Hande McGinty

Ontologies support shared meaning across Semantic Web applications, but selecting an appropriate ontology for reuse remains difficult because existing candidates often differ in documentation quality, semantic clarity, structural connectivity, and logical soundness. Reuse decisions are therefore frequently based on familiarity or intuition rather than reproducible evidence. This paper extends WiseOWL [A. S. Dalal, M. Baloch, A. Y. Lin, A. M. Masci, K. M. Jagodnik and H. K. McGinty, WiseOWL: A methodology for evaluating ontological descriptiveness and semantic correctness for ontology reuse and ontology recommendations, in 2026 Int. Conf. Semantic Computing, 2026, pp. 9–15], a schema-level methodology and web-based tool for evaluating ontology reuse readiness from an ontology’s own RDF/OWL content. WiseOWL defines four core quality metrics: Well-Described, which measures the coverage of human-readable annotations; Well-Defined, which evaluates the semantic alignment and adequacy of labels and definitions using transformer-based embeddings and textual heuristics; Connection, which assesses the breadth, diversity, and richness of object–property relationships; and Hierarchical Breadth, which measures the balance of class-taxonomy depth and branching, including hierarchy inferred from complex OWL axioms. To complement these aggregate scores, this extension introduces three diagnostic metrics: Logical Consistency, Structural Distinctiveness, and Semantic Distinctiveness, which identify unsatisfiable classes, redundant subclass assertions, and candidate near-duplicate concepts, respectively. The methodology is now formalized using RDF graph notation, bounded score definitions, algorithmic pseudocode, and stated metric properties. WiseOWL is implemented as a Streamlit application that accepts OWL files, converts them to RDF Turtle, computes normalized 0–10 scores, and presents interactive visualizations with exportable results. Evaluation is extended to seven publicly available ontologies — Plant Ontology (PO), Gene Ontology (GO), Semanticscience Integrated Ontology (SIO), Food Ontology (FoodON), Friend of a Friend (FOAF), Dublin Core Terms, and GoodRelations. The results show that WiseOWL produces distinguishable quality profiles and identifies concrete areas for improvement.

Constructing expressive axioms in the Web Ontology Language and description logics, such as subclass, disjointness, domain, range, and general concept inclusion axioms, remains a bottleneck in ontology learning, because manual authoring does not scale to large knowledge graphs. Recent surveys have covered parts of this field, but none that we identified places lexical methods, symbolic and neural-symbolic description logic learners, triple-based inductive and logical methods, and large language model methods side by side on shared dimensions. This narrative survey unifies these four families under a single taxonomy of axiom construction over Resource Description Framework data and related inputs, including triples, text, labeled examples, ontology signatures, and prompts. We compare the families by input requirements, axiom coverage, controllability, scalability, and dependence on triples. We identify a recurring architectural pattern in the language model literature, in which a generative model proposes candidate axioms and a verification layer based on retrieval, schema repair, entailment, methodology audit, or description logic reasoning constrains them. Because the studies use different benchmarks and metrics, we do not treat their results as directly comparable, and we discuss a hybrid pipeline argument in which lexical and language model proposers, triplebased scorers, and description logic learners and reasoners each carry the operational load they are best suited for. The principal open problem is evaluation, especially benchmark leakage in the language model era.

Urban flooding is driven by pluvial and infrastructure-mediated processes, yet flood susceptibility models typically rely on terrain-centric predictors and on flood inventories or remotely sensed extents. This study evaluates two underused inputs, high-water marks surveyed by municipal crews as presence observations and a city stormwater geodatabase as a source of infrastructure predictors, such as outfall proximity. Using 277 presence points from 11 flood events (2007–2024) in Manhattan, Kansas, and 17 predictors at 10 m resolution, we fitted a maximum-entropy model, compared it with five supervised classifiers, and assessed generalization by spatial-block and leave-one-event-out cross-validation. Distance to the nearest outfall was the strongest single predictor (area under the receiver-operating-characteristic curve, AUC = 0.915). Spatial-block and event cross-validation gave AUCs of 0.935 and 0.952, respectively, against 0.983 for a random split, and no model differed significantly from another after Holm correction. After controlling for road access and four terrain and land-cover confounders, outfall proximity still added 0.025 AUC. The regulatory 1% floodplain captured 40% of held-out marks while covering 17% of the study area, whereas the model captured 95% within 7%. Refitted on marks outside the floodplain, distance to outfall alone still reached AUC = 0.920, so the association is not limited to riverine flooding. An exploratory socioeconomic overlay found no significant association after Holm correction. Proximity to stormwater outfalls was the strongest available correlate of observed flooding. Because these two inputs are municipal records and the code is openly available, the workflow can be adapted to other cities that hold them.

In response to the growing need for structured, interoperable agricultural data, this paper presents the Sustainable Wheat Production Datahub, a modular, graph-based framework that brings diverse wheat production datasets together into a single, queryable store. Using the Knowledge Acquisition and Representation Methodology (KNARM), with domain experts in the loop, we developed ontologies for nutrient management and disease management, two of the areas that most affect wheat yield and its sustainability, covering practices such as nitrogen fertilization and fungicide-based disease control. The two ontologies are federated, meaning they are maintained as separate but connected modules, joined by a bridging layer of cross-domain links, and together they form the schema of a knowledge graph (KG). We construct this KG by populating the ontologies with data from diverse sources, including field trials, expert knowledge, and environmental descriptors. We validate the ontologies, showing the KG accurately answers practical agronomic questions expressed in SPARQL. We demonstrate that the KG derives facts entailed by the ontologies beyond those explicitly stored, and that a single query can draw across independently sourced datasets. The framework also has practical implications for wheat research and extension programs, since it makes data from different sources easier to find, combine, and reuse. Designed to expand toward the full wheat lifecycle, from farm to table, this work establishes the foundation for a scalable, semantically rich global food systems datahub.

