Abstract:
Some information retrieval systems, such as the Westlaw™ system, empower users to search thousands of databases. One problem with some systems is that the large number of databases makes it difficult for some users to identify the right database(s) to search. Accordingly, the inventors devised, among other things, new options for helping users target searches on the right databases. One exemplary option enables users to search sample-based database profiles. Another enables users to navigate to a category or subcategory in an online database directory and search databases or database profiles associated with the category. And, yet another enables users to select a query-category and receive a list of relevant database choices, a list of links into a database directory, or a sequence of follow-up questions. One or more of these or other disclosed innovations enable users to execute more effective search strategies, while potentially reducing computational burdens on information retrieval systems.

Description:
RELATED APPLICATIONS  
       [0001]     This application claims priority to U.S. Provisional Application 60/436,191, which was filed on Dec. 23, 2002, and to U.S. patent application Ser. Nos. 10/745,966 and 10/754,083, which were filed on Dec. 23, 2003. These three applications are incorporated herein by reference.  
       COPYRIGHT NOTICE AND PERMISSION  
       [0002]     A portion of this patent document contains material subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyrights whatsoever. The following notice applies to this document: Copyright© 2002, Thomson Legal and Regulatory, Inc. 
     
    
     TECHNICAL FIELD  
       [0003]     The present invention concerns information retrieval systems, such as those that include collections of databases and provide legal documents or other related content.  
       BACKGROUND  
       [0004]     The American legal system relies heavily on both written laws and judicial opinions that apply these laws to particular legal disputes. Because legislatures change laws and new judicial opinions affect how laws are interpreted and applied, judges and lawyers within our legal system must continually research these laws and opinions to effectively advise their clients.  
         [0005]     To facilitate this research, Thomson Legal &amp; Regulatory, Inc. of St. Paul, Minn. offers a system of databases and research tools, known as Westlaw®, which provides online access to judicial opinions, statutes, administrative regulations, legal articles, and news from all over the world. The Westlaw system empowers users to search over 100 million documents organized in over 15,000 databases.  
         [0006]     One problem recognized by the present inventors is that the vast number of databases in the Westlaw system can be difficult for some users to access effectively. For example, users unfamiliar with the array of database options may run “good” searches against a “bad” set of databases or against an overly broad set of databases. Selection of a bad set of databases will yield very few, if any, relevant documents. And, those documents that are found are likely to be of little value to the user, potentially leading him to incorrectly conclude that the system lacks what he needs. Likewise, selection of an overly broad set of databases can also lead to poor outcomes, since the search results will likely leave the user the task of sifting through a multitude of lesser relevant documents to find the few highly desirable ones. Overly broad searches, moreover, can be computationally and financially expensive to systems, such as Westlaw, that have finite computing resources to serve the thousands of researchers they attract daily.  
         [0007]     To address this problem, the Westlaw system includes two separate features: a find-a-database assistant (or wizard), and a hierarchical, online directory (or outline) of its 15,000-plus databases. The find-a-database wizard, which replaced a searchable listing of crude database profiles known as IDEN (short for identify), allows users to run a query against a set of database profiles and then to identify one or more of the found databases for use in a subsequent search. The separate online directory helps users find specific databases by presenting database titles and descriptions as an interlinked hierarchy of categories, subcategories, sub-subcategories, and so forth. The user can browse or navigate through the hierarchy by pointing to and clicking on specific categories and subcategories to reveal underlying subcategories and any associated database listings. The user can in turn review these listings and identify one or more databases for a subsequent search.  
         [0008]     Unfortunately, neither the wizard nor the directory has proven as effective as desired in helping users target the right databases. The wizard uses conventional natural language search technology, and often lists many more databases than are necessary to meet the actual needs of its users. The size and complexity of the directory make it cumbersome and time-consuming to browse. Furthermore, because systems, such as Westlaw, are expanding daily with the addition of new databases, the efficacies of the current wizard and directory are diminishing as the problem of targeting the right databases is worsening.  
         [0009]     Accordingly, the present inventors have recognized a need to improve how users search for documents in legal-research systems, such as Westlaw, as well as other information retrieval systems that employ large collections of databases.  
       SUMMARY  
       [0010]     To address this need, the present inventors devised systems, methods, and software that present users with multiple options for effectively and efficiently targeting their searches on an appropriate set of databases. One exemplary system presents online users with multiple forms of user-selectable search assistance. A first form, referred to as database discovery, provides a set of database profiles based on data-sampling techniques, and enables users to run searches against these profiles to identify relevant databases. A second form, referred to as database navigation, is a hierarchical database directory that allows users to navigate to any desired category or subcategory and search all the databases or database profiles underneath the category. A third form, referred to as database selection, allows a user to categorize a query and presents, based on the category, a set of database choices, a set of links to specific portions of a database directory, or a set of decisions to further delineate needs.  
         [0011]     One or more of these and/or other forms of search assistance presented by the inventors ultimately enable users to develop and execute more effective search strategies, while potentially reducing computational burdens on online search systems, and most importantly increasing user satisfaction. 
     
