# 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
As highlighted by [PERSON] [75], the diversity of terms employed across public sector bodies has already led to confusion during data searches and interconnections. Distinct vocabularies depict data at varying levels of detail, covering overlapping yet not identical facets of the domain, bringing to a _granularity mismatch_. This affects the alignment and mapping among both controlled vocabularies and data models. _Multilinguality_ further exacerbates vocabulary heterogeneity, especially concerning controlled vocabularies. The demand for automated cross-language semantic matching methods has been underscored by [PERSON], emphasizing their role in supporting local and national administrations in linking their service catalogs [79]. Consequently, achieving vocabulary alignment can be a challenging task, as noted by [PERSON] et al. [33], [PERSON] et al. [49], and [PERSON] et al. [77]. Another significant hurdle in handling OGD arises from the need to effectively manage, connect, and comprehend the relationships between references to entities across various datasets [77]. _Entity consolidation and interlinking_ are crucial but complex activities required to address identifier heterogeneity, enhancing the coherence and accessibility of information within diverse datasets. In particular, [PERSON] et al. [68] address the organization's consolidation as a pivotal case of consolidation in the context of LOGD, also highlighting multilingual issues. After the interlinks are established, they may not align with the expectations implied by the joint utilization of the interlinked datasets. The _quality of the links_ affects the usability of the aggregated data obtainable by exploiting the interlinking [69] and, as it has also been noticed in a more general application of LD technologies, it might seriously affect the suitability of aggregated data for specific downstream applications [99; 100]. Vocabularies  
Several hurdles are related to the development, reuse, and maintenance of vocabularies [101; 102]. OGD covers a wide set of specialized domains and requires specialized and cross-cutting vocabularies. The variety of covered domains implies that _\"No One Vocabulary Fits All\"_: a single vocabulary cannot accommodate all datasets [81]. Consequently, combining different ontologies and vocabularies becomes necessary for comprehensive data representation [76]. When well-established shared vocabularies are unavailable, _creating new vocabularies is a challenge_[80]. The creation of ontologies is a time-consuming process that demands technical and domain expertise [83]. The _absence of widely adopted shared vocabularies_ further complicates data interoperability [82]. Establishing standardization in common metadata and thesauri is imperative for seamless interoperability [2; 84]. In addressing these challenges, there is a recognized need for initiatives that focus on developing standardized models, as outlined in [33]. This signifies the importance of collaborative efforts and standardized approaches to overcome the obstacles associated with transforming, selecting, and creating vocabularies in the context of data interoperability. Even in cases where vocabularies for a specific dataset are available, challenges arise in _vocabulary reuse_. Reusing third-party vocabularies adds an additional layer of complexity to the transformation process, as emphasized by [PERSON] et al. [80]. Selecting appropriate vocabularies to represent diverse datasets accurately is a non-trivial endeavor [41; 61]. Despite the existence of some well-established vocabularies, their limited reuse is a notable issue [62]. On the other hand, distributing the effort among publishers requires clear guidelines for vocabulary choice and use, leaving room for interpretation [62]. LD Lifecycle Complexity  
The development and dissemination of LD involve a multifaceted and time-consuming lifecycle [103], demanding concerted efforts in both creating and integrating semantic assets [74; 80].