# Ontology-Based Spatial Data Quality Assessment Framework
## 4 Conclusions  
In this study, a framework is developed for assessing the quality of spatial data, utilizing OWL with SWRL rules. The aim is to make quality assessment interoperable, extensible, reusable, and web-based, for institutions. The study focuses on logical consistency as a key quality element, which refers to how well a dataset adheres to its defined specifications  
Figure 19: An excerpt from resulting ontology for “overlapping buildings in the same layer”. “Error code 2” represents “overlapping features”. Figure 20: An excerpt from resulting ontology for “road features that are crossing over buildings”. “Error code 1” represents “crossing features”. and rules. It examines topo-semantic consistency and domain consistency, two subclasses of logical consistency that are commonly required quality elements in spatial datasets. The framework consists of two types of ontologies: the main ontology, SDQO, and the specification ontologies, referred to as SFOs. The goal of designing SFOs in a manner consistent with SDQO is to create a reusable and domain-independent framework. SFOs are designed based on specifications or user requirements, predominantly employing class hierarchies that enable easy updates and manipulations by domain experts. A GUI facilitates rule formalization through the generation of SfO ontologies. Based on domain expert inputs, a CSV file is generated. This file then undergoes an optimization process, resulting in the creation of an SfO ontology. SDQO is created to incorporate general classes necessary for spatial quality assessment. Specifications often involve spatial rules that define relations between classes. For example, a \"building\" must not overlap with \"sea\", \"lake\", their subclasses, or other buildings. These spatial rules are expressed in the specifications using distinct rules for each relation. Within the framework, common rules are optimized across different classes, taking spatial relations into account. This optimization allows for efficient evaluation in a single step. SfO and SDQO are mapped using Java libraries including OWLAPI. Utilizing relations, SDQO ontology rules are implemented, and subsequently, quality assessment is performed. A data quality report is automatically generated by the proposed framework. The framework is tested with a case study for a base map of a province. The results show that all overlapping buildings, overlapping parcels, roads that cross over buildings, and buildings that are not entirely within a cadastral parcel in the classes without causing ontology inconsistencies are identified. Challenges are posed by data property-based necessary type restrictions without querying, but these are overcome by asserting the same single value for a data property across all members of specific classes using SWRL. A range of data property values is required for some problems, and this is addressed with the help of SQWRL. For efficiency purposes, the study ignores disjoint geometries. Some regulations include disjoint cases, but they typically can be redefined without disjointness. The case study did not involve regulations related to touching objects; otherwise, a separate list for touching objects in relevant classes needed to be collated. In the remaining cases, the interiors do intersect. With Java JTS suite, one can check for interior intersections and mask them appropriately (DE9 IM type), and then reuse this information later without losing much efficiency. There are typically faster languages like C and Julia. JTS, along with OWLAPI and reasoners, are the main reasons for the choice of Java as a programming language. The whole process is performed with Java code accessed through GUI or Protege. Protege (itself a Java-based program) is used for editing the ontologies, typically by domain experts. It is bundled with plugins and libraries such as SWRLAPI. For decidability issues, in the data ontology and SWRL, feature IDs are added as property values. The name and feature ID could be made to be the same in the case study.