    
     BRIEF DESCRIPTION OF DRAWINGS  
       [0012]      FIG. 1  is a diagram of an exemplary information retrieval system  100  corresponding to one or more embodiments of the invention;  
         [0013]      FIG. 2  is a facsimile of an exemplary database profile  200  used in system  100 .  
         [0014]      FIG. 3  is a block diagram of an exemplary database directory used in system  100 .  
         [0015]      FIG. 4  is a flowchart corresponding to one or more exemplary methods of operating a database-discovery module within system  100 ;  
         [0016]      FIG. 5  is a flowchart corresponding to one or more exemplary methods of operating a database-navigation module within system  100 ;  
         [0017]      FIG. 6  is a flowchart corresponding to one or more exemplary methods of operating a database-selection module within system  100 ; and  
         [0018]      FIG. 7  is a flowchart corresponding to one or more additional exemplary methods of operating the database-selection module within system  100 . 
     
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS  
       [0019]     This description, which references and incorporates the above-identified Figures, describes one or more specific embodiments of an invention. These embodiments, offered not to limit but only to exemplify and teach the invention, are shown and described in sufficient detail to enable those skilled in the art to implement or practice the invention. Thus, where appropriate to avoid obscuring the invention, the description may omit certain information known to those of skill in the art.  
       Exemplary Definitions  
       [0020]     The description includes many terms with meanings derived from their usage in the art or from their use within the context of the description. However, as a further aid, the following exemplary definitions are presented.  
         [0021]     The term “document” refers to any addressable arrangement of machine-readable data, such as textual data.  
         [0022]     The term “database” includes any physical or logical arrangement of documents. In some embodiments, one or more of the databases share content with one or more other databases. Thus, the databases are not necessarily mutually exclusive. Indeed, some databases may be entirely contained by other databases.  
       Exemplary Information Retrieval System  
       [0023]      FIG. 1  shows an exemplary online information retrieval system  100 . System  100  includes one or more databases  110 , one or more servers  120 , and one or more access devices  130 .  
         [0024]     Databases  110  include representative databases  112 ,  114 , and  116 . In the exemplary embodiment, databases  110  include over 15,000 legal, business, and news databases, such as those within the Westlaw™ online research system which is available from Thomson Legal and Regulatory, Inc. of St. Paul, Minn. However, in other embodiments, one or more of the databases include financial, scientific, or health-care data.  
         [0025]     Databases  112 ,  114 , and  116 , which take the exemplary form of one or more electronic, magnetic, or optical data-storage devices, include or are otherwise associated with respective indices  112 N,  114 N, and  116 N and respective database profiles  112 P,  114 P, and  116 P. Each of the indices includes terms and phrases in association with corresponding document addresses, identifiers, and other conventional information. In some embodiments, each of the indices may also include consonant clusters (as described below) and Porter-type word stems. (For background information on Porter-type stems, see for example, M. F. Porter, “An Algorithm for Suffix Stripping,” in Program, 14(3), pp. 130-137, 1980.)  
         [0026]     In the exemplary embodiment, two types of database profiles (or authority files)  112 P- 116 P are included: one based on metadata, such as the titles and other data about the content of its corresponding database or corresponding set of databases, and the other based on actual database contents, such as query-based data samples and/or language models.  FIG. 2  shows a facsimile of an exemplary metadata-based database profile  200  defined using a markup language, such as XML (extensible markup language.) More particularly, some of the metadata-based profiles are organized around categories, such as “courts and government agencies,” “legal practice and research,” “geographic region or location.” Similarly, some of the sample-based profiles are arranged in collections to support category-specific searches. Exemplary categories include document type (case law, statutes, analytical, news, international, and so forth); practice areas (bankruptcy, communications, criminal law, and so forth); and geographic region (United Kingdom, Arizona, New York City, and so forth).  
         [0027]     Databases  112 ,  114 , and  116  are coupled or couplable via a wireless or wireline communications network, such as a local-, wide-, private-, or virtual-private network, to server  120 .  
         [0028]     Server  120 , which is generally representative of one or more servers for serving data in the form of webpages or other markup language forms with associated applets, ActiveX controls, or other related software and data structures to service clients of various “thicknesses.” More particularly, server  120  includes a processor  121 , a memory  122 , a subscriber database  123 , one or more search engines  124 , a profile database  125 , a database directory  126 , and a search-assistant software module  127  (one or more cross-platform interface objects or modules).  
         [0029]     Processor  121 , which is generally representative of one or more local or distributed processors or virtual machines, is coupled to memory  122 . Memory  122 , which takes the exemplary form of one or more electronic, magnetic, or optical data-storage devices, stores subscription database  123 , search engines  124 , profile database  125 , database directory  126 , and search-assistance software  127 .  
         [0030]     Subscription database  123  includes subscriber-related data for controlling, administering, and managing pay-as-you-go- or subscription-based access of databases  110 . Search engines  124  provide Boolean or natural language search capabilities for databases  110 , as well as for profile database  125  and database directory  126 . (Search engine functionality is further described below.) Profile database  125  contains profiles from databases  110 , such as profiles  112 P,  114 P, and  116 P. Database directory  126  includes a hierarchically organized listing of the databases  110  and an associated index  126 N, which in the exemplary embodiment includes not only terms, stemmed terms, and phrases or concepts, but also consonant clusters (which are described in more detail below).  FIG. 3  shows an exemplary database directory structure  300  used in some embodiments.  
         [0031]     Search-assistance software  127 —which, among other things, defines one or portions of a graphical user interface that helps users define searches for databases  110 —includes one or more browser-compatible applets, webpage templates, user-interface elements, objects or control features or other programmatic objects or structures. More specifically, software  127  includes a database-discovery module  1271 , a database-navigation module  1272 , and a database-selection module  1273 . Each module, in the exemplary embodiment, includes a set of one or more browser-compatible user-interface elements and associated software structures for assisting users in targeting relevant databases from databases  110 .  
