Ebook: Information Modelling and Knowledge Bases XXXV
The volume and complexity of information, together with the number of abstraction levels and the size of data and knowledge bases, grow continually. Data originating from diverse sources involves a combination of data from traditional legacy sources and unstructured data requiring backwards modeling, meanwhile, information modeling and knowledge bases have become important contributors to 21st-century academic and industrial research.
This book presents the proceedings of EJC 2023, the 33rd International Conference on Information Modeling and Knowledge Bases, held from 5 to 9 June 2023 in Maribor, Slovenia. The aim of the EJC conferences is to bring together experts from different areas of computer science and from other disciplines that share the common interest of understanding and solving the problems of information modeling and knowledge bases and applying the results of research to practice. The conference constitutes a research forum for the exchange of results and experiences by academics and practitioners dealing with information and knowledge bases. The topics covered at EJC 2023 encompass a wide range of themes including conceptual modeling; knowledge and information modeling and discovery; linguistic modeling; cross-cultural communication and social computing; environmental modeling and engineering; and multimedia data modeling and systems. In the spirit of adapting to the changes taking place in these areas of research, the conference was also open to new topics related to its main themes.
Providing a current overview of progress in the field, this book will be of interest to all those whose work involves the use of information modeling and knowledge bases.
Information Modeling and Knowledge Bases has become an important technology contributor for the 21st century’s academic and industry research. It addresses the complexities of modeling in digital transformation and digital innovation, reaching beyond the traditional boarders of information systems and computer science academic research.
The amount and complexity of information itself, the number of abstraction levels of information, and the size of databases and knowledge bases are continuously growing. The diversity of data sources combines data from traditional legacy sources to stream based unstructured data having need for backwards modelling. Conceptual modelling is one of the sub-areas of information modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas of modelling and knowledge bases to which more attention should be paid. Therefore, philosophy and logic, cognitive science, knowledge management, linguistics, and management science as well as machine learning and AI are relevant areas, too.
In the conference, there will be three categories of presentations, i.e., full papers, short papers, and invited papers. The international conference on information modelling and knowledge bases originated from the co-operation between Japan and Finland in 1982 as the European Japanese conference (EJC). Then professor Ohsuga in Japan and Professors Hannu Kangassalo and Hannu Jaakkola from Finland (Nordic countries) did the pioneering work for this long tradition of academic collaboration. Over the years, the conference gradually expanded to include European and Asian countries, and gradually spread through networks of previous participants to other countries. In 2014, with this expanded geographical scope, the European Japanese part in the title was replaced by International. The conference characteristics include opening with a keynote session followed by presentation sessions with enough time for discussions. The limited number of participants is typical for this conference.
The 33rd International Conference on Information Modeling and Knowledge Bases (EJC 2023) held at Maribor, Slovenia constitutes a research forum for the exchange of scientific results and experiences of academics and practitioners dealing with information and knowledge. The main topics of EJC 2023 cover a wide range of themes extending the knowledge discovery through Conceptual Modelling, Knowledge and Information Modelling and Discovery, Linguistic Modelling, Cross-Cultural Communication and Social Computing, Environmental Modeling and Engineering, and Multimedia Data Modelling and Systems. The conference has also been open to new topics, related to its main themes. In this way, the content emphases of the conferences have been able to adapt to the changes taking place in the research field.
The EJC 2023 was hosted by the Faculty of Electrical Engineering and Computer Science of the University of Maribor, Slovenia on June 5 – June 9, 2023. The contributions of this proceeding feature eighteen reviewed, selected, and upgraded publications as well as one keynote and three invited contributions that are the result of presentations, comments, and discussions during the conference. We thank all colleagues for their support in making this conference successful, especially the program committee, organization committee, and the program coordination team, especially Professor Naofumi Yoshida, who maintains the paper submission and reviewing systems and compiles the files for this book.
Every object and every idea can be used as a model in an application scenario, if it becomes useful in the scenario as an instrument in a function. Through this use and function, an object or idea becomes a model, at least for a certain or long time for the respective model user in its context and environment.
