Information Integration and Web Intelligence

Table of Contents

1. Knowledge, Reasoning, and Human Interaction

   1. Frontmatter

   2. CACIKI - Compositionally Analyzed Collection of Illustrated Kanji Information

      Werner Winiwarter

      Abstract

      In this paper, we present a collection of 3,503 kanji with complete component hierarchies and representative images. The information is displayed as kanji cards to offer a versatile format that can be easily integrated into Web-based language learning applications. We will make the data publicly available in JSON format together with a simple Web application using templates to access and inspect the kanji cards. As two recent extensions we also introduce an iconic representation of the compositional layout and an evaluation environment focusing on handwritten kanji input by the learner.

   3. Integration of Knowledge Bases and External Sources Incorporating Uncertainty in Entity Linking

      Yuuki Ohmori, Hiroyuki Kitagawa, Toshiyuki Amagasa, Akiyoshi Matono

      Abstract

      Knowledge bases (KBs) are increasingly used in diverse knowledge-processing tasks. Widely used RDF-style KBs represent knowledge as subject – predicate – object triples, whereas many external sources are described in non-RDF formats. Therefore, applications often need to integrate KBs with such external sources. In our previous work, we proposed an integrated query environment named Knowledge Mediator (KM), in which external sources are accessed via SPARQL magic properties, letting users query them as if they were parts of the KB. KM performs entity linking (EL) to find corresponding KB entities for external objects. However, EL may yield multiple plausible entities, and the results may involve uncertainty. In this study, we propose a model that represents data integration while allowing for the expression of uncertainty in query results caused by EL. Furthermore, we propose an efficient query processing method that retrieves only those results whose likelihoods exceed a user-specified threshold.

   4. Data Specification Vocabulary (DSV): Representation of Application Profiles of Semantic Data Specifications

      Jakub Klímek, Štěpán Stenchlák, Petr Škoda

      Abstract

      Semantic Data Specifications (SDSs) define agreements for data exchange using semantic technologies. The authors of SDSs pick existing terms for reuse in their specific contexts, creating Application Profiles (APs). However, when the terms are reused directly, that is, without creating subclasses and subproperties, we run into a lack of machine-readable representation, especially when the AP editors still adjust labels, definitions, domains, and ranges. This leads to confusion, inefficiencies, and inconsistencies in the management and use of SDSs and APs. Based on our experience profiling SDSs in the data catalog domain, we present the Data Specification Vocabulary (DSV), our language for AP definition, supporting the specification of term reuse and refinement in SDSs. For improved machine readability of SDS metadata, DSV reuses the Profiles Vocabulary. We demonstrate DSV on DCAT, DCAT-AP and DSV itself, and we show that DSV can be used in software for SDS and AP management.

   5. Investigating the Impact of Defocus on Font Visibility in Transparent Displays

      Masakatsu Ezumi, Ayumi Ohnishi, Tsutomu Terada, Masahiko Tsukamoto

      Abstract

      A transparent display allows the presentation of images and information while simultaneously transmitting background light. Unlike conventional displays, it enables users to obtain information while visually confirming their surroundings. For instance, a transparent display equipped with a speech transcription system allows users to read transcribed conversation content while observing the facial expressions and gestures of their conversation partners, thereby enhancing comprehension. However, because users frequently shift their visual focus between the background and the overlaid text, a time lag may occur in acquiring textual information, potentially hindering smooth communication. Therefore, it is essential to clarify which methods of presenting information remain easily recognizable even when out of focus. This study focuses on fonts, the fundamental components of textual information, to investigate which fonts are more easily recognized under defocused conditions. An experiment was conducted to evaluate the visibility of fonts on a transparent display at various focal distances, using two character types with differing visual complexity (Kanji and the Latin alphabet). The results revealed that the visibility of text on a transparent display is influenced by factors such as font type, degree of defocus, and character type. Notably, differences in font visibility became particularly pronounced under moderate defocus.

   6. On the Relationship Between Crude, Adjusted, Confounder and Latent Coefficients in Linear Regression

      Sijo Arakkal Peious, Ahto Buldas, Dirk Draheim

      Abstract

      Linear regression is one of the widest used data analytics techniques in support of human decision-making. In this paper we discuss some theoretical foundations of confounder detection used in our web-based decision making platform GrandReport. In the context of linear regression, controlling confounding effects means to add further influencing, potentially confounding factors to the analysis. This paper exactly explains confounding effects in terms of the various involved coefficients by utilizing a conjecture on the noise-independent relationship between crude, adjusted, confounder and latent coefficients. We discuss, in how far our findings can improve the explainability of linear regression models as well as the maturity of their application in various contexts.

