Patent Publication Number: US-7216119-B1

Title: Method and apparatus for intelligent information retrieval

Description:
TECHNICAL FIELD OF THE INVENTION 
   This invention relates in general to information retrieval and, more particularly, to a method and apparatus for facilitating information retrieval by a person unfamiliar with relevant retrieval characteristics such as source names, content and/or source capabilities. 
   BACKGROUND OF THE INVENTION 
   There are many applications in which a person seeks to retrieve structured and/or unstructured data or information from databases or documents. However, the person is often unknowledgeable or unfamiliar with retrieval characteristics such as the retrieval source names, context and/or source capabilities. As a result, the person may locate little or none of the available information of interest. 
   As one specific example, a military officer involved in planning an operation may be seeking certain intelligence information that will facilitate planning of the mission, such as recent aerial photographs, weather information, data regarding the relevant terrain, and so forth. Although the officer will be very familiar with the context and terminology of military operations, he or she may have little or no knowledge regarding the context and terminology used by the intelligence community in storing and classifying the information of interest. 
   One prior approach to this problem has been the generation of complex templates in structure query language. However, these templates require substantial user training, are difficult to use, and are not readily extensible. A different known approach involves the use of keyword and/or concept based search engines. However, such approaches do not accommodate user specific situation criteria that describe the context of the needed information. Thus, these search engines present problems similar to those involved with structure query language templates, including but not limited to the need for substantial user training. 
   SUMMARY OF THE INVENTION 
   From the foregoing it may be appreciated that a need has arisen for a method and apparatus for effecting information retrieval by a person familiar with a context and/or terminology different from the context and/or terminology involved in accessing the information. According to the present invention, a method and apparatus are provided to address this need, and involve: maintaining a list of informational products; accepting input from a user in a user context familiar to the user; maintaining expert information developed by at least one expert which provides correlation between the user context and informational products in the list; and selecting a subset of the products as a function of the user input and the expert information. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     A better understanding of the present invention will be realized from the detailed description which follows, taken in conjunction with the accompanying drawings, in which: 
       FIG. 1  is a block diagram of a computer system which embodies aspects of the present invention; 
       FIG. 2  is a diagram showing a list of products utilized in the system of  FIG. 1 ; 
       FIG. 3  is a diagram showing a list of attributes utilized in the system of  FIG. 1 ; 
       FIG. 4  is a diagram showing a table that defines a relationship between the attributes of  FIG. 3  and the products of  FIG. 2 ; 
       FIG. 5  is a diagram of an exemplary input screen which a person can use in order to enter information into the system of  FIG. 1 , and shows one specific example of user input; 
       FIG. 6  is a view similar to  FIG. 5 , but showing a different specific example of user input; 
       FIG. 7  is a block diagram showing certain operations carried out by a computer program which is part of the computer system of  FIG. 1 ; 
       FIG. 8  is a diagram showing a table which is used by the system of  FIG. 1 , and which associates expert information with respective user input scenarios; 
       FIG. 9  is a diagram of a table showing aspects of a decisional process carried out by the computer system of  FIG. 1  for the specific exemplary user input of  FIG. 5 ; and 
       FIG. 10  is a diagram of a table which is similar to the table of  FIG. 9 , but which shows aspects of the decisional process for the specific exemplary user input of  FIG. 6 . 
   

   DETAILED DESCRIPTION OF THE INVENTION 
     FIG. 1  is a block diagram of an apparatus which is a computer system  10 . The computer system  10  includes a personal computer or workstation  12 , which is operatively coupled by a cable  13  to a network  14 . The hardware of the computer  12  is a commercially available configuration, and may for example be a personal computer of the type which is readily commercially available from Dell Computer Corporation of Austin, Tex. 
   The network  14  may include some or all of the Internet, an intranet, a local area network (LAN), a wide area network (WAN), and/or some other form of communications arrangement that can interact with a computer. In the disclosed embodiment, the network  14  carries some information which carries a security classification such as “secret”, and thus is a closed and secure network which can be accessed only by users with appropriate authorization. However, there are other applications for the present invention in which the network could be open to the public. 
