Enterprise search systems index data and documents from a variety of sources such as: file systems, intranets, document management systems, e-mail, and databases.
Many enterprise search systems integrate structured and unstructured data in their collections. Enterprise search systems also use access controls to enforce a security policy on their users.
In an enterprise search system, content goes through various phases from source repository to search results:
Content awareness (or “content collection”) is usually either a push or pull model.
In the push model, a source system is integrated with the search engine in such a way that it connects to it and pushes new content directly to its APIs. This model is used when real-time indexing is important.
In the pull model, the software gathers content from sources using a connector such as a web crawler or a database connector. The connector typically polls the source with certain intervals to look for new, updated or deleted content.
Content from different sources may have many different formats or document types, such as XML, HTML, Office document formats or plain text. The content processing phase processes the incoming documents to plain text using document filters.
It is also often necessary to normalize content in various ways to improve recall or precision. These may include stemming, lemmatization, synonym expansion, entity extraction, part of speech tagging.
As part of processing and analysis, tokenization is applied to split the content into tokens which is the basic matching unit. It is also common to normalize tokens to lower case to provide case-insensitive search, as well as to normalize accents to provide better recall.
The resulting text is stored in an index, which is optimized for quick lookups without storing the full text of the document. The index may contain the dictionary of all unique words in the corpus as well as information about ranking and term frequency.
Using a web page, the user issues a query to the system. The query consists of any terms the user enters as well as navigational actions such as faceting and paging information.
The processed query is then compared to the stored index, and the search system returns results (or “hits”) referencing source documents that match. Some systems are able to present the document as it was indexed.
Beyond the difference in the kinds of materials being indexed, enterprise search systems also typically include functionality that is not associated with the mainstream web search engines. These include:
Adapters to index content from a variety of repositories, such as databases and content management systems.
Federated search, which consists of:
Enterprise bookmarking, collaborative tagging systems for capturing knowledge about structured and semi-structured enterprise data.
Entity extraction that seeks to locate and classify elements in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.
Faceted search, a technique for accessing a collection of information represented using a faceted classification, allowing users to explore by filtering available information.
Access control, usually in the form of an Access control list (ACL), is often required to restrict access to documents based on individual user identities.
There are many types of access control mechanisms for different content sources making this a complex task to address comprehensively in an enterprise search environment.
Text clustering, which groups the top several hundred search results into topics that are computed on the fly from the search-results descriptions, typically titles, excerpts (snippets), and meta-data.
This technique lets users navigate the content by topic rather than by the meta-data that is used in faceting. Clustering compensates for the problem of incompatible meta-data across multiple enterprise repositories, which hinders the usefulness of faceting.
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