## 2 Related Work
### Knowledge Extraction  
Here, we will discuss the transformation of unstructured or semi-structured text into structured knowledge representations, focusing on advanced knowledge representation techniques. Knowledge Extraction is pivotal in managing semantic data, encompassing information extraction from unstructured sources and semantic enrichment to refine raw data into structured formats. Various tools have emerged to address Knowledge Extraction,utilising natural language processing, machine learning, and knowledge representation to create coherent and machine-interpretable knowledge representations. The significance of Knowledge Extraction is evident in initiatives such as the standardisation of RDF extraction from relational databases and projects like the conversion of Wikipedia into structured data, exemplified by DBpedia [5] and Freebase [6]. In the following sections, we examine various tools and techniques for Knowledge Extraction and semantic enrichment within the context of Linked Data interfaces. **AIDA**[7] stands out as a versatile framework and online tool designed for Named Entity Recognition (NER) and resolution. Its remarkable capability lies in connecting ambiguous references to precise canonical entities within the YAGO2 knowledge base [8]. AIDA also boasts the added feature of sense tagging and provides a wide array of customizable options. **Apache Stanbol**[9] is an open-source HTTP service meticulously crafted to elevate unstructured content by infusing it with semantic annotations. It excels in generating RDF-encoded outcomes through multilingual NER and resolution. Stanbol shines in sense tagging linked to renowned knowledge bases like DBpedia and GeoNames. Moreover, it offers text span grounding, confidence assessment, and seamless support for associated imagery. **DBpedia Spotlight**[10] is a specialised tool with an automatic knack for pinpointing and annotating references to DBpedia resources nestled within textual material. **Open Calais**[11] is a Knowledge Extraction powerhouse, renowned for its proficiency in extracting named entities and embellishing them with sense labels, factual information, and event details. It is conveniently accessible both as a web application and a web service. **Semiosearch Wikifier**[12] is a system that deftly navigates the terrain of named entity resolution, expertly matching named entities or terms to their corresponding DBpedia counterparts. This feat is achieved by employing a blend of named-entity-recognition techniques and heuristic strategies. **The GLOBDEF system**[13] showcases its adaptability with the incorporation of pluggable enhancement modules. These modules can be dynamically activated, giving rise to flexible data-enhancement pipelines. Their primary mission is to enhance data quality and structure, contributing significantly to improved data utilisation. These tools signify substantial progress in the field of Knowledge Extraction, effectively bridging the gap between unstructured text and structured knowledge representations. Notable trends include advancements in Named Entity Recognition and Linking (NERL), semantic enrichment, and support for multiple languages. In comparison, SKATEBOARD offers a holistic Knowledge Extraction solution. It excels in transforming unstructured text into structured data, identifying entities through NER, and linking these entities to existing knowledge bases like DBpedia (NEL). SKATEBOARD further enhances the extracted data through semantic annotation and ontology-based integration via its integration with the GraphBRAIN system. While the cited tools excel in specific aspects of Knowledge Extraction, SKATEBOARD provides a comprehensive solution that encompasses the entire Knowledge Extraction pipeline, making it a powerful choice for Linked Data management. Moreover, SKATEBOARD emphasises maintenance and updates to ensure long-term usability and relevance, addressing the challenges faced by some of the other tools mentioned.