One of the many functions of metadata is to facilitate and to bridge semantic gap. Semantic gap portrays the variations of two definitions of an object that might be categorized differently through computational representation. The formal languages like what is used in programming language reproduce various computational representations. Metadata should be based on properly represented information and should be contextually-relevant as well. The information, that is industry-specific, uses metadata model that has the capacity to serve its purpose.

To make the organization of information more efficient and the computational representations more accurate within a certain enterprise, the complexities of information management should be overpowered. Such complexities include: various formats of the content, the disparities of content nature, and the need to acquire accuracy and intelligence from the content.

Semantic metadata enables to reach the semantic gap so that the complete worth of the information will be obtained. So if the content of the information is about business domain, it is expected that relevant semantic metadata like the company name, sector, ticker symbol, executives and other business content should be found. Every element that can contribute an in-depth insight about a certain document should fall into a certain semantic metadata category.

The precise and proper creation of semantic metadata is required so that when extraction of information is done, relevant information will be tagged. The techniques that can be used in engaging in this kind of endeavor are as follows:
1. Dictionary and Thesauri
2. Ontologies
3. Document Analysis

Among the three, ontology-driven metadata is the most flexible, comprehensive and accommodating since structuring of domain-specific relationships among various entities is the focal point of semantic representations.

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