Patent Publication Number: US-2022237245-A1

Title: Description set based searching

Description:
PRIORITY APPLICATIONS 
     This application is a continuation of and claims the benefit of priority to U.S. patent application Ser. No. 16/554,087, filed Aug. 28, 2019, the content of which is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     Embodiments of the present disclosure relate generally to query processing and, more particularly, but not by way of limitation, to identifying item search results. 
     BACKGROUND 
     Some search engines find search results by analyzing a given query for meaning and context. Such systems require computationally complex algorithms that determine the query&#39;s meaning and context, and match the context and meaning to the most relevant search results. Not all data to be searched works well with these types of search engines, and applying computationally complex semantics-based algorithms to data not pre-configured for such algorithms can result in inefficiencies, such as computational overhead and irrelevant results. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various ones of the appended drawings merely illustrate example embodiments of the present disclosure and should not be considered as limiting its scope. 
         FIG. 1  is a block diagram illustrating a description search system implemented in a networked system, according to some example embodiments. 
         FIG. 2  shows an example data architecture for implementing a description search system, according to some example embodiments. 
         FIG. 3  shows internal functional components of a description search system, according to some example embodiments. 
         FIG. 4  shows a flow diagram of an example method for generating description sets, according to some example embodiments. 
         FIG. 5  shows a flow diagram of a method for modifying a description set, according to some example embodiments. 
         FIG. 6  shows a flow diagram of a method for returning results for a search request, according to some example embodiments. 
         FIG. 7  shows a flow diagram of a method for generating description sets in different languages using an initial description set, according to some example embodiments. 
         FIG. 8  shows an example user interface, according to some example embodiments. 
         FIG. 9  is a block diagram illustrating a representative software architecture, which may be used in conjunction with various hardware architectures herein described. 
         FIG. 10  is a block diagram illustrating components of a machine, according to some example embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. 
     
    
    
     DETAILED DESCRIPTION 
     The description that follows includes systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative embodiments of the disclosure. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the inventive subject matter. It will be evident, however, to those skilled in the art, that embodiments of the inventive subject matter may be practiced without these specific details. In general, well-known instruction instances, protocols, structures, and techniques are not necessarily shown in detail. 
     With reference to  FIG. 1 , an example embodiment of a high-level client-server-based network architecture  100  is shown. A networked system  102  provides server-side functionality via a network  104  (e.g., the Internet or a wide area network (WAN)) to one or more client devices  110 . In some implementations, a user  106  interacts with the networked system  102  using the client device  110 .  FIG. 1  illustrates, for example, a web client  112  (e.g., a browser), a client application  114 , and a programmatic client  116  executing on the client device  110 . The client device  110  includes the web client  112 , the client application  114 , and the programmatic client  116  alone, together, or in any suitable combination. Although  FIG. 1  shows one client device  110 , in other implementations, the network architecture  100  comprises multiple client devices. 
     In various implementations, the client device  110  comprises a computing device that includes at least a display and communication capabilities that provide access to the networked system  102  via the network  104 . The client device  110  comprises, but is not limited to, a remote device, work station, computer, general-purpose computer, Internet appliance, hand-held device, wireless device, portable device, wearable computer, cellular or mobile phone, Personal Digital Assistant (PDA), smart phone, tablet, ultrabook, netbook, laptop, desktop, multi-processor system, microprocessor-based or programmable consumer electronic system, game console, set-top box, network Personal Computer (PC), mini-computer, and so forth. In an example embodiment, the client device  110  comprises one or more of a touch screen, accelerometer, gyroscope, biometric sensor, camera, microphone, Global Positioning System (GPS) device, and the like. 
     The client device  110  communicates with the network  104  via a wired or wireless connection. For example, one or more portions of the network  104  comprise an ad hoc network, an intranet, an extranet, a Virtual Private Network (VPN), a Local Area Network (LAN), a wireless LAN (WLAN), a WAN, a wireless WAN (WWAN), a Metropolitan Area Network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, a wireless network, a Wi-Fi® network, a Worldwide Interoperability for Microwave Access (WiMax) network, another type of network, or any suitable combination thereof. 