   Turning in more detail to the computer  12 , the computer  12  includes a central unit  17  which is coupled to a display  18 , and which can receive user input by means of a keyboard  22  and/or a pointing device  23  such as a mouse. The central unit  17  includes a processor  26 , which in the disclosed embodiment is processor of a type commercially available under the trademark PENTIUM from Intel Corporation of Santa Clara, Calif. However, the processor  26  could alternatively be any other suitable type of processor. The central unit  17  also includes a hard disk drive (HDD)  27 . In a standard manner, the HDD  27  contains an operating system which is executed by the processor  26 , one or more application programs which can be selectively executed by the processor  26 , and data files that can be utilized by the processor  26 . 
   The central unit  17  also includes some random access memory (RAM)  28 , which is used by the processor  26  for storage of programs and/or data while the processor  26  is operational. Information stored in the RAM  28  is lost when power to the central unit  17  is turned off, and so the RAM  28  is used only for transitory storage of information. The central unit  17  also includes an interface circuit  31 , which permits the central unit  17  to communicate with the network  14  through the cable  13 . The interface circuit  31  may include a standard modem, a standard network interface, or some other appropriate circuit, depending on the nature of the interface through the cable  13  to the network  14 . 
   The HDD  27  stores an intelligent information retrieval program  41  which can be executed by the processor  26 , and also stores data  42  which is associated with the program  41 . The program  41  and the data  42  are discussed in more detail later, and for now it is sufficient to point out that the program  41  is used to selectively retrieve products  44  stored at one or more locations within the network  14 . For example, the products  44  may be available from a number of different servers scattered throughout the network  14 . For purposes of clarity, all products used in the disclosed embodiment are accessed through the network  14 , but it would alternatively be possible for at least some of the products to be stored within the computer  12 , for example on the HDD  27 . 
   As discussed in more detail later, the computer system  10  in the embodiment of  FIG. 1  happens to be used in a military context, in particular to help a military officer identify and access information that may help the officer plan a military operation, such as locating and destroying a selected target such as a mobile rocket launcher of an adversary. However, this military context is merely one example of an application for the present invention. The present invention can also be used in a variety of other applications that involve information retrieval, including entirely non-military applications. 
     FIG. 2  is a diagram representing a list of the products  44 . The list in  FIG. 2  includes seven products, which are considered a suitable example of products for the purpose of explaining one exemplary application of the present invention. The list shown in  FIG. 2  could include products additional to those shown in  FIG. 2 , but those additional products need not be shown or described in detail here in order to convey a clear understanding of the present invention. Moreover, the present invention is compatible with applications in which the products involved are entirely different from the exemplary products shown in  FIG. 2 . 
   Referring to the list of products in  FIG. 2 , the “Air Tasking Order” product is a list of aircraft assignments, such as the current locations of available aircraft. The “Target List” product is a list of potential targets. The “E/O Imagery” product refers to electro-optical images, or in other words images based on visible light, rather than on other types of radiation, such as thermal images or radar images. E/O imagery might, for example, include a reconnaissance photograph taken from an airplane or satellite. The “Radar Imagery” product includes images generated using radar technology, for example by a radar system in a reconnaissance plane. 
   The “Terrain Analysis” product includes up-to-date information regarding the type of terrain in a specified area of interest, and the current state of that particular terrain. For example, one area may have a desert terrain, whereas a different area may have tropical rain forest terrain. Further, for a given area, conditions such as the current weather can affect the state of the terrain. For example, an area that is normally dry could currently be flooded due to recent heavy rains. The “Signals Intel” product involved intercepted communications, such as an intercept of a radio transmission by an adversary. The “Order of Battle” product is a list of available military units and equipment, for example throughout the world. 
   Each of the products shown in  FIG. 2  is associated with one or more attributes.  FIG. 3  is a diagram showing a list of attributes utilized in the disclosed embodiment. The list in  FIG. 3  includes twenty-one attributes, which are considered a suitable example of attributes for purposes of explaining one exemplary application of the present invention. The list shown in  FIG. 3  could include attributes additional to those specifically listed in  FIG. 3 , but those additional attributes need not be shown or described in detail here for purposes of conveying a clear understanding of the present invention. Moreover, the present invention is compatible with applications in which the attributes involved are entirely different from the exemplary attributes shown in  FIG. 3 . 
   Turning in more detail to the specific exemplary attributes listed in  FIG. 3 , the “Target Name” attribute is an arbitrary name which can be assigned to a particular target for convenience, such as “Cave  52 ” or “Zazzo Air Base”. The “Target ID Number” is an arbitrary and unique identification number or code which can be assigned to a particular target for purposes of convenience and accuracy in identifying that particular target. The “Time on Target” attribute is the time that a target will be reached. As one example, it may be the time that an aircraft will be over the target. The “Target Location” attribute is the location of a target of interest, for example as expressed in latitude and longitude. 
   The “Daylight Visible” attribute is an indication of whether something of interest is visible in daylight. Depending on whether or not the item is visible in daylight, visible imagery such as aerial photographs may or may not provide useful information. The “Geo-Rectified” attribute is used to identify images which have been processed so as to improve them. For example, a photograph taken from a satellite may be adjusted so as to eliminate obliqueness caused by the curvature of the earth&#39;s surface. The “Feature Resolution” attribute relates to the resolution of an image such as a radar image or an E/O image, and identifies the size of the smallest feature which can be seen in the image. For example, in a satellite photograph with a low resolution, it may be possible to discern an ocean liner but not smaller details such as the presence of a person standing on the deck of the ocean liner. On the other hand, in a satellite photograph with a high resolution, it may be possible to discern not only the ocean liner, but also smaller details, such as the fact that a person is standing on its deck. The “Collection Time” attribute identifies a time frame or a point in time at which the associated information was gathered. The “Area Coverage” attribute refers to the amount of area covered by associated information. For example, an aerial photograph may show one square mile, or a thousand square miles. 
   The “Weather Effects” attribute is an indication of the extent to which the associated information can be affected by weather. For example, satellite photographs of a specified region may or may not be useful, depending on whether the region is currently free of clouds or subject to heavy cloud cover. The “Vehicle Mobility” attribute relates to various characteristics of the mobility of a vehicle, such as its speed and maneuverability in various different environments. For example, a vehicle which is fast and maneuverable on a hard surface may be virtually useless if the terrain of interest is swampy. The “Terrain Features” attribute relates to various characteristics of the terrain, including the nature of the terrain and its topographical composition. The “Obstructions” attribute relates to considerations that can affect mobility, such as a river which many vehicles cannot cross, a cliff which would impede progress of many types of vehicles, or a bridge which has been rendered unusable. 
   The “Mobility Contours” attribute represents information derived through analytical evaluation based on factors such as terrain features and vehicle mobility. As one example, the contours may be a series of nested lines on a map which represent respective probability contours as to the maximum distance that a particular target may have moved from a prior known location within respective different periods of time. 
   The “Equipment ID” attribute is an arbitrary identification number that may be assigned to a piece of military equipment of an adversary, for purposes of convenience and accuracy in referring to that particular piece of equipment. For example, a particular mobile rocket launcher could be assigned a unique identification number in order to permit it to be easily and accurately identified. 
   The “System Association” attribute is an identification code for a larger system, in which a particular target may be a part of the larger system. The “Functional Assessment” attribute is an evaluation of the function and capability of a particular item of interest. The “Strength” attribute is an indication of how many items of a given type are present. For example, where an item of interest is a mobile rocket launcher, an adversary may have several mobile rocket launchers, and the “Strength” attribute would give the number of these mobile rocket launchers. The “Composition” attribute provides an indication of whether the items of interest are all the same, or have different capabilities. For example, if an opponent has several mobile rocket launchers, some may be of one type and others may be of a different type. The “Composition” attribute would reflect this and also give a sense for how many of each type are preset. The “Affiliation” attribute is an indication of the country or entity which is associated with a particular target or item of interest. The “Courses of Action” attribute reflects an analytical assessment or estimate of future capability or intention. Assuming that a given target is a mobile rocket launcher, this attribute may include an assessment of whether it is expected to remain stationary for a specified period of time (for example for at least 30 minutes while it is halted for refueling), or whether it is expected to be on the move relatively soon (for example within 5 minutes). 
