Vladimir Alexiev

The digitalisation of building permit necessitates streamlined approaches for handling diverse and complex datasets. To meet this need, profiles of varied data sources are critical in distilling relevant information, addressing common concerns, and simplifying the use of interoperable data. Defining simplified profiles allows avoiding over-reliance on the entirety of available data, instead using constrained, modular representations tailored to specific regulatory and procedural needs. Drawing from principles of interoperability and practical applications, this chapter highlights existing and emerging practices in defining and managing such data profiles. It discusses how profiling standards like CityGML, IFC, XPlanung, and INSPIRE can improve practical interoperability to suit specific regulatory use cases. Machine-readable profiles can be used to validate whether available data meets processing requirements and ensure the reliability of digital workflows by automating compliance checks and linking disparate data sources. By leveraging profiling methodologies, digital building permit processes can achieve better data reusability, trusted automatic compliance checks, and integration of diverse datasets into cohesive workflows. This chapter reviews practices in the area and discusses how Linked Data approaches can support scalable solutions with modular, machine-readable profiles that can harmonise data while reducing complexity and enhancing traceability.

Artificial Intelligence (AI) is a transformative force driving innovation, yet tracking AI-related advancements remains challenging due to the rapid pace of development and unstructured data from platforms like GitHub. This paper proposes an AI-driven approach to innovation detection, leveraging GitHub as a data source to systematically identify and link AI projects to organizations. Key contributions include a domain-specific taxonomy comprising 7,490 AI topics, a modular pipeline for semantic annotation and entity linking, and a trend detection framework based on Singular Spectrum Analysis (SSA). A knowledge graph is constructed to represent relationships among AI topics, projects, and companies, thereby enabling structured innovation tracking. The approach addresses challenges such as data sparsity and noise, demonstrating strengths in semantic annotation and topic categorization. Results highlight the potential for accurately detecting AI innovations and linking them to organizational entities, offering valuable insights for researchers, companies, and policymakers. This work contributes a scalable, automated approach for AI innovation tracking, with future directions focusing on refining entity linking and expanding the knowledge graph to capture emerging trends.

Company data, ranging from basic company information such as company name(s) and incorporation date to complex balance sheets and personal data about directors and shareholders, are the foundation that many data value chains depend upon in various sectors (e.g., business information, marketing and sales, etc.). Company data becomes a valuable asset when data is collected and integrated from a variety of sources, both authoritative (e.g., national business registers) and non-authoritative (e.g., company websites). Company data integration is however a difficult task primarily due to the heterogeneity and complexity of company data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. In this article, we introduce the euBusinessGraph ontology as a lightweight mechanism for harmonising company data for the purpose of aggregating, linking, provisioning and analysing basic company data. The article provides an overview of the related work, ontology scope, ontology development process, explanations of core concepts and relationships, and the implementation of the ontology. Furthermore, we present scenarios where the ontology was used, among others, for publishing company data (business knowledge graph) and for comparing data from various company data providers. The euBusinessGraph ontology serves as an asset not only for enabling various tasks related to company data but also on which various extensions can be built upon.

Analysts and journalists face the problem of having to deal with very large, heterogeneous, and multilingual data volumes that need to be analyzed, understood, and aggregated. Automated and simplified editorial and authoring process could significantly reduce time, labor, and costs. Therefore, there is a need for unified access to multilingual and multicultural news story material, beyond the level of a nation, ensuring context-aware, spatiotemporal, and semantic interpretation, correlating also and summarizing the interpreted material into a coherent gist. In this paper, we present a platform integrating multimodal analytics techniques, which are able to support journalists in handling large streams of real-time and diverse information. Specifically, the platform automatically crawls and indexes multilingual and multimedia information from heterogeneous resources. Textual information is automatically summarized and can be translated (on demand) into the language of the journalist. High-level information is extracted from both textual and multimedia content for fast inspection using concept clouds. The textual and multimedia content is semantically integrated and indexed using a common representation, to be accessible through a web-based search engine. The evaluation of the proposed platform was performed by several groups of journalists revealing satisfaction from the user side.

Digital methods and tools for the humanities also change historical research into the Holocaust. The European funded Holocaust project EHRI has developed various digital tools and methods that facilitate Holocaust research. This paper will describe a number of them and discuss how they affect scholarship into the annihilation of European Jews.

The Galleries, Libraries, Archives and Museums (GLAM) sector deals with complex and varied data. Integrating that data, especially across institutions, has always been a challenge. Semantic data integration is the best approach to deal with such challenges. Linked Open Data (LOD) enable large-scale Digital Humanities (DH) research, collaboration and aggregation, allowing DH researchers to make connections between (and make sense of) the multitude of digitized Cultural Heritage (CH) available on the web. An upsurge of interest in semtech and LOD has swept the CH and DH communities. An active Linked Open Data for Libraries, Archives and Museums (LODLAM) community exists, CH data is published as LOD, and international collaborations have emerged. The value of LOD is especially high in the GLAM sector, since culture by its very nature is cross-border and interlinked. We present interesting LODLAM projects, datasets, and ontologies, as well as Ontotext's experience in this domain. An extended version of this paper is available. It has 77 pages, 67 figures, detailed info about CH content and XML standards, Wikidata and global authority control.

Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting guideline covers elements of description, identification, attribution, versioning, provenance, and content summarization. This guideline reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets.