Ontologies are useful for automatic machine processing of domain knowledge as they represent it in a structured format.

This paper presents a framework for provably fair AI that overcomes the limits of current bias mitigation methods by systematically removing all sensitive information and its proxies. Using ontology engineering in OWL 2 QL, it formally defines sensitive attributes and infers their proxies through logical reasoning, constructing a sigma algebra G that captures the full structure of biased patterns. Fair representations are then obtained via Delbaen Majumdar optimal transport, which generates variables independent of G while minimizing L2 distance to preserve accuracy. This guarantees true independence rather than mere decorrelation. By modeling bias as dependence between sigma algebras, compiling ontological knowledge into measurable structures, and using optimal transport as the unique fair transformation, the approach ensures complete fairness in tasks like loan approval, where proxies such as ZIP code reveal race. The result is a certifiable and mathematically grounded method for trustworthy AI.

While previous research has implicitly leveraged these hierarchies to enhance LMs, approaches for their explicit encoding are yet to be explored.

We investigated the effectiveness of Digit Embedding. We can see that the numbers in the (100090-100140) range are clustered together. Supplementary Materials for PERFOGRAPH: A Numerical A ware Program Graph Representation for Performance Optimization and Program Analysis We investigated with more ranges. Figure 3 shows the 2-d embedding of decimal numbers in the range [1.0, 10.0] and [20.0-31.0]. And the numbers with larger differences like (1.6478, 30.7010), (5.339, 30.5113) are far from Figure 3: Embedding of decimal numbers in the range [1.0, 10.0] and [20.0-31.0] 2 So, the above examples clearly demonstrate the effectiveness of Digit Embedding for generating the Please note that in this setup, the Digit Embedding is still applied.

Metadata is stored in a hidden directory in each project and users are not expected to manipulate it directly. This allows us to easily import datasets from external repositories (e.g. Metadata is added to Renku projects every time any of the entities in a project are created or updated. The metadata from different entities (e.g. a dataset file can also be an input to a An illustration of this structure is shown in Figure 1, which shows an example pipeline from raw data collection to model training. The Renku metadata in the KG can be extended with custom metadata using plugins for the CLI. CLI and executed alongside Renku functionality.

Contemporary language models are fluent yet routinely mis-handle the types of meaning their outputs entail. We argue that hallucination, brittle moderation, and opaque compliance outcomes are symptoms of missing type-theoretic semantics rather than data or scale limitations. Building on Montague's view of language as typed, compositional algebra, we recast alignment as a parsing problem: natural-language inputs must be compiled into structures that make explicit their descriptive, normative, and legal dimensions under context. We present Savassan, a neuro-symbolic architecture that compiles utterances into Montague-style logical forms and maps them to typed ontologies extended with deontic operators and jurisdictional contexts. Neural components extract candidate structures from unstructured inputs; symbolic components perform type checking, constraint reasoning, and cross-jurisdiction mapping to produce compliance-aware guidance rather than binary censorship. In cross-border scenarios, the system "parses once" (e.g., defect claim(product x, company y)) and projects the result into multiple legal ontologies (e.g., defamation risk in KR/JP, protected opinion in US, GDPR checks in EU), composing outcomes into a single, explainable decision. This paper contributes: (i) a diagnosis of hallucination as a type error; (ii) a formal Montague-ontology bridge for business/legal reasoning; and (iii) a production-oriented design that embeds typed interfaces across the pipeline. We outline an evaluation plan using legal reasoning benchmarks and synthetic multi-jurisdiction suites. Our position is that trustworthy autonomy requires compositional typing of meaning, enabling systems to reason about what is described, what is prescribed, and what incurs liability within a unified algebra of meaning.

The focus on'food as medicine' is gaining traction in the field of health and several studies conducted in the past few years discussed this aspect of food in the literature. However, very little research has been done on representing the relationship between food and health in a standardized, machine - readable fo rmat using a semantic web that can help us leverage this knowledge effectively. To address this gap, this study aims to create a knowledge graph to link food and health through the knowledge graphs' ability to combine information from various platforms foc using on flavonoid contents of food found in the USDA's databases and cancer connections found in the literature. We looked closely at these relationships using KNARM methodology and represented them in machine - operable format. The proposed knowledge graph serves as an example for researchers, enabling them to explore the complex interplay between dietary choices and disease management. Future work for this study involves expanding the scope of the knowledge graph by capturing nuances, adding more related d ata, and performing inferences on the acquired knowledge to uncover hidden relationships.