   As mentioned above, each of the products shown in  FIG. 2  is associated with one or more of the attributes shown in  FIG. 3 . In this regard,  FIG. 4  is a diagram showing a table or database which indicates how, in the disclosed embodiment, the products listed in  FIG. 2  are each associated with one or more of the specific attributes listed in  FIG. 3 . It will be noted that a given attribute may be associated with more than one product. For example,  FIG. 4  shows that the “Target Location” attribute is associated with several different products, including “Air Tasking Order”, “Target List”, “Signals Intel” and “Order of Battle”. 
   Referring again to  FIG. 1 , the computer program  41  can present on the display  18  an input screen which a human operator/user can utilize to enter information in a context that is familiar to the user and does not require special training. For example,  FIG. 5  is a diagram of a screen which the program  41  presents on the display  18  in order to obtain user input, which is referred to here as a profile of a proposed operation or mission. The illustrated screen includes three sections  61 ,  62  and  63 , which respectively correspond to information regarding an “Environment”, a military “Echelon”, and a “Function/Mission” of interest. 
   With reference to the Environment section  61  in  FIG. 5 , the user can select from six options, which are “Space”, “Air”, “Land”, “Urban”, “Littoral”, and “Maritime”. In the Echelon section  62 , the user can specify the military echelon which is to be responsible for the proposed operation. In this regard,  FIG. 5  shows that the user has eight possible selections for echelon, which are arranged in three columns. Each column represents a different level within the military hierarchy, with the highest level on the left and the lowest level on the right. The available options are “JTF” (Joint Task Force), “JFACC” (Joint Forces Air Component Commander), “JFLCC” (Joint Forces Land Component Commander), “JFMCC” (Joint Forces Maritime Component Commander), “JFSOC” (Joint Forces Special Operations Commander), “AIS” (Air Intelligence Squadron), DOCC (Deep Operations Coordination Cell), and “CIC” (Combat Information Center). The Function/Mission section  63  of the screen in  FIG. 5  presents the user with six possible selections, which are “Plans”, “Collection Management”, “Targeting”, “Operations”, “Exploitation”, and “Relocatable Target Detection”. 
   It will be noted in  FIG. 5  that the user has selected four of the available options, which are the highlighted options and which define a profile for a particular proposed operation or mission. In particular, the specific highlighted options in  FIG. 5  are “Air” in the Environment section  61 , “AIS” in the Echelon section  62 , and “Targeting” and “Relocatable Target Detection” in the Function/Mission section  63 . 
   The screen shown in  FIG. 5  is one example of how an input profile screen might be formatted, but the screen could alternatively have a variety of other configurations. Further, the specific selections available to the user in this screen are exemplary, and it would alternatively be possible to have additional selections, or to have selections which are entirely different from those shown in  FIG. 5 . 
     FIG. 6  is a diagrammatic view which is similar to  FIG. 5  and shows the same profile screen, but which shows different user selections representing a profile for a different proposed mission or operation. In particular, in  FIG. 6 , the user has selected “Urban” in the Environment section  61 , “JFLCC” (Joint Forces Land Component Commander) in the Echelon section  62 , and “Targeting” and “Operations” in the Function/Mission section  63 . 
   Turning now in more detail to the retrieval program shown at  41  in  FIG. 1 ,  FIG. 7  is a block diagram showing at a very high level some selected aspects of how information is processed by the program  41 . Informational flow is represented generally by three successive sections, including a user profile section  101 , an associative memory section  102 , and a retrieval product list section  103 . In the user profile section  101 , block  106  represents the input obtained from the user through the display  18  ( FIG. 1 ), in the form of the profile screen shown in  FIGS. 5 and 6 . The profile screen shown in  FIGS. 5 and 6  requests and accepts input in a context which is highly familiar to the relevant user, who in this case is likely to be a military officer. Thus, the terminology and organization used in the screen of  FIGS. 5 and 6  are built around military operations. In some other application, for example a non-military application, the terminology and organization of the screen would be tailored to a context familiar to the particular user for that application. 