     In some example embodiments, the client device  110  includes one or more applications (also referred to as “apps”) such as, but not limited to, web browsers, book reader apps (operable to read e-books), media apps (operable to present various media forms including audio and video), fitness apps, biometric monitoring apps, messaging apps, and electronic mail (email) apps. In some implementations, the client application  114  includes various components operable to present information to the user  106  and communicate with the networked system  102 . 
     The web client  112  accesses the various systems of the networked system  102  via the web interface supported by a web server  122 . Similarly, the programmatic client  116  and client application  114  access the various services and functions provided by the networked system  102  via the programmatic interface provided by an Application Programming Interface (API) server  120 . 
     Users (e.g., the user  106 ) comprise a person, a machine, or another means of interacting with the client device  110 . In some example embodiments, the user is not part of the network architecture  100 , but interacts with the network architecture  100  via the client device  110  or another means. For instance, the user provides input (e.g., uses a touch screen input device or alphanumeric input device to generate a search query) to the client device  110  and the input is communicated to the networked system  102  via the network  104 . In this instance, the networked system  102 , in response to receiving the input from the user, communicates information (e.g., description results) to the client device  110  via the network  104  to be presented to the user. In this way, the user can interact with the networked system  102  using the client device  110 . 
     The API server  120  and the web server  122  are coupled to, and provide programmatic and web interfaces respectively to, one or more application servers  140 . The application server  140  can host a description search system  150 , which can comprise one or more modules or applications, and which can be embodied as hardware, software, firmware, or any combination thereof. The application server  140  is, in turn, shown to be coupled to a database server  124  that facilitates access to one or more information storage repositories, such as a database  126 . In an example embodiment, the database  126  comprises one or more storage devices that store information to be accessed by the description search system  150  or the client device  110 . For example, a term dataset, pages, and descriptions in one or more languages are stored in the database  126 , according to some example embodiments. Additionally, a third-party application  132 , executing on a third-party server  130 , is shown as having programmatic access to the networked system  102  via the programmatic interface provided by the API server  120 . For example, the third-party application  132 , utilizing information retrieved from the networked system  102 , supports one or more features or functions on a website hosted by a third party. 
     Further, while the client-server-based network architecture  100  shown in  FIG. 1  employs a client-server architecture, the present inventive subject matter is, of course, not limited to such an architecture, and can equally well find application in a distributed, or peer-to-peer, architecture system, for example. The various systems of the application server  140  (e.g., the description search system  150 ) can also be implemented as standalone software programs, which do not necessarily have networking capabilities. 
       FIG. 2  shows an example data architecture  200  for implementing a description search system  150 , according to some example embodiments. In  FIG. 2 , an item  205  is a thing to be searched for, such as a physical item (e.g., a lamp, a reciprocating saw, a computer), images, articles (e.g., online encyclopedia article), medical records (e.g., diseases), and so on. In some example embodiments, the items are data objects stored in results datastore  235  which can be displayed as search results. 
     The item  205  is associated with item metadata  210  including properties and their underlying values. A property is an attribute or characteristic used to describe the item. Properties can be of different types. For example, item  205  can be a chair and item metadata  210  can include a quantity of legs property (“A”) with possible values including 1 leg, 3 legs, 4 legs; item metadata  210  can further include a finish type property (“B”) with possible underlying values including matte and shiny; and item metadata  210  can further include a material type property (“C”) with possible underlying values including wood, metal, and plastic. 
     In some example embodiments, the item metadata  210  of the item  205  is used to generate a plurality of descriptions  215 , which are different combinations of the properties and underlying values. As an illustrative example, an itemized list of descriptions  215  generated using the description search system  150  can include:
         A=i (one legged chair)   A=ii (three-legged chair)   A=iii (four-legged chair)   A=i, B=i (one legged chair with matte finish)   A=ii, B=i (three-legged chair with matte finish)   A=iii, B=i (four-legged chair with matte finish)   A=i, B=ii (one legged chair with shiny finish)   A=ii, B=ii (three-legged chair with shiny finish)   A=iii, B=ii (four-legged chair with shiny finish)   A=i, B=i, C=i (one legged wooden chair with matte finish), etc.       

     The descriptions  215  are stored with descriptions datastore  220  created for other items. For example, a set of descriptions can be generated for a table item type (e.g., number of legs, material, etc.), another set of descriptions can be generated for an electric tool type (e.g., properties including wattage, type, features, etc.), another set of descriptions can be generated for images (e.g., size, predominate colors, subject, etc.) blue images, red images, or anything to be searched and retrieved by the description search system  150 . 
