Ebook: Information Modelling and Knowledge Bases XXXIV

Information Modeling and Knowledge Bases have become important contributors to technology for academic and industrial research in the 21st century. They address the complexities of modeling in digital transformation and digital innovation, reaching beyond the traditional borders of information systems and academic computer-science research.

The amount and complexity of information itself, the number of abstraction levels of information, and the size of databases and knowledge bases are continually growing, as is the diversity of data sources, from combined data from traditional legacy sources to stream-based, unstructured data requiring backwards modeling. Conceptual modeling is one of the sub-areas of information modeling. 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 the problems of information modeling and knowledge bases and applying the results of research to practice. We also aim to recognize and study the new areas of modeling and knowledge bases to which more attention should be paid; this means that philosophy and logic, cognitive science, knowledge management, linguistics, and management science, as well as machine learning and AI, are also relevant areas.

There will be three categories of presentations at the conference: full papers, short papers, and position papers. The international conference on information modeling and knowledge bases originated in 1982 as the European Japanese conference (EJC); a co-operation between Japan and Finland. That is when Professor Ohsuga in Japan and Professors Hannu Kangassalo and Hannu Jaakkola from Finland did the pioneering work to bring about this long tradition of academic collaboration. Over the years, the conference has gradually expanded to include European and Asian countries, and has gradually spread to other countries through networks of previous participants. With this expanded geographical scope, the European/Japanese part of the title was replaced by International in 2014. The characteristics of the conference include opening with a keynote session, followed by presentation sessions, and leaving enough time for discussions. A limited number of participants is also typical for this conference.

The 32nd International conference on Information Modeling and Knowledge Bases (EJC 2022), held in Hamburg, Germany, constitutes a research forum drawing together those academics and practitioners dealing with information and knowledge for the exchange of scientific results and experiences. The main topics of EJC 2022 cover a wide range of themes, extending knowledge discovery through 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. The conference has always been open to new topics related to its main themes. In this way, the content emphasis of the conferences has been able to adapt to the changes taking place in the research field.

The 32nd International Conference of Information Modeling and Knowledge Bases – EJC 2022 was hosted by the Department of Computer Science of the University of Applied Sciences Hamburg, Germany. Due to regulations and restrictions caused by Corona virus, the conference was held as a hybrid event this year from May 30th – June 3rd 2022. The contributions in this proceeding feature 19 reviewed, selected, and upgraded 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, in particular, Professor Naofumi Yoshida, who maintains the paper submission and reviewing systems and who compiled the content for this book.

Editors

Marina Tropmann-Frick

Hannu Jaakkola

Bernhard Thalheim

Yasushi Kiyoki

Naofumi Yoshida

The “SPA-based 5D World Map System” realizes Cyber-Physical-Space integration to detect changes of environmental phenomena with real data resources in a physical-space (real space), map them to the cyber-space to make knowledge bases and analytical computing, and actuate the computed results to the real space with visualization for expressing the causalities and influences. This paper presents an important application of 5D World Map System, adding “AI-Sensing” functions for “Global Environment-Analysis” to make appropriate and urgent solutions to global and local environmental phenomena in terms of short and long-term changes. Focusing on the ocean plastic garbage issues, this paper describes the methodology of AI-Sensing, the preliminary models and experiments on the accuracy of AI-Sensing and the substantiative experiments on the feasibility and effectiveness of AI-Sensing with real local data. In addition, the example outputs of the integration of AI-Sensing algorithm and SPA-based 5D World Map System and the future direction of a collaborative project for ocean plastic-garbage reduction are introduced.

Through technology, it is essential to seamlessly bridge the divide between diverse speaking communities (including the signer (the sign language speaker) community). In order to realize communication that successfully conveys emotions, it is necessary to recognize not only verbal information but also non-verbal information. In the case of signers, there are two main types of behavior: verbal behavior and emotional behavior. This paper presents a sign language recognition method by similarity measure with emotional expression specific to signers. We focus on recognizing the sign language conveying verbal information itself and on recognizing emotional expression. Our method recognizes sign language by time-series similarity measure on a small amount of model data, and at the same time, recognizes emotion expression specific to signers. Our method extracts time-series features of the body, arms, and hands from sign language videos and recognizes them by measuring the similarity of the time-series features. In addition, it recognizes the emotional expressions specific to signers from the time-series features of their faces.

