Many people use a variety of different computer-based information sources such as search engines (e.g., Google™, MSN®, Yahoo®, etc.) to find information they are seeking. Typically, users are looking for information relevant to a work task in which they are currently engaged. For example, a user may be interested in information related to a topic already displayed be a web browser, or a user may be interested in information related to a word processing document they are currently working on (e.g., a word processing document). Typically, the user enters a query into an input box, and the search engine examines data associated with thousands of documents. The search engine then sends the user list of search results. In an effort to help users find relevant information quickly, most information sources rank search results for presentation to the user, thereby reducing the user's need to wade through a long list of search results. For example, documents that a search engine determines to be most relevant to the user's query are typically placed first in a list of search results.
Typically, search engines use some form of term frequency—inverse document frequency (TF/IDF) ranking algorithm or some similar method to determine this presentation order or other organization scheme. TF/IDF scores documents in direct proportion to the number of query terms present in the document and in inverse proportion to some function of the number of times the query terms appear in the information repository as a whole. In other words, documents with many occurrences of rare query terms are ranked highly. In addition, other factors may be used to rank the documents, such as the number of times other documents reference that document. Search engines might also display the documents retrieved based on data associated with the retrieved documents. For example, documents labeled with the same subject area might be presented in the same folder.
One problem with this method of ranking, organizing and presenting retrieved documents when seeking information related to a user's current work context is that the query terms alone are used to assess the relevance of the search results in the course of retrieval. However, most search engines place limitations on the length of the query and/or limitations on other aspects of the manner in which the search may be specified (e.g., the types of constraints that may be specified on desired results). For example, a search engine may limit the number of terms in a query to five, or the search engine may not contain a method for specifying a date range. In general, however, the user's current context is typically too complex to be represented in such a compressed and simplified form. For example, if the document the user is currently working on—an important aspect of the user's context—has more than five relevant terms, but a search engine only accepts queries that are five words long, the query alone is not necessarily the best representation of the user's current work context with which to assess relevance, since the user's current document (e.g., web page or word processing document) contains information beneficial to assessing the relevance of a search result that is not easily communicated to the search engine in the form of a query. Other properties of the user's current work context, for example, their task (e.g., drafting a legal document), stage in that task, their role in an organization (e.g., lawyer), the nature of that organization (e.g., a law firm), specified areas of interest (e.g., patents), the application in which they are working (e.g., a word processor), the document genre or type (e.g., legal brief, or resume) or their past behavior, might also be important aspects of assessing the relevance of a given search result. Therefore, assessing, ranking, organizing, and presenting search results associated with the user's context simply using a query acceptable to a given search engine may not produce the best results.
Moreover, as described above, the user's current document by itself typically does not constitute the entire user context in terms of which relevance of information should be assessed. Other factors, including, but not limited to, the user's task, the state of that task, the organization for which the work is being performed, the user's role in that organization, explicit user indications, the application in which the user is working on the document, the document genre, etc., may also important in determining a ranking, organization, and presentation of search results that truly reflects the user's information needs.
Consider, for example, the task of writing a scientific research paper. Presentations to others may be given before the work is more broadly published. Therefore, at the beginning of the writing task, it may be useful to assemble information by the author that very closely matches the first drafts of the paper, so that those prior writings may be reused. Later in the process, when the author is assembling related work, it may be desirable to relax those constraints so as to provide a broader, more complete set of search results. In this example, the stage and type of task influence the character of the search results desired. However it may not be possible to specify this directly to a typical search engine.
In addition, the best strategy for presenting information should be determined. For example, while composing an electronic mail message, prior messages sent to and/or received from the recipients of the current message may be retrieved. These messages may be presented next to the email editor window organized in headers labeled by the name of the recipient. Messages in each header may also be organized in a ranked list, where items on the top of the list are ordered from most to least similar to the contents of the body of the message being composed. The system may also draw icons next to each email recipient indicating the presence of the additional information. When the user moves his/her mouse over those icons, the system may present the best matching email, so as to give the user a preview of the available information. In contrast, while shopping online and viewing a product, information might be displayed in a window next to the user's web browser, organized in categories. Reviews of that product may be organized in one category, accessories in another category, and prices under yet another category. An improved search system should be able to determine how to present information to the user using a strategy that works better for the work context in which the user is currently engaged.
Another problem with relying solely on the rankings or organization schemes provided by search engines themselves occurs when querying multiple information sources. Different information sources typically do not use the same scoring algorithm in determining what to return and what order to return it in or in determining how to organize and present these results. As a result, ranking and/or organizing scores associated with results from different search engines (if returned to the requester of the search at all) typically cannot reliably be used to combine multiple result lists into combined results lists. This is typically acceptable only if information from different information sources is presented under different headings (e.g., one heading for each information source). If, however, headings are defined functionally or by content rather than just by information source, then a common assessment, ranking, organization, and presentation system may be needed in order to determine which results would be most useful to the user, which results should be presented to the user, and how the results should be organized and presented to the user (e.g., in what order). Similarly, if a unified view of information from a variety of information sources is desired, a common assessment, ranking, organization, and presentation system may be needed.