     In some example embodiments, the descriptions in the descriptions datastore  220  are not queries in that they have not yet been selected for use as a query. Instead, one or more of the descriptions can be selected as a query to be queried against a database in response to a search request received from a user. For example, a user may generate a search request  225  and the terms of the search request  225  can be processed to determine the closest matching descriptions in the descriptions datastore  220 . Different types of matching schemes can be used to find which of the descriptions stored in the descriptions datastore match the terms in the search request, including for example: word comparison, token based searching (e.g., converting a given description into a token, then matching input search words to tokens generated from the descriptions), using an inverted index, reverse index, string-to-string comparison, letter-to-letter matching to find matching descriptions that are similar to the words in the query, and other matching schemes. 
     After a description is determined to be closest to the terms of the user-submitted search request  225 , that description can be submitted as a query  230  against results datastore  235 , which returns results  240  which can then be displayed on a user device of the user that created or otherwise input search request  225 . 
     For example, assume the search request  225  includes the terms “matte chair single leg”. These terms can then be compared to the descriptions in the descriptions datastore  220  to determine that “one-legged chair with matte finish” (generated from the property and value combination: A:i, B:i) most closely matches the terms of search request  225 . Continuing the example, the description “one-legged chair with matte finish” is then submitted as the query  230  against the results datastore  235 . The results  240  can include a webpage displaying a plurality of chairs options having matching properties that are browsable via a website which received the search request. In this way, the description search system  150  can decouple the search request  225  (the terms input by the user) from the query (the terms used to query a datastore). 
     Additionally, in some example embodiments, one or more of the descriptions can be pre-linked directly to one or more search results, as indicated by the dotted arrow from query  230  to results  240 . For example, if the results are encyclopedia articles or medical records, upon a user selecting one of the descriptions from the descriptions datastore  220  the selected description is treated as query  230  and pre-linked results are displayed to the user without submitting the query to a datastore (e.g., querying a results datastore  235 ); or without using a search engine to determine which results are closest, instead, the pre-linked results are returned as results. Further detail example embodiments using pre-linked results are discussed below. 
     One advantage of the description search system  150  is that it enables the user to browse actual results of items in the database (since the descriptions are created from metadata of items in the database) instead of merely searching for them. For example, in conventional approaches, a user types in words without knowing what sort of items are available in the results datastore. In contrast, when “browsing” via the description search system  150 , the user is choosing from what will clearly bring back appropriate answers from the results datastore. This can be advantageous in embodiments where perusing the closest matching descriptions can give insight into what the user is searching for (e.g., a doctor searching for the correct diagnosis among a plurality of possible diagnosis options). 
     Conventional search engines generally use searching, not browsing; the results that they find are stored as a big “bag of words”, and the conventional engines attempt to return the best matching “bags” to the query sent. This approach is incongruent with online sites that store items of inventory, e.g., an online Fine Art store, as the online sites would prefer to let the user browse their inventory (as the user would if in a physical store) instead of merely submitting a query and “seeing what comes back”, so to speak. Furthermore, site owners may not want to show an item as a result just because the item title or description happen to match a well-known category. For instance, the famous “this is not a pipe” painting by Marcel Duchamp is a painting, and it should not be shown for people who are simply searching for “pipe” (e.g., a tobacco pipe for smoking), merely because the terms are similar. However, removing a specific item from some search results while making it available for others is a difficult online search engine problem. The description search system  150  avoids this issue by generating descriptions from items in the results datastore (e.g., generating descriptions from combinations of item metadata of items in an inventory datastore). 
     An additional advantage of the description search system  150  is that by decoupling search requests terms from results computationally expensive free-text searches are avoided. This is advantageous because often the results datastore to be searched can be a very large dataset that require tiers of servers to handle search requests; and if all user search requests are treated as queries, then each search request creates a free-text search against the very large datastore, which creates significant computational overhead, large administrative and equipment costs, and further can yield poor end-user experiences (e.g., slow results). In contrast, the description search system  150  uses the user&#39;s search request against a description datastore (e.g., descriptions datastore  220 ), which is more light weight and can be accessed and searched far more rapidly than the items stored in the large datastore (e.g., results datastore  235 ). 