Models are works of art of thinking. They come in the most diverse forms: small ingenious, ever-present, medium-sized or even elaborate ones of imposing size and full of hidden secrets. Quiet ones, animated by a flash of inspiration. Groundbreaking and revolutionary. Methodically overwhelmingly sophisticated ones that stand on the shoulders of giants. Simple ones that are so well put together that everyone likes them and understands them and cannot refuse them.
Modelology is a novel discipline that handles model development and usage in a systematic and well-founded way. We introduce the central kernels of this new science, art, and culture.
The important process of time series analysis for public health data is to determine target data as a semantic discrete value, according to a context from continuous phenomenon around our circumstance. Typically, each field of experts has their own fields’ specific and practical knowledge to specify an appropriate target part of data which contains the key features of their intended context in each analysis. Those are often implicit, thus not defined as systematically and quantitatively. In this paper, we present a context-based time series analysis and prediction method for public health data. The most essential point of our approach is to express a basis of time series context as the combination of the following 5 elements (1: granularity setting on time axis, 2: feature extraction method, 3: time-window setting, 4: differential computing function, and 5: pivot setting) to determine target data as semantic discrete values, according to the time series context of analysis for public health data. One of the main features of our method is to create different results by switching time series contexts. The method realizes 1) introducing a new normalization (context expression) method to fix a target reference data for time series analysis and prediction according to a context, and 2) presenting a process to generate semantic discrete values reflecting the 5 elements. And the significant features of the proposing method are 1) our context definition realizes the closed world of the semantic differential computing on time axis from the viewpoint of database system, and 2) the 5 elements enable to explicit and quantify experts’ semantic viewpoint of specifying a certain reference data according to a context for each analysis and prediction. As our experiment, we have realized analysis and prediction by applying actual public health data. The results of the experiments show the prediction feasibility of our method in the field of public health data, effectiveness to generate results for discussion regarding switching context, and applicability to express time series context of an expert knowledge for analysis and prediction as combination of the 5 elements to make the knowledge explicit and quantitative expression.
Our investigation seeks to enhance the understanding of responsible artificial intelligence. The EU is deeply engaged in discussions concerning AI trustworthiness and has released several relevant documents. It’s crucial to remember that while AI offers immense benefits, it also poses risks, necessitating global oversight. Moreover, there’s a need for a framework that helps enterprises align their AI development with these international standards. This research will aid both policymakers and AI developers in anticipating future challenges and prioritizing their efforts. In our study, we delve into the essence of responsible AI and, to our understanding, introduce a comprehensive definition of the term. Through a thorough literature review, we pinpoint the prevailing trends surrounding responsible AI. Using insights from our analysis, we’ve also deliberated on a prospective framework for responsible AI. Our findings emphasize that human-centeredness should prioritized. This entails adopting AI techniques that prioritize ethical considerations, explainability of models, and aspects like privacy, security, and trustworthiness.
“Semantic space creation” and “distance-computing” are basic functions to realize semantic computing for environmental phenomena memorization, retrieval, analysis, integration and visualization. We have introduced “SPA-based (Sensing, Processing and Actuation) Multi-dimensional Semantic Computing Method” for realizing a global environmental system, “5-Dimensional World Map System”. This method is important to design new environmental systems with Cyber-Physical Space-integration to detect environmental phenomena occurring in a physical-space (real space). This method maps those phenomena to a multi-dimensional semantic-space, performs semantic computing, and actuates the semantic-computing results to the physical space with visualizations for expressing environmental phenomena, causalities and influences. As an actual system of this method, currently, the 5D World Map System is globally utilized as a Global Environmental Semantic Computing System, in SDG14, United-Nations-ESCAP: (https://sdghelpdesk.unescap.org/toolboxes). This paper presents a semantic computing method, focusing on “Time-series-Analytical Semantic-Space Creation and Semantic Distance Computing on 5D World Map System” for realizing global environmental analysis in time-series. This paper also presents the time-series analysis of actual environmental changes on 5D World Map System. The first analysis is on the depth of earthquakes Earthquake with time-series semantic computing on 5D World Map System, which occurred around the world during the period from Aug. 23rd to Aug. 28th, 2014, and Jan 7th to Jan. 13th, 2023. The second is the experimental analysis of the time-series difference extraction on glacier melting phenomena in Mont Blanc, Alps, during the period from 2013 to 2022, and Puncak Jaya (Jayawijaya Mountains), Papua, during the period from 1991 to 2020 as important environmental changes.