         [0032]     Server  120  is communicatively coupled or couplable via a wireless or wireline communications network, such as a local-, wide-, private-, or virtual-private network, to one or more access devices, such as access device  130 .  
         [0033]     Access device  130  is not only communicatively coupled or couplable to server  130 , but also generally representative of one or more access devices. In the exemplary embodiment, access device  130  takes the form of a personal computer, workstation, personal digital assistant, mobile telephone, or any other device capable of providing an effective user interface with a server or database.  
         [0034]     Specifically, access device  130  includes one or more processors (or processing circuits)  131 , a memory  132 , a display  133 , a keyboard  134 , and a graphical pointer or selector  135 . Memory  132  includes a graphical user interface (GUI)  136  which upon execution causes display of data in association with one or more interactive control features (or user-interface elements) on display  133 . (The exemplary embodiment defines one or more portions of graphical user interface  136  using applets or other programmatic objects or structures from server  120 ; one or more of these applets or structures can be processed and executed using a browser.)  
         [0035]     More specifically, graphical user interface  136  defines or provides one or more display regions, such as representative regions  1361 ,  1362 ,  1363 . Display regions  1361 ,  1362 , and  1363  are respectively defined in memory to display data from databases  110  and/or server  120  in combination with interactive control features (elements or widgets)  1361 W,  1362 W, and  1363 W. In the exemplary embodiment, each of these control features takes the form of a hyperlink or other browser-compatible command input, and provides access to a search-assistance option for identifying one or more relevant target databases among databases  110  and ultimately accessing or retrieving one or more documents via server  120 .  
       Exemplary Database-Discovery Module  
       [0036]     Interactive control feature  1361 W is selectable to invoke operation of database-discovery module (program or objects)  1361 M within access device  130  (or module  1271  within server  120 .) This module assists users in determining which of databases  110  to access by accepting a query from the user and running that query against a set of sample-based database profiles in profile database  125  for each of the databases in the system, that is, databases  110 . Each profile is defined by treating its corresponding database as a document and representing its actual terms or phrases and their corresponding frequencies.  
         [0037]     More precisely,  FIG. 4  shows a flow chart  400  of an exemplary method of executing database-discovery module  1361 M. Flow chart  400  includes process blocks  410 - 470 .  
         [0038]     At block  410 , the exemplary method begins with presentation or rendering of a query dialog window portion of graphical user interface  136  on display  133 .  
         [0039]     Execution continues at block  420 .  
         [0040]     In block  420 , a query is received from the user. In the exemplary embodiment, this entails the user entering a string of natural language text in the query window. However, in other embodiments, the user may use Boolean and other forms of query commands and connectors. Execution proceeds to block  430 .  
         [0041]     Block  430  identifies and ranks by probable relevance a set of relevant databases by searching a set of database profiles based on the received query. In the exemplary embodiment, this identification entails searching profile database  125  using one or more portions of the received query and search engines  124 , specifically a natural language search engine or language-model-based approach with one or more techniques for scoring and ranking the relevance of the database profiles and corresponding databases (for example, profiles  112 P,  114 P, and  116 , and databases  112 ,  114 , and  116  in  FIG. 1 ) to the submitted query. (Exemplary scoring is described below in the Exemplary Relevance Scoring and Ranking section.) Execution continues at block  440 .  
         [0042]     Block  440  determines whether to automatically execute the received query against one or more of the identified databases. In the exemplary embodiment, this entails checking a user-preference indicator associated with the user in session data received from the user upon initiation of the current search session or retrieving user-preference data from subscription database  123  within server  120  (in  FIG. 1 .) If the determination is to perform an automatic search, execution advances to block  470 , which performs a full search of one or more of the identified databases, for example all of them or the top N-ranked among them.  
         [0043]     Some embodiments automatically execute the query against a fixed or dynamically chosen number N of the ranked databases. For example, one embodiment uses the top N-ranked databases, where N is a fixed value independent of the actual set of databases. Another determines N dynamically based on a percentage of the number of databases in the selection set. Yet another accepts the highest ranked database and every next highest database that is within some differential belief value of the previously accepted database. In other words, this embodiment truncates the results list based on gap thresholding. Other embodiments may vary N based on a minimum threshold score. The search results are then presented to the user via access device  130  as indicated in block  480 .  
         [0044]     If block  440  determines not to conduct an automatic search of the identified databases, execution branches to block  450 .  
         [0045]     Block  450  entails presenting the user with a ranked list of the identified databases for further user input. In the exemplary embodiment, this entails presenting a dialog box that lists two or more of the identified databases in rank order along with corresponding titles, identifiers, and/or other descriptive material. Each of the ranked databases is associated with a corresponding interactive control feature, such as a radio button or check box, to enable the user indicate selection or alternatively deselection of individual databases for further use. (Some embodiments include a select-all-type check box to allow the user to efficiently check all the radio buttons or check box. To aid user selection, some variants of this embodiment may list the relevance or “belief” score for each of the ranked databases.) The dialog box also includes an interactive control feature, such as a submit or next button, which the user can click to advance execution to block  460 .  
         [0046]     Block  460  entails receiving user database selections. In the exemplary embodiment, the user database selections made at block  450  are communicated from access device  130  and received at server  120 . Then, as block  470  shows, a search of the selected databases is made, and the results are presented at block  480 .  
       Exemplary Relevance Scoring &amp; Ranking for Block  430   
       [0047]     Block  430 , as described above, entails identifying a ranked set of databases based on the user-submitted query. To this end, the exemplary embodiment computes a probable relevance or belief score of each database using: 
 
