Geographical works from the Middle Ages and Renaissance offer crucial insights into the cultural and intellectual landscapes of their time. However, digital scholarship in this domain remains fragmented, with key historical sources scattered across various physical and digital repositories. The Index Medii Aevi Geographiae Operum (IMAGO), an Italian national research project conducted from 2020 to 2024, addresses this gap by building a semantically enriched, interoperable knowledge graph focused on Latin geographical literature from the 6th to the 15th centuries. By combining expertise in medieval studies, philology, and digital humanities, IMAGO employs Semantic Web technologies and a dedicated ontology extending CIDOC CRM and LRMoo. The project facilitates data integration and reuse by applying Linked Open Data (LOD) principles, thereby enhancing the discoverability and interoperability of cultural heritage data. Beyond the release of the IMAGO knowledge graph, this work contributes a methodological pipeline for semantic modelling, annotation, integration, and publication of data related to medieval and Renaissance geographical works using established Knowledge Representation and Semantic Web standards. The approach is evaluated through a set of scholarly queries. These queries showcase the IMAGO infrastructure’s potential for data retrieval and deeper scholarly analysis. Finally, a user-friendly web application further enables access to the knowledge graph via interactive maps, dynamic tables, and exportable formats.
Consistency, that is the degree to which a system, process, or its results produce similar outcomes when repeated under identical or different conditions, is a critical concern in knowledge engineering (KE). This is particularly the case given the increasing reliance on large language models (LLMs) in various tasks. This paper introduces CoLLM, a framework designed to assess whether a system or process produces consistent results in LLM-based KE tasks through three tests: (i) the LLM Repeatability Test , which evaluates the level of stochasticity or non-determinism of LLMs in existing studies; (ii) the LLM Update Impact Test , which examines the effect that LLM updates may have on results; and (iii) the LLM Replacement Test , which explores the effect of using alternative LLMs to perform the same study. Through 59 different experiments taken from five separate, recent studies, and leveraging various LLMs and datasets, we investigate the consistency of the results to empirically validate the reliability of the original findings for each study. Our investigation shows that in the majority of cases (81.4%), a consistent behavior with respect to the original studies can be observed, despite some variability across the individual outputs. Additionally, in some cases, changing the choice of LLM can result in a consistent improvement across different metrics. These results demonstrate the viability of the proposed framework in general to assess the consistency of LLM-based KE tasks.
Classical Chinese poetry, a classical form composed in Literary Chinese, originated in China and spread across East Asia, including Japan, Korea, and Vietnam. It has played a key role in elite education, cultural diplomacy, and political communication. Despite its cultural significance, research on its semantic modeling remains limited, and digital representations often face issues like fragmentation and structural inconsistencies. This paper proposes a decentralized semantic infrastructure for Classical Chinese poetry as a multilingual resource for cultural heritage. The system is organized into three layers: (i) multilingual SKOS vocabularies to represent poetic concepts; (ii) OWL ontologies that reuse BIBFRAME for bibliographic structure and FOAF for authorship representation; and (iii) a distributed RDF resource layer supporting SPARQL 1.1 federated queries across poetic corpora. The infrastructure implements TEI standards for structuring poetic materials, including metadata and digitized text, while reserving interfaces for potential IIIF integration. Future work will focus on expanding multilingual vocabularies and integrating with cultural heritage platforms.
This article investigates the methodological foundations and theoretical assumptions behind the construction of computational ontologies for modeling narrative and fiction, with a focus on literary characters. We survey and critically assess a set of existing domain-specific ontologies for fictional narrative, evaluating their modeling strategies, taking into consideration their philosophical and knowledge representation criteria. Drawing from ontology engineering principles and foundational frameworks such as Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) and Basic Formal Ontology (BFO), we propose a two-class ontology mapping methodology (harmonisation and alignment) to evaluate and foster semantic interoperability across the considered models. An experimental ontology pattern for fictional characters is then introduced and aligned with both DOLCE and BFO via International Committee for Documentation Conceptual Reference Model (CIDOC-CRM), revealing the ontological commitments and modelling trade-offs required to formalise the nuanced nature of fictional entities. This study offers a preliminary attempt to explore how foundational ontologies might support conceptual clarity, while also highlighting the epistemological challenges involved in representing complex, non-referential cultural artefacts. Ultimately, this work aims to highlight the relevance of ontologies as a shared infrastructure for computational literary studies, supporting interdisciplinary collaboration, fostering Open Science and encouraging more structured, transparent, and conceptually grounded approaches to the representation and analysis of cultural phenomena.
To achieve interoperability for effective data exchange on the web, we need a contract. Depending on the field, we may refer to technical data schemas, web vocabularies, or, more generally, to data specifications (DSes). The development and management of these DSes can become difficult in complex domains with multiple stakeholders and related DSes involved. In this article, we present Dataspecer, an open-source, modular web application for the development of semantic data specifications (SDSes), DSes that target the semantic and technical layers of data exchange. Dataspecer allows users to design web vocabularies and their application profiles, maintaining relations between reused concepts and their original SDSes. Furthermore, Dataspecer assists users in the creation of technical artifacts such as schemas for JSON or XML, while maintaining consistency of the artifacts with the application profiles. We motivate the need for SDSes and derive requirements for such a tool. In case studies based on the ecosystem of Data Catalog Vocabulary-based specifications, we demonstrate that SDSes created in Dataspecer meet these requirements and are of higher quality. We show SDSes that were created directly in Dataspecer, and in the evaluation section, we argue that using our tool is more efficient than creating them manually, even for smaller domains.
