# Ontology-Based Spatial Data Quality Assessment Framework
## 1 Introduction
The institution needs to have capable experts in one or more software types and adapt the institution to that software. The software user has to know which operation to apply and has to know the input and output. These proposed solutions exist alongside several traditional quality assessment software, most of which are proprietary [10; 11], and have their own closed rule-based structures [10; 11; 12]. In the literature, there are several ontology-based studies for quality management. While there are various ontology-based solutions available for data quality management, it is important to note that some of these solutions primarily focus on specific domains and do not specifically address the challenges associated with spatial data quality management [3; 4; 5; 6]. [PERSON] [3] proposes a data quality management system based on ontologies, especially for data streams. Functions for the quality assessment are implemented as instances in the system with correspondent SPARQL query constructs. [PERSON] et al. [4] propose Luzzu, a linked data quality assessment framework that uses an ontology for the back end. It implements a domain-specific language: Luzzu Quality Metric Language (LQML). The metrics that are applied with Luzzu are already classified for linked data. Users can invoke these metrics with the associated LQML terms. [PERSON] [5] introduced the Semantic Web Information Quality Assessment Framework. The framework has a user wiki, which enables users to define quality requirements based on predefined data quality dimensions specific to Semantic Web data. It implements a Data Quality Management vocabulary to formally represent user-defined quality requirements. Our system shares similarities with [3; 4; 5] in that they all allow users to define quality requirements based on predefined rules. These studies are all domain-dependent, and our system focuses on spatial data-related quality dimensions incorporating a user interface to define SfO for any spatial domain. SDQO has Semantic Web Rule Language (SWRL) rules that are created for topo-semantic consistency assessment, which makes our system different from the mentioned studies. [PERSON] [6] explains an ontology-based quality assessment framework, the Semantic Framework for Data Quality Assessment (SemDQ), for specific clinical domain data. SemDQ is similar to the one proposed in this study. Two types of ontology were designed: one for representing data model, and the other is data quality criteria ontology designed for quality dimensions. The main difference is the SfO creation in our system for users to define their rules and support spatial quality dimensions. These existing solutions offer valuable approaches for managing data quality across diverse domains, but there remains a need for dedicated solutions tailored specifically to spatial data quality management. Studies based on ontologies have also been conducted, particularly for the management of spatial data quality [7; 8; 12; 13; 14; 15; 16; 17; 18; 19]. The literature review suggests that ontology-based studies share common implementation aspects in spatial data quality management. For any domain, rules are created to assess data against consistency to its specification rules. These rules are implemented with ontologies by using the recommended or standard rule languages, for instance, SWRL, SPARQL Protocol and RDF Query Language (SPARQL), Semantic Query-Enhanced Web Rule Language (SQWRL), and the Rule Interchange Format (RIF). [PERSON] [7] is one of the pioneer researchers using ontologies for spatial data quality assessment. In the study, the National Topographic Data Base of Canada is assessed against the rules of schema. Despite leveraging ontologies, the study adopts a closed-world approach using Prolog rules. While it successfully performs the task of testing the logical consistency of data, it is not designed for reuse. The user can locate the data that do not follow that schema, but they cannot customize the rules. Any changes to the schema necessitate a complete rewrite.