SKATEBOARD: Semantic Knowledge Advanced Tool for Extraction, Browsing, Organisation, Annotation, Retrieval, and Discovery

Abstract

This paper introduces Semantic Knowledge Advanced Tool for Extraction Browsing Organisation Annotation Retrieval and Discovery (SKATEBOARD), a tool designed to facilitate knowledge exploration through the application of semantic technologies. The demand for advanced solutions that streamline Knowledge Extraction, management, and visualisation, characterised by abundant information, has grown substantially in the current era. Graph-based representations have emerged as a robust approach for uncovering intricate data relationships, complementing the capabilities offered by AI models. Acknowledging the transparency and user control challenges faced by AI-driven solutions, SKATEBOARD offers a comprehensive framework encompassing Knowledge Extraction, ontology development, management, and interactive exploration. By adhering to Linked Data principles and adopting graph-based exploration, SKATEBOARD provides users with a clear view of data relationships and dependencies. Furthermore, it integrates recommendation systems and reasoning capabilities to augment the knowledge discovery process, thus introducing a serendipity effect generated by the SKATEBOARD interface exploration. This paper elucidates SKATEBOARD’s functionalities while emphasising its user-centric design. After reviewing related research, we provide an overview of the SKATEBOARD pipeline, demonstrating its capacity to bridge RDF and LPG representations. Subsequent sections delve into Knowledge Extraction and exploration, culminating in the evaluation of the tool. SKATEBOARD empowers users to make informed decisions and uncover valuable insights within their data domains, with the added dimension of serendipitous discoveries facilitated by its interface exploration capabilities.

Unlocking Semantic Dimensions: Harnessing AI for Next-Gen Natural Language Understanding

“…In the context of medical AI applications, the investigation into semantic knowledge graphs assumes a pivotal role. Particularly prevalent in cancer research, these knowledge graphs aspire to elevate our comprehension of tumor evolution and prognosticate disease survival [1]. This approach involves the meticulous structuring of knowledge through semantic relationships, providing a tangible application of AI in intricate domains.…”

“…The overarching goal is to cultivate semantic coherence across varied data types, fostering a comprehensive approach to comprehension and interaction between AI systems and multimodal content. This evolution signifies the expansive applicability of semantic understanding, transcending boundaries and finding relevance in diverse domains beyond the medical sector [6]. Concurrently, addressing the intricacies of natural language processing assumes paramount importance, particularly in the exploration of unattested input processing.…”