     Some conventional approaches attempt to resolve the problem of expensive superfluous free-text searches using auto-complete suggestions that are based on popular historic searches and/or boosted common user queries. However, these approaches are vulnerable to abuse by malicious network users. For example, a group of malicious users may try to high-jack auto-complete systems by submitting fake searches so that the auto-complete system erroneously suggests the fake searches instead of previous real user searches. For instance, a group of malicious users may submit the sentence: “John Smith is a liar”, so that when other users input the word “John . . . ” auto-complete erroneously suggests “John Smith is a liar” as a genuine popular search request. The description search system  150  avoids this network issue by generating combinations of descriptions from item metadata (e.g., properties of item classes and their underlying values), which are more concrete and difficult for malicious users to manipulate. 
     Further, by avoiding reliance on past data (e.g., past user searches, common queries, etc.), the description search system  150  can more readily be adapted to new environments. For example, if a website enters a new online marketplace in a different country having a different language, the description search system  150  does not need to gather past data from users in the different country (e.g., in that country&#39;s language) to enable auto-complete suggestions. For example, conventionally, if a system configured for English autocomplete (based on English past user data) is migrated to Germany, the system will have need to gather a multitude of past user data in the German language to provide useful autocomplete suggestions in the German speaking searching users. The description search system  150  avoids this by translating description set from English to Spanish term-for-term, or by generating a description set from scratch in German, both of which can be performed more rapidly than the conventional past user data-based approaches. 
       FIG. 3  shows example internal functional components of a description search system  150 , according to some example embodiments. As illustrated, the description search system  150  comprises an interface engine  300 , a description generator  305 , a request engine  310 , a results engine  315 , and a language engine  320 . The interface engine  300  is configured to generate user interfaces to interact with users (e.g., a search UI to receive search requests from users and display returned results). The description generator  305  is configured to identify item metadata (e.g., properties and their underlying values) of different items and generate descriptions from different combinations of item metadata. The request engine  310  is configured to receive a search request from users and identify one or more descriptions that most closely match the terms in the search request. The results engine  315  manages returning results using the one or more descriptions identified by the request engine  310 . For instance, in some example embodiments, the result engine  315  is configured to query a database using the matching one or more descriptions as identified by the request engine  310 . In some example embodiments, the results engine  315  includes a search engine (E.g., inverted index, bag of words scheme, semantics based search engine, a search engine plugin, a recursive neural network based predictive search engine that updates per each word input the user, etc.) that returns results by searching against a results data store using one or more of the variation descriptions that match user&#39;s search request. Further, in some example embodiments, the results engine  315  uses the one or more identified descriptions to return pre-linked results (e.g., without searching), as discussed in further detail below. The language engine  320  is configured to generate description sets in different languages from initial description set that is in an initial language, as discussed in further detail below. 
       FIG. 4  shows a flow diagram of an example method  400  for generating description sets for different item classes, according to some example embodiments. At operation  405 , the description generator  305  identifies an item class (e.g., a chair item class). At operation  410 , the description generator  305  identifies item metadata of the item class, such as properties and their underlying values of the identified item class. At operation  415 , the description generator  305  generates an item class description set from the item metadata. For example, at operation  415 , the description generator  305  generates different descriptions from different combinations of the properties and property values of the item class. The descriptions can be generated using different mechanisms. For example, according to some example embodiments, the descriptions are created by generating every combination of properties and underlying values (e.g., one property and its underlying values, two properties and their respective underlying value combinations, etc.). Additionally, in some example embodiments in which the order of terms in the descriptions is important, the descriptions are created by generating every permutation of properties and underlying values (e.g., “black chair” would be a different description than “chair black”). In some example embodiments, each of the descriptions may be manually curated through a user interface. For example, a user may be presented with a user interface that displays various property and value options for a given item, and the user may rapidly generate different descriptions by explicitly selecting individual properties or values to be included in a given description. In some example embodiments, instead of generating descriptions by exhausting all possible combinations/permutations, the descriptions are generated using only pre-selected values. The pre-selection approach may be useful in the case where some properties are more important than others for a given item class. For example, if the item is a screwdriver, the property of “screwdriver type” may be important (with possible underlying values of “Philips head”, “flathead”, “Torx” ®, etc.) whereas the color property may not be important (e.g., to a user, the difference between a black or red colored screwdriver may be inconsequential). In this example, the screwdriver type property may be selected to be included in description generation and the color property may be left unselected, thereby creating more useful descriptions. Further, in some example embodiments, the descriptions may be augmented by additional language and templates, as discussed in further detail below. 