The current state of energy generation and consumption in the world, where many countries rely on fossil fuels to meet their energy demands, poses significant challenges in terms of energy security and environmental degradation. To address these challenges, the world is shifting towards renewable energy sources (RES), which are not only environmentally sustainable but also have the potential to reduce dependence on fossil fuels. However, the intermittency and seasonality of RES arise new challenges that must be addressed. To overcome these challenges, energy storage systems (ESS) are becoming increasingly important in ensuring stability in the energy mix and meeting the demands of the electrical grid. This paper introduces charging and discharging strategies of ESS, and presents an important application in terms of occupants’ behavior and appliances, to maximize battery usage and reshape power plant energy consumption thereby making the energy system more efficient and sustainable.
The successful operation of the laser-based synchronization system of the European X-Ray Free Electron Laser relies on the precise functionality of numerous dynamic systems operating within closed loops with controllers. In this paper, we present how data-based machine learning methods can detect and classify disturbances to such dynamic systems based on the controller output signal. We present 4 feature extraction methods based on statistics in the time domain, statistics in the frequency domain, characteristics of spectral peaks, and the autoencoder latent space representation of the frequency domain. These feature extraction methods require no system knowledge and can easily be transferred to other dynamic systems. We combine feature extraction, fault detection, and fault classification into a comprehensive and fully automated condition monitoring pipeline. For that, we systematically compare the performance of 19 state-of-the-art fault detection and 4 classification algorithms to decide which combination of feature extraction and fault detection or classification algorithm is most appropriate to model the condition of an actively controlled phase-locked laser oscillator. Our experimental evaluation shows the effectiveness of clustering algorithms, showcasing their strong suitability in detecting perturbed system conditions. Furthermore, in our evaluation, the support vector machine proves to be the most suitable for classifying the various disturbances.
In this paper, we proposed a new workplace data model and its calculation method. The method was designed to calculate appropriate workplace according to the intents (activities) and situations of a worker. The data model was designed as a semantic space with three knowledge bases: ‘Activity-affecting’, ‘Place-determining’, and ‘Activity and Place’. Experiments were conducted to show the different results depending on activities and the contexts of the workplace and presented the feasibility of the proposed data model and calculation method.
In this paper, an implementation method of GACA, Global Art Collection Archive, is proposed. Each museum maintains their own archives of art collections. GACA dynamically integrate those collection data of artworks in each museum archive and provide them with REST API. GACA works as a integrated data platform for various kinds of viewing environment of artworks such as virtual reality, physical exhibitions, smartphone applications and so on. It allows users not only to view artworks, but also to experience the creativity of artworks through seeing, feeling, and knowing them, inspiring a new era of creation.
This paper introduces Art Sensorium Project that is founded in Asia AI Institute of Musashino University. A main target of the project is to design and implement a system architecture of unified art collections for virtual art experiences. To provide art experiences, a projection-based VR system, called Data Sensorium, is used to stage art materials in a form of real-sized virtual reality. Furthermore, a system architecture of a multidatabase system for heterogeneous art collection archives is presented, so a set of integrated art data is applied to Data Sensorium for newly generated art experiences.
Throughout the COVID-19 pandemic, the news media played a crucial role in disseminating information to the public and influencing public opinion, such as governmental responses to the outbreak. The way the pandemic and pandemic-related news were handled varied across different countries and regions. This study analyzes a random selection of newspaper articles from three different sources: the German Bild, the Japanese Yomiuri Shimbun, and the American USA Today. The aim is to shed light on how these newspapers reported on COVID-19 during its initial stages, from January to March 2020. The study presents initial findings from comparing the coverage of these three newspapers with respect to (1) mentioned actors, (2) depicted regions, and (3) mentioned themes. In addition, we compare the results of our analysis with cultural values and discuss how the cultural context influences the coverage. Japan’s Yomiuri Shimbun places more emphasis on the government’s response to the pandemic, while Germany’s Bild and America’s USA Today focus more on how the pandemic has affected the lives of citizens and the individual measures taken to deal with the virus. The results show the contrast between the cultural values of individualism and uncertainty in the media coverage of the pandemic.
