## 2 Related Work  
In this section, we will delve into tools that share common characteristics with SKATEBOARD, particularly focusing on Knowledge Extraction, information retrieval, semantic data visualisation, and the broader utilisation of semantic technologies. We will analyse these tools, comparing their functionalities and approaches, while also underlining distinctions and parallels with the SKATEBOARD system. In the realm of Linked Data interfaces, many systems focus on the visualisation of SPARQL endpoints and, thus, Linked Data [1,2]. SPARQL endpoints are web services that provide an interface for querying and retrieving data from semantic datasets using the SPARQL query language. They are critical access points for obtaining information from Linked Data sources through SPARQL queries. However, SKATEBOARD goes beyond this scope, thanks to its integrated API as described in Section 4 of our work. This API allows SKATEBOARD to connect to one or more SPARQL endpoints containing Linked Data or Labelled Property Graphs (LPGs). This endows SKATEBOARD with the versatility to not only visualise Linked Data in existing endpoints, but also contribute to the creation of Linked Data, addressing the challenges associated with extracting semantic knowledge from unstructured texts and semantic annotation. Furthermore, with its integration with the GraphBRAIN system [3,4]for ontology creation and management, SKATEBOARD provides a comprehensive solution for the lifecycle of Linked Data. The GraphBRAIN system provides a dedicated API that enforces ontology compliance for all interactions with the Knowledge Graph (KG). This API offers a wide range of functionalities for KG management, classified into basic and advanced operations. The basic functionality includes standard Create, Read, Update, Delete (CRUD) operations, enabling the management of entity instances and relationship instances in the KG. For querying, the API seamlessly interfaces with the Neo4j query language Cypher, ensuring that all queries are compliant with the ontology's constraints. The advanced functionality of the GraphBRAIN API encompasses various analysis, mining, and reasoning functions. These include tasks such as computing centrality measures for entity instances based on various algorithms, extracting relevant subgraphs starting from specified nodes (possibly considering user profiles for personalised results), finding all possible paths in the KG between given pairs of nodes, checking the consistency of the available knowledge, and deducing or adducingknowledge that may not be explicitly present in the KG. Notably, the GraphBRAIN API is designed to be accessible to third-party applications, allowing developers to incorporate its advanced KG management capabilities into their own systems. This integration plays a central role in SKATEBOARD's value proposition, delivering a comprehensive solution for overseeing the entire lifecycle of Linked Data, from ontology development to advanced query and analytical capabilities. It offers adaptability, expandability, and the capability to import or export knowledge to and from other formats, including the Semantic Web standard OWL. It is important to highlight that, while there are numerous studies examining systems for managing and visualising Linked Data, only a handful of them simultaneously tackle the creation aspect of Linked Data. This facet, seen as both the extraction of semantic knowledge from unstructured texts and semantic annotation, represents one of the distinctive strengths of SKATEBOARD. As highlighted in the work by [PERSON] et al. [2], Linked Data interfaces can be categorised into five key macro-characteristics:  
1. **Knowledge Extraction**: This category pertains to the use of tools designed to extract knowledge from unstructured data, enabling the analysis of texts or unstructured data to transform them into a structured and semantic form. 2.