Cultural heritage data, particularly from colonial contexts, frequently presents an incomplete and biased view, reflecting historical institutional priorities more than contemporary knowledge requirements. Consequently, knowledge graphs derived from these records often contain incomplete, fragmented, and skewed data, including absent attributes or values, missing semantic links, and under-represented perspectives. This work addresses the challenge of knowledge discovery under such limitations, presenting a real-world case study on the provenance research of colonial cultural heritage. We present a task-aware design method for building a tool to facilitate this process. The design approach of this application is rooted in a Knowledge Discovery in Database (KDD) framework and is particularly novel due to its formalisation and operationalisation of three distinct types of semantic association: explicit, abstract, and implicit. These semantic associations, grounded in domain interpretation, are crucial for bridging data gaps where user information needs cannot be directly met by existing data. We further demonstrate how these associations can be effectively communicated through user interface components, enabling users to infer new knowledge. We evaluated the resultant application through a user study among domain experts to assess its efficacy. The evaluation confirms the effectiveness of the tool in enabling new knowledge discovery and reveals opportunities to improve the representation of the underlying data, as users could successfully infer insights even when information was missing or poorly captured in the original data sets.

Hybrid Intelligence (HI) is a rapidly growing field aiming at creating collaborative systems where humans and intelligent machines cooperate in mixed teams towards shared goals. A clear characterization of the tasks and knowledge exchanged by the agents in HI applications is still missing, hampering both standardization and reuse when designing new HI systems. Knowledge Engineering (KE) methods have been used to solve such issue through the formalization of tasks and roles in knowledge-intensive processes. We investigate whether KE methods can be applied to HI scenarios, and specifically whether common, reusable elements such as knowledge roles, tasks and subtasks can be identified in contexts where symbolic, subsymbolic and human-in-the-loop components are involved. We first adapt the well-known CommonKADS methodology to HI, and then use it to analyze several HI projects and identify common tasks. The results are (i) a high-level ontology of HI knowledge roles, (ii) a set of novel, HI-specific tasks and (iii) an open repository to store scenarios1 – allowing reuse, validation and design of existing and new HI applications.

Graph neural networks and other machine learning models offer a promising direction for machine learning on relational and multimodal data. Until now, however, progress in this area is difficult to gauge. This is primarily due to a limited number of datasets with (a) a high enough number of labeled nodes in the test set for precise measurement of performance, and (b) a rich enough variety of multimodal information to learn from. We introduce a set of new benchmark tasks for node classification on RDF-encoded knowledge graphs. We focus primarily on node classification, since this setting cannot be solved purely by node embedding models. For each dataset, we provide test and validation sets of at least 1000 instances, with some over 10000. Each task can be performed in a purely relational manner, or with multimodal information. All datasets are packaged in a CSV format that is easily consumable in any machine learning environment, together with the original source data in RDF and pre-processing code for full provenance. We provide code for loading the data into numpy and pytorch . We compute performance for several baseline models.

Digital sources are more prevalent than ever but effectively using them can be challenging. One core challenge is that digitized sources are often distributed, thus forcing researchers to spend time collecting, interpreting, and aligning different sources. A knowledge graph can accelerate research by providing a single connected source of truth that humans and machines can query. During two design-test cycles, we convert four data sets from the historical maritime domain into a knowledge graph. The focus during these cycles is on creating a sustainable and usable approach that can be adopted in other linked data conversion efforts. Furthermore, our knowledge graph is available for maritime historians and other interested users to investigate the daily business of the Dutch East India Company through a unified portal.

The cultural heritage sector has embraced social tagging as a way to increase both access to online content and to engage users with their digital collections. In this article, we build on two current lines of research. (a) We use Waisda?, an existing labeling game, to add time‐based annotations to content. (b) In this context, we investigate the role of experts in human‐based computation (nichesourcing). We report on a small‐scale experiment in which we applied Waisda? to content from film archives. We study the differences in the type of time‐based tags between experts and novices for film clips in a crowdsourcing setting. The findings show high similarity in the number and type of tags (mostly factual). In the less frequent tags, however, experts used more domain‐specific terms. We conclude that competitive games are not suited to elicit real expert‐level descriptions. We also confirm that providing guidelines, based on conceptual frameworks that are more suited to moving images in a time‐based fashion, could result in increasing the quality of the tags, thus allowing for creating more tag‐based innovative services for online audiovisual heritage.

Digital history is a branch of digital humanities concerned using ICT to improve study of history. Linked Data provides a way of effective enriched digital access to scientific texts about history (historiographies). In this paper, we present a method for connecting a historiographical text to the Linked Data cloud. We present the method and tools that we use in each of the method's steps. We focus on one extensive case study: the enriched access of an important work of Dutch World War II historiography "Het Koninkrijk der Nederlanden in de Tweede Wereldoorlog". We describe the digitization and present two sources of structured knowledge that link to individual text sources, retrievable on the Web of Data. The first is the manually constructed and highly curated "Back of the Book Index". The second is a list of extracted Named Entities. We compare both structured sources as stepping stones to the Web of Data and present a number of use cases relevant for both historical researchers as well as for the general public.

Abstract In this work we present an automatic method for the extraction of time periods related to ontological concepts from the Web. The method consists of two parts: an Information Extraction phase and a Semantic Representation phase. In the Information Extraction phase, temporal information about events that are associated with the target instance are extracted from Web documents. The resulting distribution is normalized and a model is fit to it. This distribution is then converted into a Semantic Representation in the second phase. We present the method and describe experiments where time periods for four different types of concepts are extracted and converted to a time representation vocabulary, based on the TIMEX2 annotation standard.