A concept search (or conceptual search) is an automated information retrieval method that is used to search electronically stored unstructured text (for example, digital archives, email, scientific literature, etc.) for information that is conceptually similar to the information provided in a search query. In other words, the ideas expressed in the information retrieved in response to a
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Concept search techniques were developed because of limitations imposed by classical Boolean keyword search technologies when dealing with large, unstructured digital collections of text. Keyword searches often return results that include many non-relevant items (false positives) or that exclude too many relevant items (false negatives) because of the effects of synonymy and polysemy. Synonymy
In general, information retrieval research and technology can be divided into two broad categories: semantic and statistical. Information retrieval systems that fall into the semantic category will attempt to implement some degree of syntactic and semantic analysis of the natural language text that a human user would provide (also see computational linguistics). Systems that fall into the
eDiscovery - Concept-based search technologies are increasingly being used for Electronic Document Discovery (EDD or eDiscovery) to help enterprises prepare for litigation. In eDiscovery, the ability to cluster, categorize, and search large collections of unstructured text on a conceptual basis is much more efficient than traditional linear review techniques. Concept-based searching is
The effectiveness of a concept search can depend on a variety of elements including the dataset being searched and the search engine that is used to process queries and display results. However, most concept search engines work best for certain kinds of queries: Effective queries are composed of enough text to adequately convey the intended concepts. Effective queries may include full sentences,