 p   belief ( w   i   |d   j )= d   b +(1− d   b )· tf   b   ·idf   b ,  (1) 
 
 where d b  denotes the minimum belief component; tf b  denotes the term frequency within the database profile and idf b  denotes the inverse document frequency of term w i  across the collection of database profiles and thus indicates the rareness of a term within the collection. tf b  is more specifically defined as  
               tf   b     =       d   t     +       (     1   -     d   t       )     ·       log   ⁡     (       tf   ij     +   0.5     )         log   ⁡     (       tf     max     p   ′j         +   1.0     )                     (   2   )             
 
 where d t  is the minimum term frequency component when term w i  is present in a database profile, d j ; tf denotes the frequency of a specific term w i  within a database profile, and thus provides some indication or measure of the importance of the term within the profile; and tf max , denotes the frequency of the most frequently occurring term in the collection. And, idf b  is defined as  
               idf   b     =       log   ⁡     (       N   +   0.5     n     )         log   ⁡     (     N   +   1.0     )                 (   3   )             
 
 where n denotes the number of database profiles containing the query term w i ; N denotes the total number of database profile documents; In various embodiments, N varies depending on whether one is calculating idf based on the global or local profile set. 
 
         [0051]     For additional information regarding natural language searching, see U.S. Pat. Nos. 5,488,725 entitled System of Document Representation Retrieval By Successive Iterated Probability Sampling; U.S. Pat. No. 5,418,948 entitled Concept Matching of Natural Language Queries with a Database of Document Concepts; and U.S. Pat. No. 5,265,065 entitled Method and Apparatus for Information Retrieval from a Database by Replacing Domain Specific Stemmed Phases in a Natural Language Search to Create a Search Query. These patents are incorporated herein by reference.  
         [0052]     Other embodiments use a language model, such as one based on an additive or weighted sum approach, such as 
 
 p   sum ( w   i   |d )=λ· p   profile ( w   i   |d )+(1−λ)· p   profile db ( w   i )  (4) 
 
 where λ is a weighting factor between 0 and 1. If there are sparse-data problems when p profile (w i |d) is 0 (that is, an unobserved event), some embodiments extend the original document model with a database model, such as in Eq. (4). An additive model can help by leveraging extra evidence from the complete collection of profiles. 
 