In order to provide art exhibitions in a virtual space which integrates various data of an art museum and gives an emotional experience, the Virtual Art Exhibition System is proposed. This research uses a multi-database called Artizon Cloud to display museum data, combinate with technologies called Data Sensorium and Torus Treadmill to project images and enable visitors to walk around the virtual museum. Moreover, the virtual museum will exploit the users intentions and be personalized, automatically generating further art exhibitions.

Social media analysis has become a major instrument for data-driven tourism. It allows surveying visitor behavior on multiple scales. Considering the geographical characteristics of users’ posts from social media platforms, we were able to address more specific questions related to the place type selection patterns of the visitors. In this paper, we present OPENLOSTCAT, our first-order-logic-based location categorizer applicable for modeling location types depending on Open-StreetMap data. We report our findings revealed by this tool on more than one year’s collection of global-scale geotagged Twitter data, focused on potential trail-related hiking and trekking activities. We categorized visited locations in our experiments based on place accessibility – transport and trail infrastructure –, and analyzed these categories according to the travel distance taken by visitors in general to reach these areas. Our comparisons reveal seasonal characteristics, continental differences (between Europe and North America), as well as specifics related to selected recreational areas. Besides these preliminary findings available for further verification, we show both the perspectives and limitations of our approach for future improvements and experiments.

Navigation and an agent’s map representation in a multi-agent system become problematic when agents are situated in complex environments such as the real world. Challenging modifiability of maps, long updating period, resource-demanding data collection makes it difficult for agents to keep pace with rather quickly expanding cities. This study presents the first steps to a possible solution by exploiting natural language processing and symbolic methods of supervised machine learning. An adjusted algorithm processes formalized descriptions of one’s journey to produce a description of the journey. The explication is represented employing Transparent Intensional Logic. A combination of several explications might be used as a representation of spatial data, which may help the agents to navigate. Results of the study showed that it is possible to obtain a topological representation of a map using natural language descriptions. Collecting spatial data from spoken language may accelerate updating and creation of maps, which would result in up-to-date information for the agents obtained at a rather low cost.

This paper presents a reinforcement learning approach for foreign exchange trading. Inspired by technical analysis methods, this approach makes use of technical indicators by encoding them into Gramian Angular Fields and searches for patterns that indicate price movements using convolutional neural networks (CNN). In addition to the policy that determines the action to take, an extra regression head is utilized to determine the size of market orders. This paper also experimentally shows that maximizing the return of individual trade or cumulative reward in a finite time window results to better performance.

This research proposes an AI platform for data sharing across multiple domains. Since the data in the smart city concept are domain-specific processed, the existing smart city architecture is suffered from cross-domain data interpretation. To go beyond the digital transformation efforts in smart city development, the AI city is created on the architecture of cross-domain data connectivity and transform learning in the machine learning paradigm. In this research, the health and human behavioral data are targeted on human traceability and contactless technologies. To measure the inhabitants quality of life (QoL), the primary emotion expression study is conducted to interpret the emotional states and the mental health of people in the urbanized city. The results of information augmentation draw attention to the immersive visualization of the Thammasat model.

Research work on machine learning techniques has been going on since the invention of computers. With the development of machine learning techniques, researches on how knowledge is expressed in computers and the learning process of knowledge have become more important. Unlike other machine learning models such as artificial neural networks, in the previous work of this paper, a machine learning model based on the concept of “dark-matter” is presented. In this model, matrixes are used to represent temporal and non-temporal data. The term “matter” is used to denote non-temporal data. The term “dark-matter”, on the other hand, is used to represent temporal data. In this paper, an exploratory research on the expression of knowledge and its generation process based on the concept “dark-matter” is presented. A case study is used to illustrate how knowledge is generated and expressed. The contribution of this paper is that new methods of knowledge generation and expression are proposed based on the concept of “dark-matter”. In the paper, first, the concept of “dark-matter” is briefly reviewed. After that, the methods of knowledge generation and knowledge expression are illustrated with examples. The process of knowledge generation is also illustrated with examples. Finally, the relationship between knowledge and “dark-matter” is revealed.

