National intelligence agencies have the complex task of investigating threats to the national security within strict legal and policy frameworks. Reconstructing the context of investigative decisions for post-analysis and compliance checks is prone to error and labour-intensive. To address this, we propose to capture decision-making processes and their rationale directly using an OWL-based ontology. This approach overcomes the limitations of traditional data management and existing decision ontologies in handling the intricate data dependencies within threat intelligence (TI) decision-making. The result is the Threat Intelligence Decision Ontology (TIDO), which structures analysts’ decision-making while incrementally capturing a decision trace for post-analysis as investigations unfold. The ontology was developed under the complex constraints of safeguarding threat intelligence practices and case information, and validated through competency questions from intelligence experts from the Dutch Defence Intelligence and Security Service (DISS). TIDO offers a novel solution for capturing and understanding decision processes within the sensitive domain of threat intelligence, and evidence-based decision-making in general.

In recent years, social media platforms such as Twitter have allowed people to voice their opinions by engaging in online discussions. The availability of such discussions has garnered interest amongst researchers in analyzing the dynamics on critical topics, such as inequality. Most of the current strategies are, however, limited with respect to conveying the fine-grained opinions of users, focusing on tasks such as sentiment analysis or topic modeling that extract coarse categorizations. In this work, we address this challenge by integrating a Twitter corpus with the output of finer-grained semantic parsing for the analysis of social media discourse. To do so, we first introduce the OBservatory Integrated Ontology (OBIO) that integrates social media metadata with various types of linguistic knowledge. We then present the Observatory Knowledge Graph (OKG), a knowledge graph in terms of the ontology, populated with tweets on inequality. We lastly provide use cases showing how the knowledge graph can be used as the backbone of a social media observatory, to facilitate a deeper understanding of social media discourse.

Large collections of historical biodiversity expeditions are housed in natural history museums throughout the world. Potentially they can serve as rich sources of data for cultural historical and biodiversity research. However, they exist as only partially catalogued specimen repositories and images of unstructured, non-standardised, hand-written text and drawings. Although many archival collections have been digitised, disclosing their content is challenging. They refer to historical place names and outdated taxonomic classifications and are written in multiple languages. Efforts to transcribe the hand-written text can make the content accessible, but semantically describing and interlinking the content would further facilitate research. We propose a semantic model that serves to structure the named entities in natural history archival collections. In addition, we present an approach for the semantic annotation of these collections whilst documenting their provenance. This approach serves as an initial step for an adaptive learning approach for semi-automated extraction of named entities from natural history archival collections. The applicability of the semantic model and the annotation approach is demonstrated using image scans from a collection of 8, 000 field book pages gathered by the Committee for Natural History of the Netherlands Indies between 1820 and 1850, and evaluated together with domain experts from the field of natural and cultural history.