         [0054]     Additionally, treating the query as a sequence of terms, with each term viewed as an independent event and with the query representing the joint event, yields the relevance or belief score  
                 p   sequence     ⁡     (     Q   |   d     )       =       ∏     i   =   1     M     ⁢           ⁢     p   ⁡     (       w   i     |   d     )                 (   5   )             
 
 where w 1 , w 2 , . . . w M  is a sequence of terms in the query Q and where d denotes the database profile. This treatment not only facilitates handling of duplicate query terms, but also permits construction of a model with phrases in local contexts. Some embodiments may also incorporate smoothing to address terms that may not be present in the current model. 
 
         [0056]     After computing the relevance scores and as part of the identification of relevant databases, one embodiment re-ranks the databases based on jurisdictional lexical analysis. The re-ranking entails determining whether the query contains any jurisdictionally relevant content, and if it does not, any of the search results that identify jurisdictionally biased collections are down-weighted relative to other found databases that lack the jurisdictional restriction. The rationale for this down-weighting is that legal topics often appear in collections organized around jurisdictions, such as a state jurisdiction. Some embodiments apply the re-ranking technique to each query, but leave the databases that match the jurisdictional lexical clues in their original rank(s), while downweighting those that do not.  
         [0057]     For additional information on the database-discovery module and potential variants, see the section of the incorporated application 60/436,191; Ser. No. 10/745,966; or 10/754,083, which is entitled Database Selection Using Actual Physical and Acquired Logical Collection Resources in a Massive Domain-specific Operational Environment.  
       Exemplary Database-Navigation Module  
       [0058]     In addition to  FIG. 1 &#39;s interactive control feature  1361 W, which invokes execution of database-discovery module  1361 M as illustrated in  FIG. 3 , graphical user interface  136  provides interactive control feature  1362 W. This feature is selectable to invoke execution of a database-navigation module (program or objects)  1362 M that assists users in browsing and searching using a hierarchical database directory (or tree structure), such as database directory  126  or  300 . In other words, this module allows a user to drill down into the directory, level by level, pausing at any level, entering a query, and running a restricted natural language search against the databases associated with all subdirectories located beneath the current mid-level node.  
         [0059]     More specifically,  FIG. 5  shows a flow chart  500  of an exemplary method of executing database-navigation module  1362 M. Flow chart  500  includes process blocks  510 - 570 .  
         [0060]     Block  510  entails presenting or displaying at least a portion of database directory on display  133 . In the exemplary embodiment, this entails initially displaying a window showing the root and main categorical nodes of the database directory, with each node associated with an interactive control feature, such as a navigation hyperlink or icon, which indicates whether each currently shown category includes subordinate categories (or leaf nodes) and which is selectable using a mouse or other graphical pointer to reveal subordinate categories and/or database identifiers. Also, some embodiments associate one or more directory listings with interactive control features that allow for users to view a detailed written description of the contents of the corresponding category of databases or of corresponding individual databases. Execution then advances to block  520 .  
         [0061]     Block  520  entails determining whether there has been a selection made to display one or more other portions of the directory. In the exemplary embodiment, this entails determining whether the interactive control feature for one of the currently displayed categories has been selected. If a control feature has been selected, execution advances to block  530 .  
         [0062]     Block  530  presents another portion of the database directory. In the exemplary embodiment, this entails display of one or more portions of the directory associated with the interactive control feature selected or activated at block  520 . These portions may include portions associated with subordinate, superior, or equal-rank categories. It may also include a listing of databases underneath the selected or activated node, for example, if the user has navigated to a leaf or terminal node.  
         [0063]     Block  540 , which immediately follows execution of block  530  or a negative determination at block  520 , entails determining whether the user has requested a search of databases associated with the currently displayed portion of the directory. If the determination is that no search is desired, for example, because the no interactive control feature associated with a search command has been selected, then execution returns to block  520  to determine whether additional navigational input has been received. However, if the determination is that a search is desired, for example, through interaction with an appropriate control feature, execution continues at block  550 .  
         [0064]     Block  550  entails receiving a query. In the exemplary embodiment, this entails the user entering a text query in a dialog box and selecting a submission button.  
         [0065]     Block  560 , which is executed in response to receipt of a query, runs the query against one or more databases or database profiles associated with the currently selected or displayed portion of the directory. The exemplary embodiment runs the query using search engines  124  (shown in  FIG. 1 .) Execution then continues at block  570 , which presents results (databases or documents) of the search to the user via access device  130 . If the presented search results include databases (more precisely database identifiers), the user selects one or more of the databases and initiates a search for documents.  
         [0066]     For additional information on the exemplary database-navigation module and potential variants, see the sections of the incorporated application 60/436,191; Ser. No. 10/745,966; or 10/754,083, which are entitled Effective Collection Metasearch in a Hierarchical Environment, Global vs. Localized Search: A Comparison of Database Selection Methods in a Hierarchical Environment, and The New Westlaw Directory: White Paper on Browse+Search Features.  
       Exemplary Database-Selection Module  
       [0067]     Interactive control feature  1363 W is selectable to invoke execution of a database-selection module (program or objects)  1363 M that assists users in identifying relevant databases based on query categorization. Some of the categories of queries are addressed with a natural language search engine that runs against several authority resources, whereas others are addressed using interactive decision trees.  
         [0068]     More particularly,  FIG. 6  shows a flow chart  600  of an exemplary method of operating database-selection module  1363 M. Flow chart  600  includes blocks  602 - 626 .  
         [0069]     In block  602 , the exemplary method begins with display of a query-categorization window presenting a set of query-categorization options to the user. In the exemplary embodiment, this set of categories includes the following 10 categories:  
                                                       1) Legal Practice &amp; Research Areas   2) Geographic Regions &amp;                Locations           3) Sources &amp; Publications   4) Courts &amp; Government                Agencies           5) Legal Issues   6) News           7) Definitions   8) Named Entities           9) Document References   10) Other                      
 