   In  FIG. 7 , the user information obtained in the block  106  of the user profile section  101  is supplied to the associative memory section  102 , and in particular to a correlator  121  within the associative memory section. The correlator  121  correlates the user input with information from a database  122 . In the disclosed embodiment, the database  122  is part of the data  42  ( FIG. 1 ) stored on HDD  27 . However, the database  122  could alternatively be stored at some other location within the network  14 , provided the program  41  had knowledge of where the database  122  was stored. 
     FIG. 8  is a diagrammatic view of a table which represents the information stored within the database  122 . The table includes a portion  126  containing profile information, and a portion  127  containing expert information.  FIG. 8  shows only two rows of the table, which are respectively identified by reference numerals  131  and  132 . The table actually includes many additional rows, but the two illustrated rows  131  and  132  are sufficient for purposes of conveying an understanding of the present invention. 
   It will be noted that the profile information  126  in row  131  is identical to the user-specified profile information shown in  FIG. 5 , and that the profile information  126  in row  132  is identical to the user-specified profile information in  FIG. 6 . Stated differently, when the user enters the specific combination of profile items shown in  FIG. 5 , the correlator  121  searches for a single corresponding row in the table of  FIG. 8  (within database  122 ), which is the row  131 . Similarly, when the user selects the specific combination of profile items shown in  FIG. 6 , the correlator  121  searches for a single corresponding row in the table of  FIG. 8 , which is the row  132 . 
   For each row in the table of  FIG. 8 , one or more experts have taken the specific combination of profile items shown at  126  within that row, and have developed related expert information which is set forth in the portion  127  of the same row. For example, for the specific profile information shown at  126  in row  131 , one or more experts have determined that the operation proposed by the user relates to an air attack, and in particular an air attack which involves targeting and detection of a relocatable target, and which would be carried out by the Air Intelligence Squadron. Further, with reference to the right column in the table of  FIG. 8 , the expert information includes an identification of one or more products which are from the list of  FIG. 2  and which one or more experts believe are likely to be useful to a person trying to plan such an operation. In the row  131  of  FIG. 2 , all seven products from  FIG. 2  have been identified, but other rows in the table of  FIG. 8  may identify only a subset of the products shown in  FIG. 2 , and/or may identify other products in the list which are not specifically identified in  FIG. 2 . 
   In a similar manner, with reference to row  132  in  FIG. 8 , one or more experts have determined that, for the specific combination of profile items set forth at  126 , the user is proposing a ground attack that will involve command element targeting and line communication denial, and which would be carried out by tank and artillery maneuver forces. Further, with reference to the right column in  FIG. 8 , one or more experts have identified a list of relevant products for the proposed operation. Row  132  happens to identify all seven products specifically listed in  FIG. 2 , but as noted above it would be possible for row  132  to identify a subset of these seven products, and/or to identify other products which are part of the product list but which are not specifically identified in  FIG. 2 . 
   Referring again to  FIG. 7 , the correlator  121  takes the information from the selected row of the table of  FIG. 8 , and supplies it to a Bayesian decision network  141 . The Bayesian decision network  141  also receives information from an attributes database  143 , and a products attributes relative probabilities database  144 . In the disclosed embodiment, the databases  143  and  144  are each part of the data  42  stored on the HDD  27  of the computer  12 . However, either or both could alternatively be stored at some convenient location within the network  14 , so long as the retrieval program  41  knew the location of this information. 
   The attributes database  143  essentially includes the information shown in  FIG. 4 , or in other words an identification of which attributes are associated with each product. In this regard, the determination of which attributes should be associated with each product is yet another determination made by one or more experts, and then stored in the database  143  for use by the Bayesian decision network  141 . The probabilities database  144  includes a large number of probability values that have been determined by one or more experts, and that may be adjusted by experts from time to time based on actual experience or other factors. The nature of the probability information stored in the database  144  will become clearer in the context of the discussion below regarding operation of the Bayesian decision network  141 . 
