# Linked Open Government Data: Still a Viable Option for Sharing and Integrating Public Data?
## 6 What Factors Are Holding back the Spread of LOGD?
### LOGD Data Friction
#### 6.2.1 Technical Dimension  
The technical dimension encompasses several critical facets that collectively shape the interconnected data landscape. _Heterogeneity_, a key concern in this dimension, arises from the diverse nature of data sources, structures, and formats, necessitating strategies for improved integration. _Vocabularies_ play a crucial role in achieving semantic interoperability, with efforts focused on aligning existing vocabularies and mitigating heterogeneity. _LD lifecycle complexity_ highlights the complexity of establishing meaningful relationships between entities, emphasizing the need to address data linkage issues for the reliability of LOGD. Finally, _Data quality_, characterized by accuracy, completeness, and consistency, is a fundamental aspect of the production and release of valid LOGD. Heterogeneity  
Heterogeneity is widespread in the realm of Open Government, exerting a significant influence on both the publication and consumption of OGD. The data are disseminated by disparate providers, often lacking coordination, resulting in a diversity of APIs and formats (referred to as _data source heterogeneity_). Furthermore, publishers exhibit heterogeneity in their use of vocabulary, combining and reusing distinct data models and terminologies (referred to as _vocabulary heterogeneity_). Additionally, they create and employ their own institutionally minted and managed identifiers, contributing to a further layer of heterogeneity in the form of unique identifiers (_identifier heterogeneity_) [2; 41; 74]. Adopting LD practices eases the data source heterogeneity and vocabulary heterogeneity. As elucidated by [PERSON] and [PERSON] [53], adhering to LD principles establishes a standard approach for accessing information through the dereferencing of HTTP URIs into RDF descriptions. Accessing such data is possible through various channels, including querying SPARQL endpoints or obtaining data in the form of RDF dumps. The utilization of RDF and HTTP dereferenceable Internationalized Resource Identifiers (IRIs) establishes a consistent method for expressing data references, facilitating the exploration of additional data sources through the traversal of RDF links, and ensures a seamless integration with the vocabularies that organize such data. Utilizing self-explanatory HTTP-dereferenceable IRIs for vocabulary terms fosters the integration and combination of domain-specific and cross-cutting third-party vocabularies. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Dimension** & **Description** & **Issues** \\ \hline \multirow{4}{*}{Technical} & deals with challenges like diverse data sources, vocabulary alignment for semantic interoperability, managing LD complexity, and ensuring data quality for LOGD production & _Heterogeneity_ [2; 33; 34; 41; 49; 61; 68; 69; 74; 75; 76; 77; 78; 79] \\  & emphasizes strategic data management in government structures, directing attention and enhancing the skills of public servants, fostering a culture that understands and effectively manages LOGD, aligning with broader public sector objectives & _Vocabularies_ [2; 33; 35; 41; 62; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79] \\  & deals with crucial aspects of data management and usage, requiring a thorough understanding of the legal and regulatory landscape & _Data ownership and licensing_ [73; 94; 75] \\  & discusses challenges related to stakeholder awareness and motivation, as well as public misconceptions about open data & _Data privacy and security_ [73; 78] \\ \hline \multirow{2}{*}{Scocal} & underlines the importance of financial commitment & _Sustainability_ [2; 43; 97] \\  & for valuable data release & _Maintenance_ [9; 33; 88] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data friction dimensions that impact on the creation of Linked Open Government Data. However, the _alignment of vocabularies_ remains imperative to deal with vocabulary heterogeneity when aggregating and independently provided data.