The increasing adoption of semantic web technologies and the rapid proliferation of knowledge graph-driven tools underscore the urgent need for a robust, scalable infrastructure to manage ontology change requests, foster community feedback, and support both developers and users with advanced tooling. There is a growing demand for a platform that not only facilitates these processes but also accelerates community engagement and practical use of ontologies and knowledge graphs through AI-ready technologies such as Neo4j. To address this gap, we present the Ontology Marketplace, a community-driven, event-aware platform designed to streamline the submission, tracking, moderation, and implementation of ontology-related requests. By establishing seamless communication channels among users, tools, and curators, this platform aims to reduce the turnaround time for ontology updates from months to minutes.

Although understanding the formation and expression of human behavioral patterns is central to psychology and other social sciences (e.g., sociology, economics), the lack of a standardized vocabulary has stunted progress in these fields. Establishing a formal ontology of human behavior would enable researchers to operate within a consistent and unified framework both within and across disciplines. This study introduces the Behavioral Patterns Ontology (BPO), a foundational step toward the formalization of psychological concepts through structured behavioral patterns. While existing ontologies in the behavioral and social sciences address domains such as addiction, behavior change interventions, mental functioning, emotions, and social entities, each is tailored to a highly specific purpose and does not systematically capture the learning and conditioning mechanisms through which behaviors are formed, reinforced, and modified over time. The BPO moves beyond domain-specific applications and provides a transferable framework for representing behavioral patterns across contexts. The BPO consists of 162 classes and 21 object properties interconnected through 149 hierarchical relationships and defined by 644 axioms, providing a structured representation of behavioral processes. To demonstrate its practical applicability, we used the BPO to annotate TikTok videos related to illicit/recreational drug-seeking behavior in order to better understand the observable behavioral patterns associated with drug-seeking activities on social media platforms. We then analyzed the annotated data using a Social Media Analysis Dashboard, developed by our adjacent team, which enabled the visualization and interpretation of human behavioral patterns at the level of individual videos. The annotations were next queried using ontology-driven SPARQL analyses, with results organized across multiple use cases, demonstrating that the BPO accurately captured learning mechanisms, substance-specific behavioral patterns, and the drivers and contextual settings of drug-seeking behavior on the TikTok platform. Lastly, a machine learning (ML) model was implemented as a preliminary feasibility analysis designed to classify videos into three behavioral categories: drug-seeking behavior, avoidance behavior, and no behavior. This experiment serves to evaluate the predictive potential of ontology-driven annotations under limited data conditions and thereby demonstrates the practical viability of integrating structured semantic modeling with data-driven classification approaches.

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 .

This study investigates how age affects the biological response of mammary gland tissue to spaceflight, focusing on potential risks for breast and ovarian cancers. We introduced a novel bioinformatics pipeline to analyze tissues from young and old mice which were sent to space. Through our analysis, we found that older mice had a significantly stronger molecular response to space flight elements. Key biological processes, such as signal transduction and gene regulation, were observed to be more active in the older group. The molecular changes in older mice showed a stronger association with genes linked to cancer, particularly ovarian and breast cancer. Our analysis points to conclusions that show how age is a critical factor in how the body responds to spaceflight. This study points to specific biological pathways that could be targeted for future cancer risk assessment and prevention for astronauts as well as assessing reproductive system risks for space missions.

Climate-driven heat and water stress are increasingly compromising rainfed maize yields in transition zones, with significant implications for global food security. While continental-scale models of crop suitability exist, they often fail to capture the high-resolution heterogeneity of agricultural landscapes or distinguish between irrigated and rainfed systems in semi-arid regions. This study models the current and future suitability of rainfed maize in Kansas, USA, using a Maximum Entropy (MaxEnt) approach. To accurately isolate biophysical constraints, we employed a novel data-filtering workflow using the USDA Cropland Data Layer (CDL) and Landsat-based Annual Irrigated Datasets (LANID) to train the model exclusively on rainfed occurrences. We projected suitability shifts for the mid- (2041–2070) and end-of-century (2071–2100) periods under two CMIP6 Shared Socioeconomic Pathways (SSP3-7.0 and SSP5-8.5), using high-resolution CHELSA bioclimatic variables. The model, achieving an Area Under the Curve (AUC) of 0.73 and validated against 30 years of historical USDA production records, reveals a distinct spatial contraction of areas climatically suitable for growing maize. Projections indicate a significant decline in suitability across Western and Central Kansas driven by rising temperatures and precipitation variability, with the most highly suitable optimal habitats projected to decline by approximately 90% by mid-century. These findings quantify mounting climate impacts on maize-growing areas of the Great Plains and provide spatially explicit baselines for the development of regional adaptation strategies and groundwater conservation policies.