Finding the right information has long been a major challenge for companies. Overall, an information Worker spends an average of 26 per cent a day looking for information. That implies a lot of potential in optimizing the process of search. Search technologies aim to solve this problem. Unfortunately search results are mostly below expectations. The reason is not the search technology per se, but the quality of data to be searched. The following topics cause considerable problems:

- Knowledge is not explicitly available. Important information is not explicitly captured but available in various documents.

- Information is very rugged. Various documents with information are in physically different locations and in different structures. There are documents, blog and forum posts with unstructured content or applications and databases with structured information, which all contain relevant information about a single topic, but do not represent an overall picture.

- A large part of knowledge is only in minds. This knowledge, which constitutes a significant part, is not detectable by a search technology. But this knowledge is necessary to achieve the target of the most effective and efficient use of knowledge.

To address these issues two essential conditions must be created. First, the user must be empowered and encouraged to capture his knowledge.

Information describing If you managed to capture as much information as possible, you have to face further challenges. Information is often captured and stored inconsistently. No search technology can create an overall picture, when content is captured somehow and somewhere.This is where metadata comes into play. Metadata is structural information about particular content. Modern content management systems like SharePoint 2010 help to manage knowledge. By using metadata, articles and documents are easier to find and can be published group-specific. The problem is that metadata is tagged manually by the user.

As a result there is inconsistent metadata, for example different tags for the same subject or similar tags for different topics. The Term store helps to manage this problem by using defined standard metadata (Taxonomies). Taxonomies will restrict the choice of metadata to guarantee a more consistent tagging of content. By using metadata available knowledge is easier to find. Consistent tagging enables e.g. a refining of the search and document results.

Despite the advanced onboard-tools, major problems remain in SharePoint 2010. The reason is that content is tagged manually, which leads to inconsistent or heterogeneous data or will not even be used. A completely blank form with a request to enter metadata often causes no or not well thought-out metadata to be entered. Furthermore the term store does not solve the problem of inhomogeneous metadata. Especially increasing amounts of information are making it difficult to find the right metadata, because it increases the probability that content will not completely be tagged and that it increases the costs of searching for certain information.

This is exactly the point where the automatic and semi-automatic tagging of content comes into play. beyCoo for SharePoint enhances the capabilities of SharePoint 2010 by using an automatic enrichment of suitable metadata (For more information please visit www.beyCoo.com). After importing content, beyCoo automatically extracts relevant information and generates suitable metadata that is tailored to individual needs of the enterprise.

Content Enrichement with beyCoo for SharePoint
beyCoo for SharePoint is a web service that structures unstructured content such as texts, web pages, office documents or PDF`s by enriching the original content with additional information. It extracts a set of standard terms like persons, locations, companies or communication data. Furthermore beyCoo can be customized by extracting individual or corporate terms. beyCoo is called up via a Web Service and via an existing Plug-in within SharePoint 2010 (beyCoo for SharePoint). By using a transaction-based payment model, beyCoo facilitates the semantic content expansion easy and economical.

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