# Linked Open Government Data: Still a Viable Option for Sharing and Integrating Public Data?
## 4 Related Works
By employing statistical methods to analyze the responses to the questionnaires, they were able to identify five key factors that influence the implementation and effectiveness of government initiatives. These factors, as determined through principal component factor analysis, include governance, resource constraints, capacity building, technology, and lack of awareness. These elements were found to be significant in shaping the outcomes and effectiveness of government policies and initiatives within the surveyed agencies. [PERSON] et al. 's 2018 study [67] presented a systematic analysis of OGD literature, identifying six dimensions of data friction. Their categorization of data frictions overlaps significantly with the findings of our study. However, they differentiate between data quality and technical aspects, whereas, in our research, following the framework established by [PERSON] et al. [65], these two dimensions are merged into one. Building on the foundations laid by the mentioned studies, our survey highlights the opportunity to systematically examine the challenges associated with LOGD. This area, which has not been fully explored in existing research, is where our survey aims to fill the gap. Through a thorough analysis of obstacles in LOGD, we aim to contribute to a more effective approach for the dissemination and implementation of LOGD practices. This is essential for overcoming the current stagnation we have highlighted in the field. Indeed, although many previous studies have addressed barriers in the LOGD space, they often carry this out superficially, identifying a problem only to immediately suggest a technological solution. This approach tends to overlook the complexity and interconnectedness of the challenges. However, a few of the studies we reviewed offer somewhat more elaborate insights into several LOGD barriers. [PERSON] et al. [68] delve into the intricate task of connecting organizational information from public datasets to established entities in knowledge graphs such as Wikidata and DBpedia. They undertook this by manually establishing links between datasets from the Open Data Portal Watch [24] and their respective publishing organizations in these knowledge graphs. Through this meticulous process, they uncovered a series of interconnected challenges. These ranged from the dynamic nature of organizations, which frequently change, to the complexities arising from the lack of a uniform base ontology that would facilitate standardized link creation. Furthermore, they grappled with the variable quality of metadata, which is crucial for establishing accurate links, and the complexities introduced by multilingual datasets. Another notable challenge was distinguishing between similar public sector organizations, a task requiring precise disambiguation. To navigate these obstacles, the authors not only proposed targeted solutions for each identified challenge but also advocated for a community-driven approach. They suggested a hand-search service that would enable the collaborative efforts of the data science community and dataset publishers in annotating and refining dataset-level links. This collaborative method highlights the importance of human input and collective effort in enhancing the accuracy and reliability of data linking in the public sector. In their 2020 study, [PERSON] et al. [69] investigated the use of LOGD to improve budget transparency in Kosovo. Their research, which included desk reviews and interviews with government and NGO officials, revealed key challenges: poor metadata quality (e.g., temporality, formats, provenance), difficulties in data linking by government employees, and a lack of educational programs for effective data management. The main conclusion drawn from the interview responses indicates that the application of LOGD in Kosovo remains limited. Despite this, there is a general belief in the potential for implementing LOGD in managing Kosovo's budgetary data.