In exploratory search scenarios, users frequently turn to Knowledge Graphs (KGs) as rich sources of structured knowledge. However, user queries are often imprecise, incomplete, or ambiguous, and KGs themselves are inherently incomplete. Moreover, when KGs are constructed from crowd-sourced content or by integrating heterogeneous data sources of varying quality, the need for mechanisms to assess information reliability becomes critical. This work introduces Context-Augmented Knowledge Graphs (CoaKG), an enriched KG model designed to support context-aware exploration for decision-making and task learning. CoaKG incorporates both explicit contextual mappings and inference rules to derive implicit context, enabling more nuanced query answering. We also present the CoaKG Query Engine, a query processing framework to retrieve contextually enriched answers tailored to users' goals and context.
Investigating narratives in specific text-as-data applications has been extensively implemented using hybrid approaches, combining several NLP solutions and statistical techniques. While this direction provides a substantial understanding of key semantic patterns, it remains a challenging task not applicable to all languages. Narratives, especially in social sciences, are often regarded as the study of causality between specific terms, to uncover embedded directed or indirected links for explanation and further hypotheses testing. This paper establishes CausaMap, as a semi-supervised method to unfold causality between predefined terms of interests. It is built on dependency parsers and the use of tree-based structural equation models to quantify direct and indirect causal links. Thus, testing terms' causality in a directed network of parsed, lemmatized words is equivalent to uncovering informative inner structures with preserved semantic properties, without external information. Applied to two different corpora in English and German languages, CausaMap yielded mixtures of directed acyclic graphs and bidirectional edges, forming a narrative picture centered around monetary objectives and macroeconomic goals reported in the Federal Reserve governors' speeches (1996-2024). The German political parties' manifestos (1990-2021) demonstrated a dominance of policy-oriented terms when studying economic and employment debates, where the relatively high number of uncovered connecting nodes explains the causality buildup of pre-defined terms as well as languagespecific morphologies.
Temporal knowledge graphs (TKGs) have emerged as a critical component in modern artificial intelligence systems, enabling machines to reason over dynamic information. However, existing methods for TKG reasoning use massive amounts of information and computational resources, failing to capture minimal essential temporal knowledge with their dynamics while eliminating irrelevant or noisy information for a more precise reasoning process. To this end, we introduce the Forest Fire Contrastive Approach (FFCA), a contrastive learning architecture based on forest fire sampling that presents a preferential attachment mechanism for the extrapolation of TKG. This allows high-degree nodes to attract new connections, improving the pipeline's predictive capability while keeping it compact, leading to an efficient learning and inference process. This approach introduces a degree-biased burn probability that gathers a minimal but highly correlated subgraph relevant to the query as the global view. Simultaneously, a sufficient number of the most recent snapshots were gathered as the local view, preserving task-relevant graph information and removing noise while reducing the computational complexity and memory requirements, improving the sustainability of TKG reasoning models. Experiments on three publicly available benchmark datasets widely used for TKG extrapolation tasks demonstrate that the proposed approach achieves competitive predictive performance to current state-of-the-art methods while demonstrating substantial improvements in memory efficiency, a critical consideration for scalability to large-scale temporal knowledge graph datasets.
The identification of anomalies and outliers through machine-learning models constitutes a critical and extensively studied domain. However, lack of training data constitutes a significant concern in many application domains. Considering the realm of network intrusion detection - detecting previously unseen attacks, and/or attacks on devices for which training data has not been collected - is essential for implementing effective security measures. To address this issue, we propose an innovative transfer-learning approach. Our method generates new labels for source datasets by leveraging insights from the target dataset, thereby facilitating the accurate identification of normal data within the source datasets. Subsequently, we train an unsupervised model using these normal data to infer unseen anomalies. Furthermore, we employed state-of-the-art explainable artificial intelligence techniques to extract significant features from source datasets, thereby enhancing our model's performance. Our comprehensive evaluations clearly demonstrate the efficacy of our method across both generic (non-IoT) and IoT datasets, consistently surpassing existing transfer learning approaches. For example, our method achieved a remarkable accuracy of 0.99 in detecting anomalous traffic from UDP flooding attacks, whereas the well-known transfer learning model, SSkNNO, achieved an accuracy of only 0.83.
The Visual Question Answering (VQA) task requires not only accurate answers but also interpretable reasoning processes, particularly in real-world applications where transparency is critical. To reduce annotation and computational costs while maintaining interpretability, the Few-shot Multimodal Explainable VQA (FS-MEVQA) task has been introduced, which aims to generate explanations with limited supervision. In this work, we propose OPeMer (One-shot Prompting and Execution-driven Multimodal Explainable Reasoning), a code-based framework that leverages large language models (LLMs) to generate executable Python programs for multimodal reasoning in a oneshot setting. These programs interact with a lightweight Python API to process visual inputs, capture intermediate reasoning artifacts—such as object crops and spatial relations—and optionally call external tools for open-world visual understanding. The resulting execution traces are serialized and provided to the LLM via a secondary prompt, enabling the generation of coherent multimodal explanations grounded in both visual and textual evidence. Designed without reliance on handcrafted rules or large-scale supervision, OPeMer offers an efficient and extensible approach to explainable multimodal reasoning. Experimental results on the SME dataset demonstrate that OPeMer achieves strong answer accuracy and explanation quality, even when using cost-effective LLMs under limited supervision, suggesting its potential for scalable and interpretable VQA.
