# Ontology-Based Spatial Data Quality Assessment Framework
## 1 Introduction  
Institutions produce and share spatial data with the National Spatial Data Infrastructure (NSDI). Quality assessments of data produced by governmental and other entities frequently reveal flaws in data management, particularly those resulting from redundancy and judgments made during the creation or manipulation of spatial data. The production of spatial data in institutions often suffers from adhering to the specified requirements and specifications. In addition, these institutions, because of their purposes, have separate and sometimes conflicting policies, even though they may have common interests. Attempts at interoperability may fail due to human error, such as de facto solutions. Inefficiency in management and delegation leads practitioners to their own temporary solutions to quickly \"solve\" the problems they encounter. Partial inputting a road detail, where the road detail has a common boundary with a building, is an error encountered in the base maps and can be given as an example of a quick temporary \"solution\". As a result of theproblems, inconsistencies in the produced data are inevitable, which in turn reduces their overall quality. Quality refers to logical consistency that is accepted as conformance of data to specifications. Specifications include rules with respect to data application schemas, including attribute and topological rules in a specific domain. \"Contours must not cross each other\" is a rule related to the contour feature in an application schema. Examples of inconsistencies in spatial data produced by an organization are intersecting contours, a road crossing over a building, or a missing attribute value for a feature when it is required. Failure to manage quality and overcome associated problems has, among technical problems, economic consequences. In the U.S. alone, poor data quality costs millions of dollars per organization and billions of dollars overall [1; 2]. In addition to domain experts and people in the field, institutions employ various experts who specialize in specific software. The structure of the software is rigid, and the quality assessment process is performed manually. Decision makers may not be able to make the best decisions, even if they are personally able to conceive those decisions. This limitation is exacerbated by the lack of interaction between institutions, which increases data redundancies and inevitably reduces efficiency. Non-standardized, proprietary-based software- or manual quality assessment methods cause reusability, interoperability, and extensibility problems. Research into novel, extensible, reusable, interoperable, and web-based methods for evaluating the quality of geospatial data is required considering these issues. Currently, the technical backbone of NSDI is web services and it is evolving into Semantic Web services. The fundamental building blocks of Semantic Web services are ontologies. Ontology can be defined at an abstraction level that allows for easy reusability by new users with eventually different datasets at hand. Therefore, in this study, ontology-based methods for quality assessment have been researched in addition to the software-based methods that are already applied by institutions or in the literature. There is academic research based on ontologies [3; 4; 5; 6; 7; 8] and software such as 1 Spatial 1 Integrate and ArcGIS Data Reviewer that propose solutions to overcome various data quality assessment issues [9; 10; 11]. QGIS Geometry Checker is an open-source software ([[https://docs.qgis.org/3.34/en/docs/user_manual/plugins/core_plugins/plugins_geometry_checker.html](https://docs.qgis.org/3.34/en/docs/user_manual/plugins/core_plugins/plugins_geometry_checker.html)]([https://docs.qgis.org/3.34/en/docs/user_manual/plugins/core_plugins/plugins_geometry_checker.html](https://docs.qgis.org/3.34/en/docs/user_manual/plugins/core_plugins/plugins_geometry_checker.html)), accessed on 1 September 2024) alternative. Even if the software is open source, it will not be easily customized per institutional needs.