   In particular, turning in more detail to the Bayesian decision network  141 , persons skilled in the art are familiar with Bayes&#39; theorem, which describes a relationship between the conditional probability of events and the probability that a specific event may occur. Bayes&#39; theorem is used here to rank (describe the prioritized value of) specific intelligence products for their likelihood to reflect an operational event of interest to a specific user. For a specific proposed operation, such as the user input of  FIG. 5  (and row  131  in  FIG. 8 ), or else the user input of  FIG. 6  (and row  132  in  FIG. 8 ), there exists a set of events for which a user desires to maximize the probability that the available intelligence products ( FIG. 2 ) will reflect the operational event, for example through detection, identification and/or tracking. On a general level, this can be stated using Bayes&#39; Theorem in the following manner: 
   
     
       
         
           
             
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   The manner in which this theorem is incorporated into the Bayesian decision network  141  can be understood with reference to  FIG. 9 , which is a chart having the seven products of  FIG. 2  listed across the top, and the twenty-one attributes of  FIG. 3  listed along the left side. It will be noted in  FIG. 9  that the column for each product includes probability values for each of the attributes associated with that product. As one specific example,  FIG. 4  shows that the product “Air Tasking Order” is associated with four attributes. In  FIG. 9 , it will be noted that the column for this product includes probability information for each of these four associated attributes, and in particular includes two probability values for each attribute. The right probability value is a probability value which reflects the probability that the particular product exists, and the left value represents the probability that, assuming the product exists, the product will reflect the event or information of interest. All of the probability values shown in  FIG. 9  are part of the information in the probabilities database  144  ( FIG. 7 ). 
     FIG. 10  is a diagram similar to  FIG. 9 , except that it contains a different set of probability values, which correspond to the user input represented by  FIG. 6  and by row  132  of  FIG. 8 . Thus, in the disclosed embodiment, the probabilities database  144  effectively includes a respective set of probability values for each row in the table of  FIG. 8 . As mentioned earlier, the probability values in the database  144 , including those shown in the tables of  FIGS. 9 and 10 , are determined by one or more experts, and may be adjusted by experts from time to time, based on experience and other relevant factors. 
   In each of the tables of  FIGS. 9 and 10 , the second to last row includes product relative probability values, which are each calculated from the probability values set forth above them in the same column. In more detail, those in each “Reflects” column are calculated according to the equation: 
             PRP   ⁡     (   Reflect   )       =           P   1     ⁡     (   Reflect   )       +       P     i   +   1       ⁡     (   Reflect   )       +   …   +       P   N     ⁡     (   Reflect   )         N           
where P i  is the attribute probability, and N is the number of attribute probabilities values in the column. Further, the product relative probability values in each “Exists” column are calculated according to the following equation:
   PRP (Exist)= P   i (Exist)* P   i+1 (Exist)* . . . * P   N (Exist) 
where P i  is the attribute probability, and N is the number of attribute probability values in the column. The next to last row in each table sets forth product conditional probability values, which are each calculated from the product relative probability values in the immediately proceeding row, according to the equation:
 
           PCP   =           P   1     ⁡     (   Reflect   )       *       P   1     ⁡     (   Exist   )             ∑     i   =     1   ⁢           ⁢   to   ⁢           ⁢   N         ⁢         P   1     ⁡     (   Reflect   )       *       P   1     ⁡     (   Exist   )                   
where P i  is the product probability, and N is the number of products.
 
   In the tables of  FIGS. 9 and 10 , the final row of each table sets forth for each listed product a respective unique integer which is a rank value. These rank values are determined directly by the relative magnitudes of the product conditional probability values set forth in the immediately proceeding row. Thus, for example, the highest product conditional probability value in  FIG. 9  is 0.243 for the “Air Tasking Order” product, and so this product is given a rank value of 1. The second highest product conditional probability value is 0.224 for the “Radar Imagery” product, which therefore receives a rank value of 2. It will be noted that the ranking or order of the products in  FIG. 9  is different from the ranking or order of the same products in  FIG. 10 . This reflects the fact that the products most likely to be useful for planning an air operation of the type addressed by  FIG. 9  may be different from the products most likely to be useful for planning a land operation of the type addressed by  FIG. 10 . 