     At operation  420 , the description generator  305  modifies the item class description set, as discussed in further detail below with reference to  FIG. 5 . At operation  425 , the description generator  305  stores the generated item class description set in a description datastore (e.g., descriptions datastore  220 ). At operation  430 , the description generator  305  determines whether there are additional item classes for which to generate additional description sets. If there are additional item classes, the description generator  305  loops through operations  405  through  425  for each of the additional item classes. If there are no additional item classes, the method  400  ends or returns. 
     One feature of the method  400  is that it allows for new items to be rapidly added to the description-based search system. For example, when a new item of inventory is received, item metadata can be generated for the new item, then descriptions can be generated using different combinations, then the new set can be stored with the description datastore. 
       FIG. 5  shows a flow diagram of a method  500  for modifying the generated description set, according to some example embodiments. In some example embodiments, the method  500  can be configured as a subroutine of operation  420  in  FIG. 4 . At operation  505 , the description generator  305  removes one or more descriptions. For example, in the case where the descriptions are generated by exhausting all combinations of properties and underlying values, an administrative user may instruct description generator  305  (e.g., a UI generated by the description generator  305 ) to remove descriptions that do not make sense or are irrelevant. Additionally, one or more descriptions may be removed based on other considerations, such as low or no inventory or seasonality (e.g., removing “sun umbrella” descriptions during winter). In some example embodiments, a set of removal rules (e.g., trigger conditions, if/then statements, heuristics) are stored in memory accessible to the description generator, and at operation  505  the description generator  305  uses the stored rules to cull the description set. The stored rules can be preconfigured before method  500  and can be updated when new types of errors are encountered (e.g., a administrative user noticing the descriptions are inaccurate, such as a seasonal/weather inaccuracy, and creates a new rule to include in the stored rules of the description generator). 
     At operation  510 , the description generator  305  weights descriptions. For example, the description generator  305  can boost the weighting of one or more descriptions so that they are more likely to be selected as matching the terms in the user search request. For instance, a new inventory item (e.g., item class) can be promoted by boosting the weightings of descriptions generated for the new item inventory item versus other description sets from other items. Alternatively, if one or more items are low in inventory, the weightings of their corresponding descriptions can be lowered thereby guiding users towards browsing of items (i.e., item descriptions) that have adequate inventory. In contrast to past approaches which use search engines to find whichever results are closest to the search terms, the method  500  can more efficiently be managed as the set of descriptions is a closed set of conditions that are low in quantity due to the combinations of the item metadata resulting in a manageable set of combinations or permutations. In this way, whereas administrators implementing conventional approaches expend considerable resources to manage heuristics and weightings for open ended search system, an administrator managing the description search system  150  can efficiently change how the closed set of descriptions search via method  500 . 
       FIG. 6  shows a flow diagram of a method  600  for returning results for a search request, according to some example embodiments. At operation  605 , the request engine  310  identifies a search request. For example, a user may have submitted one or more terms as a search request input into a search bar of a user interface generated by the interface engine  300 . At operation  610 , the request engine  310  matches the received search request to one or more descriptions in a descriptions datastore (e.g., descriptions datastore  220  comprising multiple sets of description sets, each of which are generated for a different item or item class). 
     At operation  613 , one or more matching descriptions are displayed to the user as suggestions (e.g., non past-data based auto-complete suggestions). In some example embodiments, operation  613  is omitted and the top matching description is selected. At operation  615 , the results engine  315  submits the one or more matching descriptions as a query to a database. In some example embodiments, the description that most matches is automatically submitted as the query for submission to the database. To the searching user, it will appear as if their actual search terms were submitted to a search engine or database, whereas behind the scenes, their search term was matched to a top ranking description, and the top ranking description was submitted to the search engine or database. 
