A Review of Semantic Annotation in the Context of the Linked Open Data Cloud

Abstract

Semantic annotation, a pivotal technology facilitating comprehension within textual data, serves as the process of appending supplementary information or metadata to text, thereby augmenting its meaning. Given the inherent ambiguity of natural language, which renders the data susceptible to multiple interpretations, the task of discerning the intended meaning from raw data proves significantly more challenging than interpreting structured text. This ambiguity necessitates mechanisms to render text comprehensible to both machines and humans, thereby enabling the efficient extraction and innovation of various subjects. In response to these challenges, considerable research efforts have been dedicated to advancing the methodologies of text annotation. This review explores the role of semantic annotation in addressing contextual ambiguity, underspecified semantic representations, formal semantics, and the resolution of semantic ambiguities. By integrating additional data into the text, semantic annotation establishes a synergy with the Linked Open Data (LOD) framework, thereby providing context and enhancing machine readability. LOD, a practice of publishing structured data on the web to facilitate interlinking and utility, benefits from semantic annotation as it improves data publishing, linking, and enrichment processes. This enhancement directly contributes to the precision of web search results. The literature on semantic annotation, encompassing tools, methods, and techniques, as well as its relationship with LOD, is meticulously reviewed. This paper employs a systematic approach to select pertinent articles, highlighting state-of-the-art methods in semantic annotation, including deep learning and ontology-based techniques. The exploration aims to delineate the evolution of semantic annotation practices and their consequential impact on the LOD ecosystem, underscoring the mutual enrichment of both fields.