   It will thus be recognized that the use of Bayes&#39; theorem in the disclosed embodiment provides a means to describe and therefore optimize the relationship between an operational event to be observed and other specified independent events such as intelligence collection, through analysis of the likelihood that the observed event will be reflected in the given specified intelligence events. As a specific example, the likelihood and therefore the ranked value to a user that a reconnaissance imaging activity will show a relocatable missile launcher can be estimated based on the fractional analysis of the individual intelligence products relative likelihoods that a relocatable missile launcher is reflected in the intelligence product and the relative probability of the product to exist. 
   With reference to  FIG. 7 , the Bayesian decision network  141  supplies to a comparator  201  of the retrieval product list section  103  a ranked list of products, which is determined in the manner discussed above in association with  FIGS. 9 and 10 . The comparator  201  compares the product conditional probability values (for example as shown in the next-to-last row of the tables of  FIGS. 9 and 10 ) to a ranking threshold  203 . In the disclosed embodiment, the ranking threshold is determined by one or more experts and may be adjusted by experts from time to time based on experience. However, it would alternatively be possible for the ranking threshold  203  determined by the experts to serve as an initial or default threshold value, and for the user to have the capability to selectively adjust the threshold  203  through use of the keyboard  22  and/or the mouse  23  ( FIG. 1 ). 
   In the present discussion, it is assumed for the sake of example that the ranking threshold  203  has a expert-determined value of 0.200. The comparator  201  compares the calculated product conditional probability value for each product to the threshold  203 , and outputs a list containing only the highest-ranked products, namely those which have a product conditional probability value in excess of the threshold value. Thus, with reference to  FIG. 9 , the products passed by the comparator  201  to its output would be “Air Tasking Order”, “Radar Imagery”, and “Target List”, which are respectively ranked  1 ,  2  and  3 . With reference to  FIG. 10 , the products passed by the comparator  201  to its output would be “Terrain Analysis”, “E/O Imagery”, and “Radar Imagery”, which are respectively ranked  1 ,  2  and  3 . Although the examples of  FIGS. 9 and 10  each happen to yield three products which meet the threshold requirement, the number of products output by the comparator  201  could be higher or lower, and would typically vary from one user profile to another. The ranked list of products which the comparator  201  supplies to its output are then supplied to a not-illustrated product retrieval engine of a known type, which retrieves each of those selected products from the network  14  ( FIG. 1 ), and then presents the retrieved products to the user of the computer  12  for study and analysis. 
   It will be noted that the output of the comparator  201  effectively represents a translation of the user input at  106  in the form of military operational terminology into a list of products in intelligence community terminology. This facilitates the intelligent selection and presentation to the user of relevant intelligence community products, without any need for a user familiar with military operational terminology to undergo training in intelligence products, intelligence community terminology, and the like. Thus, the retrieval product list is the result of converting a list of retrieval product attributes such as time, location, resolution, geospatial orientation, sensor type, and/or movement detection into a list of products to retrieve, such as a radar map and/or an infrared image. This conversion includes correlation of the attribute list with a product list, in a manner determining the minimum product set needed to encompass the product attributes, with emphasis on reducing product redundancy. 
   The present invention provides a number of technical advantages. One such technical advantage is the provision of a user interface which communicates with a user using terminology and a context that are familiar to the user, after which the user input is converted as a function of expert information into an information request that may use terminology and/or a context that are not familiar to the user. This permits the user to obtain relevant information without necessarily being knowledgeable in, or undergoing specialized training with respect to, the specific terminology and context in which the information is stored. As a specific aspect of this, the translation of the user input into different terminology and/or a different context is carried out as a function of information developed by one or more experts. 
   Although one embodiment has been illustrated and described in detail, it will be understood that substitutions and alterations are possible without departing from the spirit and scope of the present invention, as defined by the following claims.