Abstract High school English teachers often encounter barriers to assembling diverse, thematically aligned text sets due to limited planning time and pedagogical resources. To address this need, we present T-TExTS (Teaching Text Expansion for Teacher Scaffolding), a knowledge graph (KG)-based recommendation system that suggests Literature texts based on pedagogical merit rather than surface-level metadata. We construct a domain-specific ontology using the Knowledge Acquisition and Representation Methodology (KNARM), instantiate it as a knowledge graph with separate Terminological Box (TBox) and Assertional Box (ABox) components, and evaluate four graph embedding strategies (DeepWalk, biased random walk, hybrid embedding, and Node2Vec) across three dataset configurations (98, 196, and 351 texts) and two relation-weighting schemes. The experimental results reveal that traversal-level expert weighting alone does not outperform algorithmic structural tuning: Node2Vec achieves the highest Area Under the Curve (AUC) at every dataset size (0.9642–0.9750) and the strongest ranking metrics (Hits@K, MRR, nDCG) at larger scales. Combining structural and pedagogical signals through embedding concatenation, however, preserves both interpretability and competitive ranking quality, with the hybrid model maintaining a high AUC across all scales (0.9122–0.9350) and remaining within a few percentage points of Node2Vec on every ranking metric. These findings highlight the value of ontology-driven knowledge graph embeddings for educational recommendation systems and demonstrate that T-TExTS can meaningfully ease the burden of English Literature text selection for secondary educators, supporting more informed and inclusive curricular decisions. The source code for T-TExTS is available at https://github.com/koncordantlab/TTExTS .

Aging biomarkers play a crucial role in uncovering the biological mechanisms behind aging and in developing strategies to support healthy aging. However, the search for reliable aging biomarkers is particularly challenging due to the intricate and multifactorial nature of the aging process. Furthermore, biomarker names and categories are not well-standardized in the current literature. While, a formal definition of a biomarker is nonexistent in the current literature, formally defining biomarkers and standardizing the vocabulary for biomarkers can help accelerate AI research around this concept which can lead to better, faster and more accurate analyses of the existing data and literature. Thus, in this work, we generated Knowledge Graphs that can help us define and standardize biomarkers. We present our Knowledge Graphs (KGs) generated using both an LLM and expert-curated datasets. We compare both KGs to understand why systematic integration between these two models is needed. The integration of Knowledge Graphs (KGs) and Large Language Models (LLMs) presents a promising approach to advancing aging biomarker research through the inherent structured and standardized nature of ontology schemas in knowledge graphs. We showcase that the accuracy of LLM-generated KGs remains questionable but systematic methods such as KNARM can help us with the accuracy of these efforts. In future work, we will propose a synergistic framework where KGs and LLMs interact iteratively to improve both the comprehensiveness and accuracy of aging biomarker information.

Automated Scene Graph Generation (SGG) is challenged by the "Global-Local Dilemma"—the tension between capturing holistic scene context and granular object details. This paper presents a preliminary investigation into a new generative SGG paradigm designed to address this challenge. We introduce a novel, two-track generative framework (GLIIDE) that creates a rich textual intermediary for a Large Language Model (LLM) to extract knowledge. Our hybrid pipeline fuses a YOLO-based track for extracting factual attributes with a DETR-guided track for generating contextual descriptions of object interactions. Our preliminary results on 1,000 images from the Visual Genome dataset demonstrate the potential of this fusion, which achieves a higher Relation Density than relative baselines. This work provides a preliminary proof-of-concept for a generative SGG paradigm that integrates local and global visual information.

The rapid emergence of novel psychoactive substances and evolving slang presents ongoing challenges for effective drug surveillance and public health intervention. Traditional keyword-based methods often fail to capture informal, misspelled, or newly coined terminology prevalent in online communities. In this study, we explore a data-driven approach to slang detection by analyzing Reddit posts from drug-related subreddits. Beginning with a curated list of known drug names, we collect Reddit data using PRAW, a Python-based Reddit API wrapper, and focus on high-engagement posts from sections like “hot,” “new,” and “top.” After preprocessing the text via SpaCy for lemmatization and normalization, we identify frequent out-of-vocabulary terms not present in the seed list. To assess their potential as drug slang, we evaluate the semantic similarity between these candidate terms and known drugs using two NLP models: Word2Vec and BERT-based transformers. While Word2Vec captures surface-level morphological similarities, BERT models leverage deeper contextual understanding for phrase-level comparisons. We compare the outputs of both methods by analyzing their overlap with known drug variants and inspecting top-scoring matches. Our results indicate that BERT models generally identify more semantically valid yet lexically distinct slang terms than Word2Vec. This comparative analysis highlights the relative strengths of each embedding approach and suggests transformer-based models are more effective for scalable and adaptive slang discovery from social media.

A knowledge graph focusing on water quality in relation to health risks posed by water activities (such as diving or swimming) is not currently available. To address this limitation, we first use existing resources to construct a knowledge graph relevant to water quality and health risks using KNowledge Acquisition and Representation Methodology (KNARM). Subsequently, we explore knowledge graph completion approaches for maintaining and updating the graph. Specifically, we manually identify a set of domain-specific UMLS concepts and use them to extract a graph of approximately 75,000 semantic triples from the Semantic MEDLINE database (which contains head-relation-tail triples extracted from PubMed). Using the resulting knowledge graph, we experiment with the KG-BERT approach for graph completion by employing pre-trained BERT/RoBERTa models and also models fine-tuned on a collection of water quality and health risks abstracts retrieved from the Web of Science. Experimental results show that KG-BERT with BERT/RoBERTa models fine-tuned on a domain-specific corpus improves the performance of KG-BERT with pre-trained models. Furthermore, KG-BERT gives better results than several translational distance or semantic matching baseline models.