   7. Privacy Patterns and Objectives for Legally Compliant Software Based on the Indonesia’s PDP Law

      Guntur Budi Herwanto, Arif Nurwidyantoro, Annisa Maulida Ningtyas, Muhammad Oriza Nurfajri, Gerald Quirchmayr, A Min Tjoa

      Abstract

      Organizations worldwide face significant challenges in translating privacy regulations into implementable technical requirements, creating a critical gap between legal privacy compliance and system development. This paper adapts KORA (Konkretisierung Rechtlicher Anforderungen - Concretization of Legal Requirements) methodology by incorporating established privacy patterns to systematically translate regulatory privacy requirements into applicable solutions. Applying this methodology, we examine Indonesia’s Personal Data Protection Law (UU-PDP) to propose technical solutions for privacy compliance. Our three-phase methodology systematically identifies regulatory requirements, maps them to established privacy objectives, including transparency, manageability, and intervenability, and connects them to implementable privacy patterns. Through rigorous analysis of the 76 articles in the UU-PDP, we extracted 183 distinct legal criteria in 59 articles, revealing that transparency, manageability, and intervenability emerge as predominant regulatory priorities. Our analysis identifies 53 applicable privacy patterns, with the implementation of just 10 key patterns addressing half of the regulatory requirements, providing an efficient pathway toward compliance for resource-constrained organizations. The research contributes a privacy-oriented regulatory engineering framework and empirical evidence that structured approaches can achieve substantial compliance coverage through targeted technical implementations.

   8. RAG-Driven Financial QA: Preserving Privacy and Enhancing Performance with Synthetic Data

      Niorn Suchonwanich, Siranee Nuchitprasitchai, Kanchana Viriyapant, Sucha Smanchat

      Abstract

      While AI-driven financial advisory has become increasingly essential for individuals to navigate volatile markets that offer complex financial products, limited data and privacy concerns pose challenges in its development. This research explores the potential of combining Retrieval-Augmented Generation (RAG) with synthetic data, which imitates real-world data without compromising privacy, in enhancing financial question-answering systems. We propose a framework to generate and incorporate synthetic data into the RAG model with strategies to enhance retrieval processes. The proposed framework is compared with a baseline model without synthetic data to evaluate our approach using the Retrieval-Augmented Generation Assessment (RAGAS). The result shows that the integration of synthetic data can improve recall, precision, and faithfulness of the generated responses. However, relevancy can degrade due to the broader scope of retrieved data. The outcome demonstrates that synthetic data can enhance the accessibility and accuracy of financial data while safeguarding privacy in financial question-answering systems.

   9. ML-Assisted Semi-Automated Analysis of Audio/Video Meeting Recordings

      Farhan Ali Khoso, Gabriele Kotsis

      Abstract

      With the growing volume of audio and video content generated during both virtual and in-person meetings, there is an increasing need for efficient analysis tools to support decision-making. While recent research has addressed individual tasks such as transcription, speaker identification, and summarization, these components are often developed and applied independently. This work introduces a unified, machine learning-assisted, semi-automated pipeline that integrates these tasks into a cohesive system. The proposed method enables real-time transcription, speaker diarization, and summarization, offering a deeper understanding of meeting dynamics. Unlike traditional tools that lack adaptability and operate in isolation, our approach incorporates a Human-in-the-Loop (HITL) interface for user validation and refinement, enhancing both accuracy and flexibility. By leveraging state-of-the-art speech recognition, speaker embedding models, and topic modeling techniques, our system provides actionable insights from raw meeting recordings with minimal manual intervention. This integrated solution marks a significant advancement in meeting analysis by effectively combining automation with human oversight.

2. Emerging Technologies and Applied Innovation

   1. Frontmatter

   2. From Connectivity to Value: Mapping Slovak IoT Business Archetypes Against EU Adoption Benchmarks

      Matúš Kovár, Rastislav Kulhánek

      Abstract

      Internet of Things technologies offer the interconnection of various sensors through telecommunications networks, which facilitate their integration into IoT applications. This collaboration of diverse technologies provides an unprecedented opportunity to monitor processes with exceptional precision in human history. Currently, a wide spectrum of end devices contributes to the immense potential of these technologies across various industries. Tracking events and processes offers opportunities for better optimization, both in corporate settings and private life.

      For business, IoT can reveal inefficiencies in established processes, leading to improved performance. For instance, monitoring freezer temperatures and tracking door neglect can reduce cooling costs. On a personal level, IoT can enhance home security or enable remote control of smart devices.