 Other embodiments may use a subset of these categories, other categories altogether, or a subset of these categories plus one or more other categories. For example, another embodiment uses the ten listed categories plus financial, educational, scientific, or healthcare information. 
 
         [0071]     Block  604  entails receiving at least one query-category selection. In the exemplary embodiment, this entails the user selecting the interactive control feature, such as a check box or radio button, associated with one of the listed query categories. To submit the query, the user clicks on an interactive control feature, such as a next or submit button, in the dialog window, which in turn causes module  1363 M (or more precisely, graphical user interface  136  and access device  130 ) to communicate the query-category selection to server  120 . Execution then continues at block  606 .  
         [0072]     In block  606 , the system receives a query from the user. To this end, the exemplary embodiment prompts the user to enter a text query through use of a text dialog box. With submission of the query through user selection of a submit or next button on the dialog box, execution advances to block  608 .  
         [0073]     In block  608 , the system determines, based on the received query category, how to assist the user in answering the query. In the exemplary embodiment, there are four possible ways to assist. If category 1 or 2 is selected, execution branches to block  610 ; if category 3 or 4 is selected, execution branches to block  612 ; if category 5, 6, 7, 8, or 9 is selected, execution branches to block  614 ; and if category 10, the “other” category, is selected, execution branches to block  616 .  
         [0074]     In block  610 , which is executed with selection of the “Legal Practice &amp; Research Areas” category or the “Geographic Regions &amp; Locations” category, the system searches category-specific database profiles based on the received query. To this end, the exemplary embodiment uses a natural language search engine to search a category-specific variant or subset of profile database  125  (shown in  FIG. 1 ) comprising metadata-based profiles. Each metadata-based profile in the profile database represents a set of one or more databases determined to be generally relevant to the selected category and thus facilitates identification of more highly relevant databases. However, other embodiments may search other forms of database representations. After the search, execution continues at block  618 .  
         [0075]     Block  618  entails presenting or displaying a ranked list of links to relevant portions (for example, categories or leaf nodes) of the database directory. The exemplary embodiment displays the ranked list in association with interactive control features, such as icons and hyperlinks, that are selectable to display or reveal the corresponding portions of the database directory. For example, if the user query were “California” and the selected category was “geographic,” the exemplary system would present a link to a California portion of the online directory, which the user could use to access all links having databases determined to be relevant to California. However, if the selected query category were “legal practice” for the same “California” user query, the results would be a specific set of links related to legal practice in California. Execution then advances to block  621 .  
         [0076]     Block  621  entails the user selecting a link to cause display of a corresponding portion of the directory, such as shown in  FIG. 3 . Display of the selected portion of the directory provides the user information regarding the organization and content of databases underneath the selected link. In some embodiments, the revealed portion of the database directory can then be further navigated or searched by the user using the browse-search functionality of database-navigation module  1362 W, as shown in  FIG. 5 . Execution then advances to block  622 , with user selection of one or more databases.  
         [0077]     Block  622  entails the system receiving user database selections. In the exemplary embodiment, this entails the user selecting and submitting one or more databases identified through use of the identified directory links. User database selections are communicated to server  120 . Execution continues at block  624 .  
         [0078]     In block  624 , the system uses the query submitted at block  606  to search databases corresponding to the database selections provided at block  622 . In the exemplary embodiment, the search is conducted using a natural language search engine. Some embodiments provide a block prior to block  624 , which enables the user to change or update the query submitted at block  606  and run the changed query against the selected database(s.) Execution then advances to block  626 , which entails presenting search results to the user.  
         [0079]     In addition to block  610 , execution from block  608  can proceed, based on the selected query category, to block  612 ,  614 , or  616 .  
         [0080]     In block  612 , which is executed with selection of the “Sources &amp; Publications” or the “Courts &amp; Government Agencies” categories, the system searches category-specific database profiles based on the received query. In the exemplary embodiment, a natural language search engine is used to search a specially crafted profile database  125 , with each database profile based on a language model for a subset of the databases predetermined to be relevant to the selected category. However, other embodiments may search other forms of database representations.  
         [0081]     Block  614 , which corresponds to selection of query categories “Legal Issues,” “News,” “Definitions,” “Named Entities,” or “Document References,” entails collecting additional category-specific information from the user. In the exemplary embodiment, this entails presenting a category-specific decision tree as a series of dialog boxes, with each dialog box corresponding to a separate level or decision in the tree. For example, if the legal-issue category were selected at block  604 , the exemplary system would prompt the user to first select one of the options: cases, statutes, law review; and if the cases option were selected, the system would prompt the user to select between state and federal options. Other embodiments may use other methods for gathering further information regarding a given category.  