BigRobot Mk.2, which creates the body motion of a giant, is described. The robot has 10 actuated joints so that it can emulate the body motion of human. A pilot rides on the top of the robot and it moves according with walking action of the pilot. This function provides the pilot to the body sensation that is extended to an 8-m giant. The basic design, implementation, and performance evaluation of BigRobot Mk.2 are presented.

Though there is a huge amount of the so-called epistemic logics that deal with propositional attitudes, i.e., sentences of the form “a knows that P”, their ‘wh-cousins’ of the form “a knows who is a P”, “a knows what the P is”, “a knows which Ps are Qs”, etc., have been, to the best of my knowledge, almost neglected. A similar disproportion can be observed between the analysis of Yes-No questions, which has been under scrutiny of many erotetic logics, and Wh-questions which have been dealt with just by a few authors. To fill this gap, we have analysed Wh-questions in Transparent Intensional Logic (TIL) and adjusted Gentzen’s system of natural deduction to TIL natural language processing; thus, our TIL question-answering system can answer not only Yes-No questions but also derive answers to Wh-questions. In this paper, I am going to apply these results to the analysis of sentences containing a ‘knowing-wh’ constituent. In addition, I will analyse the relation between ‘knowing-that’ and ‘knowing-wh’. For instance, if a knows that the Mayor of Ostrava is Mr Macura, can we logically derive that a knows who is the Mayor of Ostrava? Or, vice versa, if a knows who is the Mayor of Ostrava and the Mayor of Ostrava is Mr Macura, do these assumptions logically entail that a knows that the Mayor of Ostrava is Mr Macura? Though in case of rational human agents the answers seem to be a no-doubt YES, perhaps a rather surprising answer is in general negative. We have to specify rules for deriving the relation between knowing-that and knowing-wh, and if a software agent is rational but resource bounded, it does not have to have in its ontology the rules necessary to derive the answer. The goal of the paper is the specification of these rules. Hence, when applying these results into the design of a multi-agent system composed of software resource-bounded agents, we have to compute their inferable knowledge, which accounts not only for their explicit knowledge but also for their inferential abilities.

The paper deals with the rules for converting natural language text with motion verbs into TIL-Script, the computational variant of Transparent Intensional Logic (TIL). This function is part of the TILUS tool, which is now being worked on, and which will be used for the needs of appropriate textual information sources retrieval and natural language processing. Our work is currently starting on a module that allows the transformation of a particular subset of natural language texts describing journey descriptions into logical constructions. Hence, in this paper, we focused on the transformation rules for sentences containing motion verbs describing the agent’s movement on the infrastructure. These rules are based on the utilization of Stanford typed dependencies representation and verb valency frames of motion verbs.

This paper analyzes the shortest path problem (SPP) in social networks, based on the investigation and implementation of different methods on a simulated example. The objectives of the paper include identification of the most commonly used methods for finding the shortest path in a social network as a strategic attempt to speed the search of network nodes, focusing on the application of the two most used SPP methods: the Dijkstra and Bellman-Ford algorithms. A comparative analysis is used as an investigation method for performance evaluation of different algorithms, based on their implementation and behavior, tested on a social network example. The research results indicate that the Dijkstra algorithm is faster, and therefore more suitable for searching the shortest connection in social networks.

The article deals with the analysis of reliability and objectivity of information that can be found on the internet and the objectivity and reliability of such information is compared to the system’s behavior. The terms “useful” and “useless” information have been introduced. On the basis of Shannon’s law of connection between information and entropy, as the measure of system’s organization the notion of information chaos is analyzed, it illustrating growth of entropy in such system. The work comprises a graphical interpretation of various events with Lars Onsager’s curves. Described is the parameter which has to discern authentic useful information available for analyzing and obtaining new knowledge from false and biased. A variant of the general scheme of the dynamic information system of the Internet, reflecting the appearance of inaccurate information, is given. The analysis of experts’ evaluation of the internet users’ reaction on appearance of false, biased or unreliable information showed that young users were oriented largely on emotional content, while the scientific society preferred reliability, objectiveness and authenticity of information.

