The challenges for today's enterprise information integration systems are well understood. In order to manage and use information effectively within the enterprise, three barriers that increase the complexity of managing information have to be overcome: the diverse formats of content, the disparate nature of content and the need to derive "intelligence" from this content. Current software tools that look at structuring content by leveraging syntactic search and even syntactic meta data are not sufficient to handle these problems. What is needed is actionable information from disparate sources that reveals non-obvious insights and allows timely decisions to be made. A new concept known as semantic meta data is paving the way to finally realize the full value of information. Indeed, Tim Berners-Lee's vision for the next generation of the Web is termed the "semantic Web," where semantic meta data plays the pivotal role. By annotating or enhancing documents with semantic meta data, software programs can automatically understand the full context and meaning of each document and can make correct decisions about who can use the documents and how these documents should be used. This article looks at how semantic meta data is created and used within the enterprise.

Meta data that describes contextually relevant or domain-specific information about content (in the right context) based on an industry-specific or enterprise- specific custom meta data model or ontology is known as semantic meta data. For example, if the content is from the business domain, the relevant semantic meta data could be company name, ticker symbol, industry, sector, executives, etc., whereas if the content is from the intelligence domain, the relevant semantic meta data could be terrorist name, event, location, organization, etc. Meta data elements that offer greater depth and more insight "about the document" fall under the semantic meta data category.

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