Design principles are used to specify design knowledge and describe the aim of artefact instantiation. Accessibility research aims to create artefacts that can be used by all users. However, schemes for design principles lack the tools to define accessibility explicitly. This study proposes extensions to scheme design principles for accessibility-related design science research. We draw accessibility domain-specific characteristics from the literature to include accessibility in design principles for Human-Computer Interaction (HCI) instantiations. We extended the components of design principles with the following attributes: HCI Artefact Features; Contextual factors; Computer Input Modalities; Computer Output Media; Human Sensory Perception; Human Cognition; Human Functional Operations. We devised a checklist for researchers to follow the variations in accessibility. The extensions are intended to foster researchers to incorporate accessibility in producing a more accurate formulation of design principles.
The Thammasat AI City distributed platform is a proposed AI platform designed to enhance city intelligent management. It addresses the limitations of current smart city architecture by incorporating cross-domain data connectivity and machine learning to support comprehensive data collection. In this study, we delve into two main areas, that is, monitoring and visualization of city ambient lighting, and indoor human physical distance tracking. The smart street light monitoring system provides real-time visualization of street lighting status, energy consumption, and maintenance requirement, which helps to optimize energy consumption and maintenance reduction. The indoor camera-based system for human physical distance tracking can be used in public spaces to monitor social distancing and ensure public safety. The overall goal of the platform is to improve the quality of life in urban areas and align with sustainable urban development concepts.
The energy security of the EU is a current issue for all member countries. The EU’s energy policy aims for diversification of energy resources and energy independence. After 2022, this issue has worsened. The article analyzes the main risks to the energy security of EU countries and industries located in these countries. The dynamics of energy consumption by different sectors of the EU economy are considered and the impact of changes in the energy sector on the economic security of businesses is evaluated. Approaches to modeling the impact of transformations in the energy sector on the economic security of businesses are discussed. A simulation model of a three-sector energy market has been developed. The driver of changes in the model is the minimization of carbon emissions. Experiments were conducted to simulate the development of the energy market from 2012 to 2052. Digital modeling showed that in a case of “gas blackmail” the most probable scenario means increasing of dirty energy sources with high carbon emission.
In this article, the ongoing research on collaborative prototype development between university and enterprises is presented. The study of project featured numerous pilot cases and prototypes, executed in collaboration with organizations to address real-world challenges. This article assesses the appropriateness of the Descriptive Model for Prototyping Process (DMPP) for research project applications. We delve into two primary facets: the synergy between universities and enterprises, and the potential for artifact reusability within the DMPP. The article presents various pilot cases from the KIEMI project, highlighting the DMPP’s role in each. Furthermore, the paper evaluates the model, sets forward the challenges faced, and, finally, discusses topics for future research.
We introduced a novel approach to global sign language recognition by leveraging the capabilities of the Editable Mediator. Traditional methods have often been limited to recognizing sign languages from specific linguistic regions, necessitating ad hoc implementation for multilingual regions. Our method aims to bridge this gap by providing a unified framework for recognizing sign languages in various linguistic areas and promoting global communication. At the core of our system is the Editable Mediator, a mechanism that determines the actual sign meaning from various local hand-shape recognitions. Instead of focusing on specific sign language notations, such as HamNoSys, our approach emphasizes the recognition of common primitive actions shared across different sign languages. These primitive actions are recognized by multiple modules, and their combinations are interpreted by the Editable Mediator to determine the intended sign-language message. This architecture not only simplifies the recognition process, but also offers flexibility. By merely editing the Editable Mediator, our system can adapt to various sign languages worldwide without the need for extensive retraining or ad hoc implementation. This innovation reduces barriers to introducing new sign language systems and promotes a more inclusive global communication platform.
A significant amount of real-world information is documented in simple text format, such as messages found on social networks. These messages include various types of data, including spatial details, which can be extracted through natural language processing. The extracted data can be represented as a plain topological graph, stored as tuples that describe individual edges. This paper outlines an algorithm that utilizes these tuples to generate a simplified map.
