## 1 Introduction  
In this paper, we propose a tool for exploring knowledge, leveraging semantic technologies for Knowledge Extraction, management, and interactive visualisation. The rapid growth of information and the increasing complexity of data have highlighted the need for advanced tools that empower users to gain valuable insights and discover unexpected relationships within their domain of interest.  
Graphs have emerged as a powerful means of representing information when the goal is to derive knowledge and uncover hidden patterns. Unlike traditional tabular data structures, graphs allow us to capture complex relationships between entities, making them an ideal choice for knowledge representation and discovery. Through graph-based approaches, users can navigate and explore interconnected data, unlocking valuable knowledge that might remain hidden in other representations.  
Artificial Intelligence (AI) has significantly transformed research methodologies, enabling machines to process vast amounts of data, interpret them, and derive valuable insights. Notably, models like OpenAI's Generative Pre-trained Transformer (GPT) have demonstrated the capability to generate coherent text, extracting meaningful information from raw data. However, it is essential to acknowledge that these capabilities are based on learned patterns and associations rather than transparent semantic reasoning. This highlights the critical need for transparent systems that empower users with greater control and understanding of the underlying processes.  
In the realm of AI and semantic technologies, there is an evident gap when it comes to tools for transparent Knowledge Extraction and exploratory visualisation. Many existing AI-based solutions generate outputs that are not easily comprehensible or controllable by end-users, leading to concerns regarding trust and accountability.  
To address this pressing issue, we present a novel framework and tool called Semantic Knowledge Advanced Tool for Extraction Browsing Organisation Annotation Retrieval and Discovery (SKATEBOARD). SKATEBOARD is designed to provide users with complete control over all stages of information extraction and manipulation, offering a transparent approach to knowledge exploration and management.  
SKATEBOARD facilitates a multi-faceted approach, encompassing the extraction of relevant information, the creation of domain-specific ontologies based on the extracted data, efficient ontology management, and a powerful platform for interactive exploration. By adopting Linked Data paradigms, such as graph-based exploration, the tool offers users unparalleled transparency, allowing them to interactively navigate through information with full visibility into relationships and dependencies. Additionally, SKATEBOARD opens the door to recommendation systems and reasoning capabilities, unlocking the potential for serendipitous discoveries and novel insights.  
In the following sections, we will delve into the details of SKATEBOARD's functionalities, showcasing how this tool empowers users to harness the power of semantic technologies to unlock the full potential of their data. Through a combination of intelligent Knowledge Extraction, ontology building, and interactive exploration, SKATEBOARD represents a promising step forward in the realm of knowledge discovery and management. Its user-centric design ensures that researchers and practitioners can confidently navigate through the complexities of their domain, making informed decisions and uncovering knowledge that goes beyond their initial expectations.  
The rest of the paper is organised as follows. After discussing related work in the next session, we describe in detail the SKATEBOARD pipeline in Section 3 and how it connects RDF and LPG representations in Section 4. Sections 5 and 6 discuss the Knowledge-Extraction and -exploration functions of SKATEBOARD, respectively, while Section 7 evaluates it. Finally, Section 8 concludes the work.