# Ontology-Based Spatial Data Quality Assessment Framework
## 1 Introduction
These are application ontologies that include domain ontology, task ontology, domain-independent ontologies, and linked-data. The system focuses on data quality enhancement instead of data quality assessment, which is the focus of our framework. For the agriculture domain, [PERSON] [15] discussed the automation of specification rules. To accomplish this, [PERSON] et al. [23] used RIF to implement spatial rules and proposed GeoRIF. In their approach, specification rules are modeled as RIF rules, whereas our study implements similar rules using SWRL. As part of their contribution, [PERSON] and colleagues developed a reasoner specifically designed for RIF to enable the execution of these rules. Our framework provides a user interface to create specification rules such as SfO. This situation enables the framework we propose in our study to be reusable, unlike the framework proposed in [PERSON] et al. [23]. [PERSON] et al. [18] designed a domain-specific system to assess the quality of spatial data for autonomous cars proposing an ontology to identify the inconsistent data according to the rules created for them. It introduces a workflow for ensuring the quality of lane-level high-definition digital maps, particularly relevant for autonomous driving systems. The authors propose an ontology-based approach for map data quality assurance creating Map Quality Violation Ontology to formalize map errors and guide the violation detection and resolution process. MQVO defines the types of errors (e.g., topology or geometric errors), their severity levels, and the affected map objects, providing a structure to handle errors in a systematic way. The system is domain-dependent and does not provide a user interface to define rules as our system does. This paper presents a framework that addresses the need for a reusable, interoperable, extensible, and web-based spatial data quality assessment. The framework is based on the development of ontologies, leveraging an open-world approach for formalization and reasoning. By adopting Open-World Assumptions and integrating ontologies, the framework aligns with the objective of creating solutions that are reusable, extensible, interoperable, and web-based. The insistence on closed-world approaches is another \"elephant in the room\" with significant economic costs [24]. The ontological framework is based on two facts:  
* There is a central regulatory system (the authority, the constitution) that is dynamic but stable (not rigid), an open-world approach. Changes are expected but not frequent. To model these needs, SDQO is designed. Data quality concepts and general spatial relation rules are the main scope of the designed ontology. * Some other regulations that have an inherent dependence on the central system. More such rules can be introduced into the system in the future without breaking any previous handling. These rules are more frequently modified and are open to interdependencies. SfO is designed to conceptualize the rules of an institution. Furthermore, the implementation considers what should be expected from domain experts; in particular, they are not expected to have expertise in Semantic Web technologies and Open-World Assumption. Institutional ontologies (SfO) are highly hierarchical. They are created and modified using a GUI which is designed for domain experts or any user to create Specification Ontology without Semantic Web expertise. A short survey showed that this interface had a flat learning curve. After optimization, the interface generates a CSV file; this file is used to make modifications in the institutional ontology automatically. The next Section provides background on data quality elements and ontology concepts. A detailed explanation of each component of the framework is presented in the same Section. Usability of the framework is demonstrated with a case study in Section 3.