         [0082]      FIG. 7  shows a set of five category-specific decision trees used in the exemplary embodiment to gather additional category-specific data. Specifically,  FIG. 7  shows a two-level legal tree  710 , a two-tier news tree  720 , a two-tier definitions tree  730 , a single-tier named-entities tree  740 , and a two-tier references tree  750 .  
         [0083]     Block  616 , which is executed in response to selection of the “Other” category, entails searching a set of generic database profiles. In the exemplary embodiment, this set of database profiles represents the entire available collection of databases rather than a category-specific subset as used in blocks  610  and  612 . Each profile, based on the actual data in the database, rather than metadata, is derived using data-sampling and/or language modeling techniques.  
         [0084]     After execution of block  612 ,  614 , or  616 , execution continues at block  620 . Block  620  identifies results of searching as a ranked list of databases. Execution then continues at block  622 , which as already described, entails receiving user database selections and possibly modified user queries.  
         [0085]     For more detailed information on the database-selection module and potential variants, see the section of the incorporated application 60/436,191; Ser. No. 10/745,966; or 10/754,083, which is entitled Early User-System Interaction for Database Selection in Massive Domain-specific Online Environments.  
       Search Engine Enhancements  
       [0086]     As noted above, server  120  (in  FIG. 1 ) includes one or more search engines  124 . In the exemplary embodiment, one or more of these search engines employ natural language techniques. Such techniques are described in detail in the previously referenced U.S. Pat. Nos. 5,488,725; 5,418,948; and 5,265,065. These search engines may also incorporate one or more of the following forms of enhancement.  
       Differential Treatment of Query Terms in Relevance Scoring  
       [0087]     One or more search engines in the exemplary embodiment treat certain terms or classes of terms in queries differently from other query terms. These terms include geographic locations, legal practice areas, and certain common legal terms. For example, in relevance calculations, such as tf×df (term frequency times inverse document frequency) scores, the exemplary embodiment up-weights, by a factor of two, the relevance value of query terms related to geography and practice area as well as others deemed as having high discrimination or user-preference value. Examples of geographic terms include the “United Kingdom,” “Arizona,” and “New York City.” Examples of practice-area terms that are up-weighted include “Civil Rights”, “Intellectual Property”, and “Workers Compensation.” 
         [0088]     Additionally, the exemplary embodiment down-weights, by a factor of two, the significance of generic terms, for example, generic legal terms, that are used frequently, but offer little value in discriminating one document from another similar document or one database from another. Examples of generic legal terms that are subject to down-weighting include law, regulation, code, department, cases, reporter, and journal.  
         [0089]     Thus, rather than viewing every search term in a query as being of equal significance within relevance calculations, the exemplary system ultimately takes advantage of domain-specific knowledge to produce relevance scores that more accurately reflect the marginal relevance of terms with low or high discriminatory value within the domain.  
       Consonant Clustering of Query Terms  
       [0090]     One or more search engines in the exemplary embodiment also selectively process queries, such as those related to category-specific databases, using a technique called consonant clustering. Specifically, to ensure that abbreviated, misspelled, or otherwise mistyped occurrences of terms in a query are given some role in query interpretation, the exemplary embodiment clusters consonants from the terms that do not match any terms in the applicable database indexes. In the exemplary embodiment, a clustered term is only matched against another clustered term, and not, for instance, against an indexed abbreviation that happens to correspond to a given cluster. The exemplary clustering process also uses an exception list since certain significant words share the same cluster. For example, ‘supreme’ and ‘superior’ have the same cluster.  
         [0091]     Prior to clustering, the exemplary embodiment determines whether a given query term is five or more characters in length and whether it contains only letters. If both these conditions are satisfied, the exemplary embodiment defines a cluster for the query term as its first letter and the next two consonants. In the case where the next two consonants are double consonants, the exemplary embodiment takes the first of the double consonants and the next consonant following the double-consonant pair. The letter ‘y’ can be either a vowel or a consonant, depending on whether the preceding letter is a consonant or a vowel, respectively. However, if ‘y’ is a vowel at the end of a word and the cluster is not yet complete, the ‘y’ can serve to complete the cluster. The table below shows several examples of how the exemplary cluster algorithm facilitates matching of misspelled query terms with correctly spelled versions of those terms.  
                                                           Equivalent               Query Term   Cluster   Matched Term                           colarado   clr   Colorado           missorri   msr   Missouri           loisiana   lsn   Louisiana           calif   clf   California           bankruptcy   bnk   bankruptcy           environmental   env   environmental                      
 
