The case of the largest automotive classifieds portal in Poland, which is OTOMOTO, was used in this study. The theoretical backdrop of this research, which serves as its begin- ning point, will be the problem of the uncertainty of judgments, coming from information asymmetry, an example of which is described in the groundbreak- ing essay by Akerlof (1970). In this work, the baseline environment for illustrating the problem of information asymmetry is also the automotive industry. In order to achieve the goal of this study, the following research questions were posed: RQ1. What are the implications of information asymmetry for judgment uncer- tainty in online transactions, and how can they be mitigated? RQ2. How can web scraping tools be designed to specifically address the chal- lenges of information asymmetry in the e-commerce sector? RQ3. What is the potential impact of reducing information asymmetry through web scraping on the overall efficiency and fairness of the e-commerce market, especially in automotive industry? This book is organized as follows. Chapter 1 outlines the theoretical back- ground with specific attention dedicated to the issue of information asymmetry as articulated in Akerlof (1970). Chapter 2 discusses the theoretical foundation of data extraction from internet resources (with particular focus on web scrap- ing), their characteristics, particularly legal as well as ethical issues, and the necessity to deploy data collection technologies in the research setting. In Chapter 3, a tool for data extraction created together with a suitable database to be able to harvest data from the OTOMOTO advertising site is discussed. The Chapter also provides technical elements including the Python language upon which the constructed tool is predicated. Chapter 3 additionally covers a practi- cal portion of the research in which a sample evaluation of the automotive in- dustry in Poland is done, which draws on the data gathered from OTOMOTO advertisement portal with the assistance of the built web scraping tool. The book can be found useful for researchers, academics, and data scien- tists, offering scholarly insights into reducing information asymmetry in e-commerce through web scraping. E-commerce practitioners and business owners in the automotive sector can gain competitive advantages by applying the book’s practical guidance for market analysis. The employment of the cre- ated web scraping tool, once quantitative data is retrieved, can be used by, e.g., data analysts, for the advanced analysis of the particular market, the verifica- tion of research hypotheses and the facilitation of decision-making processes. Policy makers, regulators, and legal professionals will find valuable perspectives on the legal implications of web scraping in enhancing information transparency. On the other hand, everyday customers of online stores may benefit from the theoretical and practical value that this book brings, especially with their will- ingness to compare offers posted in advertisements, further analyze them, and make the right purchase decision for themselves based on more complete access to information (or put another way: minimized uncertainty among buyers). This monograph is an adaptation of the author’s master’s thesis with the same title, which was defended in July 2021 at the University of Economics in Katowice (Poland) under the supervision of Associate Professor Maria Mach- -Król, PhD. The thesis was defended with a very good result and served as the basis for issuing a Master’s degree diploma with distinction to the author. The thesis was awarded the second degree prize in the 2022 nationwide competi- tion of diploma theses in the field of economic informatics, which was awarded by the Scientific Society of Economic Informatics (Częstochowa, Poland).

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Section: Figure 22 Six Steps Of Conversion Of Data Into Informationmentioning

confidence: 93%

Reduction of information asymmetry in e-commerce: the web scraping approach

Hadasik

2024

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“…On the one hand, the concept of granularity regained importance in areas from computing to human reasoning, as a component of knowledge representation in cognition and knowledge production. Knowledge is structured and organised or "encapsulated" in different-sized pieces and levels of detail that enhance accuracy and flexibility of interpretation (Mach & Owoc, 2010). According to Pawlak (1998), knowledge granularity is strictly connected with the indiscernibility of the smallest discrete knowledge pieces.…”

Section: New Ontologies Of Knowledgementioning

confidence: 99%

Changing Scientific Production in Design

Lupo

2022

Diid

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In this essay the Author claims that scientific and academic publishing should comply with the epistemological changes in knowledge production and cognition due to the digital revolution. In calling on the design community for a responsible systemic and cultural change in the publishing ecosystem, the essay discusses the more relevant challenges and new forms, and envisions processes of publication that can enable the emergent diversities of knowledge.

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“…• heterogeneous instances: over time different occurrences of the same value have different meanings in a domain extension; for instance, if the organization merge or split departments, then the preserved naming represent a different set of resources (e.g. employees, faculties); • cardinality changes: in particular, cardinality relationships between domains might also change over time; in other words, the number of occurrences in one entity which are associated to the number of occurrences in another are not always constant; for example, a 1-to-n relationship between departments and faculties may be changed to m-to-n as a result of new legal regulations; • granularity transition: from existing population values, having different granularity, might be added to a domain extension; for instance, the numeration of rooms or buildings might be changed due to the merge or acquisition [84,85]; • encoding changes: particular values might have also encoded meaning, which neither is known, nor provided elsewhere; for example, the naming of projects successfully delivered are eventually different from the others (failed, cancelled, etc. ; see [86]); • time zone and unit differences: organization sites use local time zone and units which globally differ; thus directly comparing such values may be irrelevant; • identifier changes: the organization needs changes over time; as a consequence the indexing strategies may also change over time, leading in parallel or overlapping naming schemas; for instance, the codes of the products, previously 4-digits numbers, now having additional 6 zeros, are different for both the users and IT systems; • field recycling: in some systems it is difficult or even infeasible to alter certain database properties; in this case there might be a need to shrink the database or even implement a new instance with a different naming schema, replacing the existing ones; for example, a company might shift from hierarchical to a matrix structures, remodeling data structures [87,88,89].…”

Section: Knowledge Dynamicsmentioning

confidence: 99%

Dynamical Aspects of Knowledge Evolution

Owoc

Weichbroth

2019

IFIP Advances in Information and Communication Technology

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Undeniably, in this rapidly changing world some knowledge granules change over time. Tracking these changes seems to be one of the most crucial processes in knowledge management. Every potential change is a result of knowledge adoption and application to solve a given problem or task in particular domains. However, there is a lack of a model that provides an eventdriven framework, along with the core adoption process explicitly expressed with related factors, which together serve as an efficient tool to adopt and reuse knowledge on one hand, while on the other, to measure and evaluate the various aspects of knowledge quality and usefulness. This paper aims to fill this gap by introducing a knowledge adoption process and an ontology-aided encapsulation knowledge (OAKE) model. While the former breaks down the tacit adoption process into two explicit sub-processes and measurable factors, the latter exposes knowledge evolution over time by a sequence of recorded events.

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Knowledge Granularity and Representation of Knowledge: Towards Knowledge Grid

Cited by 11 publications

References 10 publications

Reduction of information asymmetry in e-commerce: the web scraping approach

Reduction of information asymmetry in e-commerce: the web scraping approach

Changing Scientific Production in Design

Dynamical Aspects of Knowledge Evolution

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