Contamination by heavy metals, per- and polyfluoroalkyl substances (PFAS), and other emerging pollutants poses serious risks to environmental and human health. Effective monitoring and tracing require integrating data from diverse sources. A knowledge graph approach enables semantic integration, but relies on an ontology that supports intuitive and robust querying and reasoning. To address this, we present the Contaminant Observations and Samples Ontology (ContaminOSO), a framework for semantically enriching environmental contaminant data. Built on SOSA and QUDT ontologies, ContaminOSO introduces key extensions to meet contamination-specific needs and real-world data challenges. This paper highlights four of its core design solutions: (1) extending SOSA to model multiple features of interest; (2) using QUDT to standardize the representation of contaminants and observed properties; (3) developing a detailed and nuanced pattern for measurement result representation using QUDT and STAD; and (4) adopting a pragmatic approach for connecting to existing taxonomies from the OBO Foundry, such as the NCBI organismal classification and relevant subsets of the Food Ontology (FoodOn), for classifying samples.

We introduce AnNER, an open-source, lightweight, web-based tool for annotating scientific and other domain-specific entities—including named entities—in text corpora. AnNER offers a full expert review mode that allows reviewers to accept, reject, or revise annotations created by humans or machines. The full provenance of all annotation and review actions is captured in a JSON-based semantic model to ensure utmost transparency during initial annotation and review. AnNER also supports exporting annotations and their histories as RDF documents compatible with the Ontology for Named Entity Representation (OnNER), enabling easy knowledge graph construction to facilitate querying, analysis, and downstream machine learning tasks involving the annotated entities.

A significant portion of scientific knowledge resides within scholarly publications, both in print and digital formats. Recent advancements in natural language processing and information extraction techniques have enhanced the accessibility of this knowledge for further automated querying and processing. Structured and semantically-aware representations, such as ontologies, play a crucial role in simplifying and integrating access to this vast pool of knowledge. While several ontologies have been developed to capture the structure and discourse of scientific publications, there is a notable scarcity of ontologies for succinctly representing named entities that are present in scholarly documents. This paper introduces the Ontology for Named Entity Representation (OnNER) to address this gap. OnNER is designed to represent named entities – the terms identified and labeled using named entity recognition (NER) methods – from scholarly publications. The ontology provides a structured semantic representation of the named entities, how they are labeled, and where they occur. We discuss the overall design of OnNER, its integration with other ontologies, and demonstrate how the ontology facilitates advanced querying of named entities’ presence and collocation within and across publications.

Use and reuse of an ontology requires prior ontology verification which encompasses, at least, proving that the ontology is internally consistent and consistent with representative datasets. First-order logic (FOL) model finders are among the only available tools to aid us in this undertaking, but proving consistency of FOL ontologies is theoretically intractable while also rarely succeeding in practice, with FOL model finders scaling even worse than FOL theorem provers. This issue is further exacerbated when verifying FOL ontologies against datasets, which requires constructing models with larger domain sizes. This paper presents a first systematic study of the general feasibility of SAT-based model finding with FOL ontologies. We use select spatial ontologies and carefully controlled synthetic datasets to identify key measures that determine the size and difficulty of the resulting SAT problems. We experimentally show that these measures are closely correlated with the runtimes of Vampire and Paradox, two state-of-the-art model finders. We propose a definition elimination technique and demonstrate that it can be a highly effective measure for reducing the problem size and improving the runtime and scalability of model finding.

Satisfiability of first-order logic (FOL) ontologies is typically verified by translation to propositional satisfiability (SAT) problems, which is then tackled by a SAT solver. Unfortunately, SAT solvers often experience scalability issues when reasoning with FOL ontologies and even moderately sized datasets. While SAT solvers have been found to capably handle complex axiomatizations, finding models of datasets gets considerably more complex and time-intensive as the number of clause exponentially increases with increase in individuals and axiomatic complexity. We identify FOL definitions as a specific bottleneck and demonstrate via experiments that the presence of many defined terms of the highest arity significantly slows down model finding. We also show that removing optional definitions and substituting these terms by their definiens leads to a reduction in the number of clauses, which makes SAT-based model finding practical for over 100 individuals in a FOL theory.

The need to share and integrate heterogeneous geospatial data has resulted in the development of geospatial data standards such as the OGC/ISO standard Simple Feature Access (SFA), that standardize operations and simple topological and mereotopological relations over various geometric features such as points, line segments, polylines, polygons, and polyhedral surfaces. While SFA’s supplied relations enable qualitative querying over the geometric features, the relations' semantics are not formalized. This lack of formalization prevents further automated reasoning - apart from simple querying - with the geometric data, either in isolation or in conjunction with external purely qualitative information as one might extract from textual sources, such as social media. To enable joint qualitative reasoning over geometric and qualitative spatial information, this work formalizes the semantics of SFA’s geometric features and mereotopological relations by defining or restricting them in terms of the spatial entity types and relations provided by CODIB, a first-order logical theory from an existing logical formalization of multidimensional qualitative space.

Multiple ontologies for units of measure have been proposed within the Applied Ontology community, and all of these ontologies introduce an array of new classes based on supposed distinctions between quantities, quantity kinds, and measures. Units are combined using notions of dimensional analysis that often conflate the combination of units with algebraic operations on real numbers. In this paper we present an alternative approach that shifts the focus to the connection between the units of measure and the physical objects and processes that are being measured. One of the key features of this approach is that it makes minimal ontological commitments with respect to the TUpperWare upper ontology – the only new classes that are introduced are the classes for the units of measure. We propose correct and complete axiomatizations for combining units of measure, and the correct axiomatization of the relationship between the units of measure and the existing upper ontology.