This paper presents an important application of 5D World Map System with a Risk-Resilience calculation and visualization method using time-series multilayered data for disasters and environmental change analysis to make appropriate and urgent solutions to global and local environmental phenomena in terms of short and long-term changes. This method enables the calculation of the current risk and resilience of a target region or city for disasters and rapid environmental changes, based on the analysis of past time-series changes of natural and socioeconomic factors’ distribution. This method calculates the total risk and resilience to disaster as a total aggregate value that reflects the amount of change in each variable in the past, by transforming multidimensional and heterogeneous variables into a form that allows comparative and arithmetic operations through geographical normalization and projection. As an implementation and experiments, we apply our method to assessing the role of forests in urban disaster resilience as an example, by analyzing time-series changes in vegetation and forest distribution and their relationships in urban areas. Specifically, using GIS, satellite data, demographic data, urban infrastructure data, and disaster data, we analyze the relationship among urban disaster occurrence and 1) population density, 2) urban infrastructure development, and 3) forest distribution and calculate “urban-forest-disaster risk/resilience”.
This paper aims to analyze the phenomenon of inapplicability of experience, which means that sometimes we make mistakes when we use our past experience to solve current problems. We propose a knowledge model based on the concept of “dark-matter”, which is a term used to describe the time-related data that is hidden from our observation. We use a two-dimensional matrix to represent both time-related and non-time-related data, and we call it space. We also introduce the concept of parallel spaces, which are composed of several spaces that can explain different situations and outcomes. We use case studies to illustrate how knowledge is generated and expressed using “dark-matter” and parallel spaces. We also reveal the reason for the inapplicability of experience and suggest some solutions. The contribution of this paper is that we provide a new perspective and a new model to understand and process knowledge based on “dark-matter” and parallel spaces.
This paper proposes a spatio-temporal and categorical correlation computing method for induction and deduction analysis. This method is a data analytics method to reveal spatial, temporal, and categorical relationships between two heterogeneous sets in past events by correlation calculation, thereby finding insights to build new connections between the sets in the future. The most significant feature of this method is that it allows inductive and deductive data analysis by applying context vectors to compute the relationship between the sets whose elements are time, space, and category. Inductive analysis corresponds to data mining, which composes a context vector as a hypothesis to extract meaningful relationships from trends and patterns of past events. Deductive analysis searches past events similar to a context vector’s temporal, spatial, and categorical conditions. Spatio-temporal information about the events and information such as frequency, scale, and category are used as parameters for correlation computing. In this method, a multi-dimensional vector space that consists of time, space, and category dimensions is dynamically created, and the data of each set expressed as vectors is mapped onto the space. The similarity degree of the computing shows the strength of relationships between the two sets. This context vector is also mapped onto the space and is calculated distances between the context vector and other vectors of the sets. This paper shows the details of this method and implementation method and assumed applications in commerce activities.
The central concept of browser fingerprinting is the collection of device-specific information for identification or security purposes. This chapter provides an overview of the research conducted in the field of browser fingerprinting and presents an entry point for newcomers. Relevant literature is examined to understand the current research in the field of browser fingerprinting. Both research in the field of crafting browser fingerprints and protection against it is included. Finally, current research challenges and future research directions are presented and discussed.
In this chapter, we investigate the complex process of analyzing and understanding the factors that influence individuals’ access to and participation in digital education within the Higher Education context. While the digital transformation in Higher Education Institutions (HEI) has produced numerous benefits for both students and educators, it has also brought forth challenges, particularly for students with special educational needs and disabilities (SEND). A literature review was conducted, to gain insight into the specific requirements of this student demographic. This review aimed to identify the multifaceted factors that impact e-inclusion within Higher Education. Our research resulted in the identification of 24 different factors that should be considered when evaluating e-inclusion within HEI. These factors serve as essential indicators in the assessment of the accessibility and inclusivity of digital education, allowing for a more multifaceted understanding of the dynamics in the Higher Education landscape.
Networks and data centres are under a lot of stress due to the rapid growth of connected devices and the significant amount of data that they produce. While data centres are struggling with heavy workloads, the cloud-native IT solutions underutilise the increasingly powerful clients. By bringing computational capabilities closer to the edge, the Edge Computing direction offers a way to alleviate the pressure on data centres, reduce network traffic, and opens the way for novel applications that would take full advantage of the benefits it brings. Edge Computing can also supplement existing applications by giving them the resources to run new, more complex operations, or improve existing ones partially. Edge applications are less reliant on the cloud, and provide more stability and customisation to globally distributed systems.