Open Source Intelligence (OSINT), in addition to closed military sources, provides timely information on emerging cyber attack techniques, attacker groups, changes in IT products, policy updates, recent events, and much more. Often, dozens of analysts scour hundreds of sources to gather, categorize, cluster, and prioritize news items, delivering the most pertinent information to decision makers. However, the sheer volume of sources and news items is continually expanding, making manual searches increasingly challenging. Moreover, the format and presentation of this information vary widely, with each blog entry, threat report, discussion forum, and mailing list item appearing differently, further complicating parsing and extracting relevant data. The research projects NEWSROOM and EUCINF, under the European Defence Fund (EDF), focus on leveraging Natural Language Processing (NLP) and Artificial Intelligence (AI) to enhance mission-oriented cyber situational awareness. These EDF initiatives are instrumental in advancing Taranis AI, a tool designed to categorize news items using machine learning algorithms and extract pertinent entities like company names, products, CVEs, and attacker groups. This enables the indexing and labeling of content, facilitating the identification of relationships and grouping of news items related to the same events – a crucial step in crafting cohesive "stories." These stories enable human analysts to swiftly capture the most significant current "hot topics", alleviating them from the task of consolidating or filtering redundant information from various sources. Taranis AI further enhances its capabilities by automatically generating summaries of reports and stories, and implementing a collaborative ranking system, among other features. This paper serves as an introduction to Taranis AI, exploring its NLP advancements and their practical applications. Additionally, it discusses lessons learned from its implementation and outlines future directions for research and development.

This paper presents the Transcribathou.eu tool and Europeana Transcribe, a citizen science initiative which aims at crowdsourcing transcription and enrichment of historical documents made available through the Europeana.eu platform. The public value of this work is internationally recognized through the honorary mention1 awarded within the scope of European Union Prize for Citizen Science. The process for performing a complete digitization of historical documents is described together with AI technologies used for analyzing document content, generating semantic enrichments, clustering and visualizing documents by their topics.

Creating analysis reports on events published in open source news data is a tedious task when done manually. Due to the large-scale nature of news data, analysts, such as government officials, often spend unnecessary resources when trying to research news data on a specific topic. In this paper, we present an interactive system for unsupervised classification of news articles in a dynamic set of hierarchical labels. By providing users with explanations in the form of highlighted words, we enable them to quickly assess the relevance of an article to a particular topic. We also provide aggregated visualisations to detect emerging events and include several quality-of-life enhancements such as a source rating mechanism and report generation.

The Sequential Sentence Classification task within the domain of medical abstracts, termed as SSC, involves the categorization of sentences into pre-defined headings based on their roles in conveying critical information in the abstract.In the SSC task, sentences are sequentially related to each other.For this reason, the role of sentence embeddings is crucial for capturing both the semantic information between words in the sentence and the contextual relationship of sentences within the abstract, which then enhances the SSC system performance.In this paper, we propose a LSTM-based deep learning network with a focus on creating comprehensive sentence representation at the sentence level.To demonstrate the efficacy of the created sentence representation, a system utilizing these sentence embeddings is also developed, which consists of a Convolutional-Recurrent neural network (C-RNN) at the abstract level and a multi-layer perception network (MLP) at the segment level.Our proposed system yields highly competitive results compared to state-ofthe-art systems and further enhances the F1 scores of the baseline by 1.0%, 2.8%, and 2.6% on the benchmark datasets PudMed 200K RCT, PudMed 20K RCT and NICTA-PIBOSO, respectively.This indicates the significant impact of improving sentence representation on boosting model performance.

Current methods for embedding-based query answering over incomplete Knowledge Graphs (KGs) only focus on inductive reasoning, i.e., predicting answers by learning patterns from the data, and lack the complementary ability to do deductive reasoning, which requires the application of domain knowledge to infer further information. To address this shortcoming, we investigate the problem of incorporating ontologies into embedding-based query answering models by defining the task of embedding-based ontology-mediated query answering. We propose various integration strategies into prominent representatives of embedding models that involve (1) different ontology-driven data augmentation techniques and (2) adaptation of the loss function to enforce the ontology axioms. We design novel benchmarks for the considered task based on the LUBM and the NELL KGs and evaluate our methods on them. The achieved improvements in the setting that requires both inductive and deductive reasoning are from 20% to 55% in HITS@3.

Among many solutions for extracting useful answers from incomplete data, ontology-mediated queries (OMQs) use domain knowledge to infer missing facts. We propose an extension of OMQs that allows us to make certain assumptions—for example, about parts of the data that may be unavailable at query time, or costly to query—and retrieve conditional answers, that is, tuples that become certain query answers when the assumptions hold. We show that querying in this powerful formalism often has no higher worst-case complexity than in plain OMQs, and that these queries are first-order rewritable for DL-Liteℛ. Rewritability is preserved even if we allow some use of closed predicates to combine the (partial) closed- and open-world assumptions. This is remarkable, as closed predicates are a very useful extension of OMQs, but they usually make query answering intractable in data complexity, even in very restricted settings.

SHACL (SHape Constraint Language) is a W3C recommendation for validating graph-based data against a set of constraints (called shapes). Importantly, SHACL allows to define recursive shapes, i.e. a shape may refer to itself, directly of indirectly. The recommendation left open the semantics of recursive shapes, but proposals have emerged recently to extend the official semantics to support recursion. These proposals are based on the principle of possibility (or non-contradiction): a graph is considered valid against a schema if one can assign shapes to nodes in such a way that all constraints are satisfied. This semantics is not constructive, as it does not provide guidelines about how to obtain such an assignment, and it may lead to unfounded assignments, where the only reason to assign a shape to a node is that it allows validating the graph.

We advocate the use of ontologies for relaxing and restraining queries, so that they retrieve either more or less answers, enabling the exploration of a given dataset. We propose a set of rewriting rules to relax and restrain conjunctive queries (CQs) over datasets mediated by an ontology written in a dialect of DL-Lite with complex role inclusions (CRIs). The addition of CRI enables the representation of knowledge about data involving ordered hierarchies of categories, in the style of multi-dimensional data models. Although CRIs in general destroy the first-order rewritability of CQs, we identify settings in which CQs remain rewritable.