Mereotopological relations, such as contact, parthood and overlap, are central for representing spatial information qualitatively. While most existing mereotopological theories restrict models to entities of equal dimension (e.g., all are 2D regions), multidimensional mereotopologies are more flexible by allowing entities of different dimensions to co-exist. In many respects, they generalize traditional spatial data models based on geometric entities (points, simple lines, polylines, cells, polygon, and polyhedra) and algebraic topology that power much of the existing spatial information systems (e.g., GIS, CAD, and CAM). Geometric representations can typically be decomposed into atomic entities using set intersection and complementation operations, with non-atomic entities represented as sets of atomic ones. This paper accomplishes this for CODI, a first-order logic ontology of multidimensional mereotopology, by extending its axiomatization with the mereological closure operations intersection and difference that apply to pairs of regions regardless of their dimensions. We further prove that the extended theory satisfies important mereological principles and preserves many of the mathematical properties of set intersection and set difference.

Geographic information systems (GIS) customarily encode spatial information using geometric objects (points, polylines and polygons) and their locations. But people frequently use qualitative relations, such as topological relations (e.g., connection or overlap) or cardinal direction relations (e.g. North or Southeast), to describe spatial scenes. While topological relations have been integrated into modern GIS, direction relations have remained isolated from GIS and are not available for user interaction. Instead, a user must visually infer them from map depictions.

Comprehensive water data management requires semantically integrating various data models and ontologies that represent hydrologic knowledge. But integration is hampered by nuances in the use of water-related vocabulary (e.g. terms such as water body, aquifer, reservoir, well, etc.) across water representations and by the reliance on a mix of formal and informal specifications of how these terms are interpreted in each representation. Reconciliation of only partially formal encodings of the semantics of water representations requires manual inspection using tools from ontological analysis. This paper investigates as to what extent a domain reference ontology that is fully formalized in first-order logic can guide the ontological analysis.In particular, it is studied as to what extent the Hydro Foundational Ontology (HyFO), which encodes the semantics of a small set of unifying water concepts and associated relations in first-order logic, can serve as a reference ontology for the water domain to steer the ontological analysis of individual water representations, and to formalize their semantics more fully. This is specifically tested on the Groundwater Markup Language (GWML2). The result is GWML2-FOL, a concise logical description of GWML2’s key terms as a logical extension of HyFO. GWML2-FOL is structured into three layers of terms (mostly classes) of increasing specificity. The top layer consists of terms shareable across the earth and physical sciences, an intermediate layer includes HyFO’s hydro terms that span surface and subsurface water storage, and the bottom layer encapsulates groundwater specific GWML2 terms. The analysis and stratification uncover semantic ambiguities in GWML2 and suggest terminological and semantic clarifications and modifications in preparation for integrating GWML2 with other semantic water representations.The analysis also identifies two necessary additions to the HyFO: the concept of a hydro rock body as a hybrid of water and solid matter, which generalizes key groundwater terms such as aquifers or wells, and the concept of dependent hydrologic features such as springs, water tables, or divides. More broadly, differences between domain ontologies and a domain-reference ontology and their respective complementary roles in semantic-enabled geosciences are outlined.

The spatio-temporal processes that describe hydrologic flow - the movement of water above and below the surface of the Earth -- are currently underrepresented in formal semantic representations of the water domain. This paper analyses basic flow processes in the hydrology domain and systematically studies the hydrogeological entities, such as different rock and water bodies, the ground surface or subsurface zones, that participate in them. It identifies the source and goal entities and the transported water (the theme) as common participants in hydrologic flow and constructs a taxonomy of different flow patterns based on differences in source and goal participants. The taxonomy and related concepts are axiomatized in first-order logic as refinements of DOLCE's participation relation and reusing hydrogeological concepts from the Hydro Foundational Ontology (HyFO). The formalization further enhances HyFO and contributes to improved knowledge integration in the hydrology domain.

This paper explores an alternative vision for upper ontologies which is more effective at facilitating the sharability and reusability of ontologies. The notion of generic ontologies is characterized through the formalization of ontological commitments and choices. Ontology repositories are used to modularize ontologies so that any particular upper ontology is equivalent to the union of a set of generic ontologies. In this way, upper ontologies are not replaced but rather integrated with other theories in the ontology repository.

The relation between an object and its matter is fundamental to all physical sciences, and represented widely and diversely in scientific ontologies. An under-appreciated aspect of this relation is the emergence of voids at finer levels of physical granularity. In this paper we enhance the constitution relation to account for the presence of finer voids, and show how this helps delineate two forms of constitution that hold within and between granular levels. This enhanced notion of the constitution relation is characterized formally, and is applied to hydro ontology development.

Material-spatial interdependence (mat-dep) is a type of dependence in which the physical extents of two entities are necessarily and mutually contingent, e.g. an object and its matter, or a hole and its host. Such dependence is commonly found amongst arrangements of physical entities, particularly in models of the natural environment. In this paper, we analyze and formally characterize mat-dep, and show how it augments the physical characterization of the containment, constitution, and hosting relations, primarily for development of a hydro ontology.

Voids are extremely important to water science, because their size and connectivity determines the storage and flow of water both above and below the ground surface. While previous formal theories about voids strictly consider holes hosted inside objects, we generalize voids to also include spaces between objects, and distinguish voids in macroscopic objects from those occurring microscopically in an object's matter. These notions are axiomatized in first-order logic as an extension of the DOLCE ontology, and are applied to key aspects of hydrology and hydrogeology, laying the groundwork for a foundational hydro ontology.

In this paper we show how the relationships between first-order ontologies within a repository can be used to support ontology verification. We discuss the use of representation theorems and classification theorems to characterize the models of an ontology, and then show how such results can be obtained from notions such as relative interpretation.