A Survey on Facilitating Document Annotation Using Content and Querying Value

Now-a-days many organizations generate and share descriptive and textual data of their products, services, and actions. Such type of collections of textual data contain significant amount of structured information, which remains saved in the unstructured text. While information extraction algorithms facilitate the extraction of structured relations in an very expensive and inaccurate way especially when operating on top of text that does not contain any instances of the targeted structured information. There are many alternative approaches that facilitate the generation of the structured metadata by identifying documents that are likely to contain information of interest and this information is going to be subsequently useful for querying the database which relies on the idea that humans are more likely to add the necessary metadata during creation time. This can be done like prompting by the interface, or that it should made much easier for humans (and/or algorithms) to identify the metadata when such information actually exists in the document, instead of naively prompting users to fill in forms with information that is not available in the document. There are different algorithms that identify structured attributes that are likely to appear within the document, by jointly utilizing the content of the text and the query workload.