The work is devoted to the problems of efficiency of bitcoins, especially power inputs due to generating of this cryptocurrency. Nowadays the problems of mining power input efficiency seem to have passed from a comparative problem to the problem of existence of this blockchain technology, requiring a different engineering policy or different managerial decisions. The comparative analysis of bitcoins generating power input and the world’s power input has shown that there are almost no trustworthy methods of evaluating input power for blockchain technologies. This problem is solved by application of contemporary methods by setting up big mining companies, located in areas with cheap electric energy and free power balance, possessing their own technological resources and thinking of creating alternative power capacities for mining objectives. At that the state systems start to introduce various limitations for power capacities used for cryptocurrencies including quotas and price privileges. Represented are the results of the analyses of the dynamics of alternation of the main parameters, influencing power consumption at generation of bitcoins on the basis of available literary data and own investigation. Given are the results of calculations regarding bitcoins generation, the costs of consumed electric power per mining of one bitcoin. A more objective index of bitcoin power consumption is suggested, it being correlated with its unit and the unit of heshrate eh showing that N_btc·LgH duplex is constantly growing despite a decrease in general mass of generated bitcoins, while the relative bitcoin energy consumption decreases with time, still such decrease happens slower than the growth of its market value.

The article deals with issues related to the creation of digital mechanisms for managing the priorities of goals in enterprises in the context of inter-goal conflicts. A system-dynamic model of a system for forming and serving a queue of goals has been developed and it has been shown that with an average load of such a system, even at 90%, large delays in the execution of goals are already observed. It is also shown that the introduction of a feedback mechanism through goal priority management can significantly increase the effectiveness of the goal setting system. The concept of goal priority is proposed as a complex parameter consisting of 10 components that form the static, dynamic and purchase parts of the priority. A digital mechanism for the formation and management of enterprise priorities is proposed. The obtained results make it possible to increase the economic security of the enterprise due to better coordination of strategic, operational and tactical goals, as well as to accelerate management processes and increase their transparency.

It is significant to detect, estimate and predict “Human-health situations” and “a spread of transmitted disease” with past and current information of health-related phenomena. Temporal-transition and differential computing realizes semantic interpretations for situation changes in two phenomena with “temporal-length” in “specific situation”. The “temporal-length” in “specific situation” is used to compare two phenomena in multiple contexts in semantics. We present a new Temporal-transition Differential Computing Model for detecting, estimating and predicting “Human-health situations” and “a spread of transmitted disease.” This model defines “temporal-transition data structure” for expressing past and current information of health-related phenomena with temporal-axis, and two processes for Human-Health Semantic Space Creation and Semantic Computing with dimensional control mechanism.

In this paper, we proposed a two-phase project on emotion corpus creation based on multi-knowledge of cognitive semantics, discourse analysis, paralinguistics, and computer science. Data were gathered from Thai lexicon of five main Thai dictionaries and thesaurus, in addition to written and spoken texts of people with depression in Thai and facial expression with speech situation. We found that semantic primes and features of each emotion were needed to serve as a guideline of emotion categorization in Thai context. We introduced the step-by-step methods of the first phase to create Thai emotion corpus entailing both verbal and nonverbal corpora. The way to classify emotion corpus by focusing on the specific text of depression as well as to find the guidelines of labelling facial expression in the situation of specific emotions was explored. Lastly, the step of creating emotion corpus in the second phase was introduced with some suggestions and discussion.

This paper describes about project “Data Sensorium” launched at the Asia AI Institute of Musashino University. Data Sensoriumis a conceptual framework of systems providing physical experience of content stored in database. Spatial immersive display is a key technology of Data Sensorium. This paper introduces prototype implementation of the concept and its application to environmental and architectural dataset.

The “free” business model prevails in mobile apps available through the major channels, hinting at the possibility that users “pay” for the use of the mobile apps by sharing their private data with the developers and platform providers. Several types of personal data and permissions of mobile applications were analyzed. We examined 636 apps in several categories, such as medical, health & fitness, business, finance, and entertainment. The types of personal data being requested by the apps were collected from their privacy policies and the list of permissions was scraped from the platform’s store. We implemented a privacy policy word processing algorithm, the purpose of which was to gain a better insight into the types of data collected. Using the algorithm results, we also performed statistical analyses, based on which we found, expectedly, that free mobile applications collect more data than paid ones. However, there are discrepancies between the permissions we obtained from the privacy policy texts and those stated on the Google Play and Apple App Store websites. More permission requirements emerged from the privacy policy texts than were shown on corresponding app stores, which is a worrying result.