# A Graph DB-Based Solution for Semantic Technologies in the Future Internet
## 2 Related Work
### Linked Data Interfaces  
Here, we explore the tools available for knowledge extraction, information retrieval, and semantic data visualization and the use of semantic technologies at large to draw a comparison of the state-of-the-art tools with the SKATEBOARD interface for Linked Data exploration of the GraphBRAIN platform. A concise overview of the main distinctions between the GraphBRAIN framework and the analyzed works is provided in Table 1.  
The focus of knowledge extraction is on using advanced techniques to transform unstructured text into meticulously structured knowledge representations. The significance of knowledge extraction in the proficient management of semantic data is unmistakable as it encompasses both information extraction and semantic enrichment. To achieve this, various tools harness the capabilities of natural language processing, machine learning, and knowledge representation. Among the prominent contenders in this field are AIDA [16], Apache Stanbol [17], DBpedia Spotlight [18], Open Calais [19], Semiosearch Wikifier [20], and the GLOBDEF system [21]. Each excels in specific facets of knowledge extraction. However, SKATEBOARD distinguishes itself by offering a comprehensive solution. It adeptly transforms unstructured text into structured data, identifies entities through Named Entity Recognition (NER), establishes connections with Knowledge Bases like DBpedia [24], and enhances data through semantic annotation. SKATEBOARD has emerged as a pragmatic choice for managing Linked Data, providing an inclusive solution that spans the entire knowledge extraction pipeline. In the arena of traditional visual information-seeking tools, venerable names such as L'ERMA [22] and TORROSA [23] have long played pivotal roles in visual data retrieval. However, the landscape underwent a remarkable transformation with the introduction of SKATEBOARD, empowered by cutting-edge semantic technology. SKATEBOARD transcends traditional paradigms by enabling semantic faceted browsing, fostering serendipitous discovery, and providing intelligent recommendations. These capabilities significantly enhance the user experience, redefining the search boundaries. The domain of visualizing semantic data presents distinct challenges due to its diverse and dynamic nature. Prior research has diligently categorized visualization tools based on interaction paradigms and the types of information they represent. Interaction paradigms encompass a range of options: \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Related Work** & **Key Features** & **GraphBRAIN** \\ \hline \multirow{2}{*}{AIDA [16]} & Specific focus on Named Entity Recognition (NER) and semantic annotation & Broader range of knowledge extraction and visualization capabilities \\ \hline \multirow{2}{*}{Apache Stanbol [17]} & Knowledge extraction and semantic enrichment & Extended capabilities for semantic data retrieval and exploration \\ \hline \multirow{2}{*}{DBpedia Spotlight [18]} & Entity recognition and linking to DBpedia & More comprehensive knowledge extraction and visualization \\ \hline \multirow{2}{*}{Open Calais [19]} & Text analytics and entity recognition & Advanced semantic browsing and visualization features \\ \hline \multirow{2}{*}{Semiosearch Wikifier [20]} & Entity recognition and semantic annotation & Robust semantic data exploration tools \\ \hline \multirow{2}{*}{GLOBDEF [21]} & Comprehensive knowledge extraction and enrichment & Broader range of visualizations and semantic browsing \\ \hline \multirow{2}{*}{L'ERMA [22]} & Traditional visual data retrieval tools & Enhanced semantic faceted browsing and tailored visualizations \\ \hline \multirow{2}{*}{[PERSON] [23]} & Traditional visual data retrieval tools & Innovative semantic exploration capabilities \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages of GraphBRAIN compared to other frameworks/initiatives.