      In our research, we focused on analysing the utilization of IoT technologies in companies within Slovakia as EU member state. We explored IoT network architecture, communication protocols, and archetypes of companies leveraging or offering IoT solutions. Our study involved collecting primary data through unstructured interviews with respondents from three companies operating in the telecommunications and IoT Technology industry. Secondary data were compiled from information gathered online portals (Statista, Eurostat) regarding the use of IoT technologies in various sectors and EU countries.

      The findings of case study are that organizational and business structures defined by theoretical models were found to be used in businesses and have direct impact on the number and services structure of IoT services offered by enterprises. Furthermore the local Slovak market adoption of an IoT services in comparison to EU market is limited and requires regionally balanced disbursement of Digital Europe and RRF funds.

   3. Integrating Digital Product Passports in E-Commerce: An Architectural Framework for Sustainability and AI-Driven Value

      Martin Tamm, Dirk Draheim, Tanel Tammet, Ingrid Pappel

      Abstract

      The global economy's transition towards a Circular Economy (CE) is being accelerated by regulatory instruments like the European Union's Digital Product Passport (DPP), mandated under the Ecodesign for Sustainable Products Regulation (ESPR). While the DPP aims to enhance transparency and enable circular practices, its integration presents significant architectural and operational challenges for businesses, particularly within e-commerce. This paper addresses these challenges by proposing a conceptual architectural framework for a scalable, secure, and federated DPP system. Developed through a qualitative synthesis of regulatory analysis, standardization efforts, and expert workshop feedback, the framework outlines the necessary components for both the EU's registry-centric ecosystem and the corresponding enterprise-level implementation. A key finding is that traditional Product Information Management (PIM) systems are insufficient, requiring a more comprehensive, API-driven, and modular architecture. Furthermore, the paper concludes that AI is not merely an efficiency tool but a critical strategic capability. AI is essential for managing data quality at scale, enhancing security, and transforming the DPP from a compliance burden into a value-creating asset through predictive analytics and personalized consumer engagement. The research posits that successfully operationalizing the DPP marks a paradigm shift towards a product-intelligence economy, demanding a holistic architectural approach to realize its full potential.

   4. The Impact of Incremental eService Changes on Customer Experience: A Conceptual Review

      Bela Philip Kramer, Gabriele Kotsis, Christine Strauss, Andreas Mladenow

      Abstract

      Businesses are depending more and more on small adjustments to their eServices in the face of rapidly accelerated digital transformation in order to stay competitive and satisfy changing client demands. This study looks at how these small-scale developments affect customer experience (CX), pointing out both the advantages and disadvantages. Based on a thorough examination of the literature, the study investigates how usability, personalization, trust, and cultural variations influence how customers react to changes in digital services. The results highlight the importance of user-centered design, open communication, and agile innovation processes by indicating that even minor adjustments can have a big impact on CX. In addition to providing useful implications for matching innovation objectives with customer expectations, the paper adds to the current conversation on digital service management.

   5. Exploring the Efficacy of Learning Analytics Dashboards for Metacognitive Activity Support in Self-learning

      Koki Saitoh, Chiemi Watanabe, Kouhei Kikuchi

      Abstract

      This study examined the effect of a Learning Analytics Dashboard (LAD) on metacognitive activities in self-directed learning environments. We developed a LAD system that visualizes detailed video clickstream analysis and quiz results to support learners’ self-reflection. Through qualitative analysis using questionnaires and interviews, we conducted a one-month experiment with six first-year university students learning linear algebra, measuring metacognitive activities with the Metacognitive Awareness Inventory (MAI).

   6. UPPSALA – Universal Pictographic Pictorial Sentence Annotation for Language Acquisition

      Werner Winiwarter

      Abstract

      In this paper, we introduce a new sentence annotation, which relies on visual elements to convey the semantic interpretation. This makes it perfectly suited for the use in language learning environments and other multilingual applications. By building upon the rich experience in the related field of augmentative and alternative communication (AAC), we organize the annotation as a grid of tiles with pictograms for concepts and emoji for roles. As first use case we developed a framework for Japanese based on a flexible, distributed architecture using augmented browsing and several dedicated high-performance servers.

   7. Experiences Before On-the-Job Training of University Graduates Employed in the IT Sector in the Slovak Republic

      Vincent Karovič, Marek Hlásny

      Abstract

      In our study we present the basic theoretical background of On-the-Job training of university graduates employed in the IT sector and the results of research conducted in this specific segment of the Slovak labour market, focusing on the respondents’ experiences prior to On-the-Job training.