 Consonant clustering enables the exemplary embodiment to preserve the core or essence of a query term that may have been misspelled or poorly abbreviated or truncated. Thus, in one or more indices in the exemplary embodiment, each indexed term is associated or stored with its cluster representation. (Some terms, such as database identifiers and other special tokens, may not be represented with equivalent clusters.) Clustering is particularly useful for query terms representing states or other geographical regions, since such terms are generally of higher discriminatory value within a database and would generally contribute to higher precision results from a user perspective. 
 
       Top-Portion Scoring of Documents  
       [0093]     One or more search engines in the exemplary embodiment also employ top- or best-portion scoring in quantifying the relevance of a document or database profile, rather than solely relying on the score of a whole document or profile. Specifically, the exemplary embodiment assesses the relevance of each portion, for example, an XML-tagged field or segment, of a document or profile and then assigns the document or profile the relevance score associated with the highest or best scoring portion of the document rather than diluting the relevance score of this portion through aggregation with scores for lesser relevant portions of the document or profile. The improved performance produced by top-portion scoring is statistically significant.  
         [0094]     Another benefit of this technique is its preservation of user confidence in the search system. In some embodiments, many database profiles list each constituent publication in its own field, thus permitting users who enter a query representing a court or publication title to find an exact match when examining their ranked results list. However, with these type profiles, complete-document scoring can mask or discount the sectional or portion-based separations of terms, thus potentially allowing proximate terms from separate titles or other portions to bolster or suggest relevance where there is little or no relevance. This could promote lesser or even irrelevant profiles to higher ranks and thus could ultimately lead users to question the accuracy and validity of the searching system.  
       CONCLUSION  
       [0095]     In furtherance of the art, the present inventors have disclosed herein various exemplary systems, methods, and software that provide new options for effectively and efficiently targeting searches on an appropriate set of databases. One exemplary system presents online users with multiple forms of user-selectable search assistance. A first form, referred to as database discovery, provides a set of searchable database profiles based on data-sampling techniques. A second form, referred to as database navigation, is a hierarchical database directory that allows users to navigate to any desired category or subcategory and search databases or database profiles underneath the category. And, a third form, referred to as database selection, allows a user to categorize a query and presents, based on the category, a set of database choices, a set of links to specific portions of a database directory, or a set of decisions to further delineate needs.  
         [0096]     One or more of these and/or other forms of search assistance taught by the inventors ultimately enable users to develop and execute more effective search strategies, while potentially reducing computational burdens on online search systems, and most importantly increasing user satisfaction.  
         [0097]     The embodiments described above are intended only to illustrate and teach one or more ways of making and using the present invention, not to restrict its breadth or scope. The actual scope of the invention, which embraces all ways of practicing or implementing the teachings of the invention, is defined only by one or more issued patent claims and their equivalents.