     At operation  620 , the interface engine  300  displays the query results as results the search request received at operation  605 . Further, in some example embodiments, one or more of the descriptions can be pre-linked to a certain search result or set of search results. For example, inputting “Alan Parker Fame 1980” may display a plurality of description based auto-complete suggestions, including some descriptions generated for the Alan Parker movie “Fame” and other descriptions that are not about the Alan Parker “Fame” but nonetheless have terms that match the search request (e.g., another Alan Parker movie made circa 1980). In this example embodiments, if the user selects any of the descriptions generated from the “Fame” item description set (i.e., the description set generated from the “Fame” item), the user navigates to the same page: a web-article for the 1980 movie “Fame”. That is, the web-article has been pre-linked to all of the generated descriptions that mention the term “Fame”, and upon any of the descriptions comprising that term are selected, no search occurs and instead the web-article for the 1980 movie “Fame” is rapidly returned and displayed on the user&#39;s device. 
       FIG. 7  shows a flow diagram of a method  700  for generating description sets in different languages using an initial description set, according to some example embodiments. At operation  705 , the language engine  320  identifies a description set in an initial language, such as English. At operation  710 , the language engine  320  generates a translated description set in a different language, such as Spanish. For example, at operation  710  the language engine  320  generates a translated description set by converting the English terms in the initial description set (which are English) to Spanish terms. In some example embodiments, the translation occurs word-for-word for each description. In other embodiments, a translation program is used to translate descriptions as entire sentences. The translation program can be an off the self-program that is integrated as a plugin into the language engine  320 . In this way, by translating descriptions as entire sentences, the translated description set can be more accurately generated for languages that change the order of sentence parts (e.g., blue pants in English will translate to pantalones azules in Spanish). 
     At operation  715 , the interface engine  300  receives a search request in the different language. For example, a Spanish user enters a search request in Spanish in a search bar of a website. At operation  720 , the request engine  310  identifies a match description in the translated description set by determining which of the descriptions in the translated set best match the terms in the received search request. At operation  725 , the results engine  315  queries a database using the matching description (e.g., a relational database query, a open-ended search engine search request). At operation  730 , the interface engine  300  displays query results. For example, at operation  730  the interface engine  300  displays query results as web-page product items, where the descriptions of the product items and their corresponding web-pages are all the same language as the search request received at operation  715 . 
     In some example embodiments, the description search system  150  creates description sets for each language from scratch. That is, instead of converting the English description set to a German description set using language engine  320 , it may be faster or more practical for the description search system  150  to generate properties and underlying values in German and generate combinations of the properties and values to create different descriptions, as discussed above with reference to  FIG. 4 . 
     Although in some of the examples above the description search system  150  is configured to perform description-based searches on items of a network site, it is appreciated that the description search system  150  can likewise be implemented in different types of environments. In some example embodiments, the description search system  150  is configured to perform article searching, e.g., encyclopedia searches where the searched items is a closed set of results that are rarely updated (e.g., yearly). In the article searching context, an item class (e.g., item  205 ,  FIG. 2 ) corresponds to a subject of an article. For example, a first example item can be Carl Philipp Emanuel Bach (son of Johann Sebastian Bach), a second item can be “Fame” (a movie made in 1980). Further, in this example, the properties of the item/subjects can be any attribute, fact, or term that describes the subject. For the first item/subject of “Carl Philipp Emanuel Bach”, the properties can be manually curated by an administrative user and include: “CPE Bach” (a common abbreviation the subject&#39;s name), “Son of Bach”, “1714” (CPE Bach&#39;s date of birth), “classical-era composer”, and so on, from which descriptions including “CPE Bach, son of JS Bach” CPE Bach, classical-era composer, CPE Bach, born 1714, CPE Bach, composer of Adagio for string orchestra in B minor” can be generated and stored in the description set (e.g., descriptions datastore  220 ). The second item properties for the movie fame can include: “1980”, “Alan Parker”, “teen musical”, “drama”, and so on, from which descriptions including “Fame 1980 film”, “Alan Parker movie Fame”, and “Fame, teen musical drama film” can be generated and store with the description datastore (e.g., descriptions datastore  220 ). 
     In some example embodiments, the descriptions created from different combinations of properties and values can be augmented by additional area terms (e.g., jargon, terms of art specific to a knowledge area or study) that provides additional insight and context and improves readability of the descriptions. The area terms can be included in templates into which different combinations of properties and values can be inserted. For example, in a health/medical based description terms-of-art embodiment, each item (e.g., item  205 ) can be aspects related to diagnosis of a patient. For example, a “drug” item can include the following properties: conditions for which it is treated, cross-drug usage for treatments, cross-drug interferences, side effects, drug sensitivities, and so on, each of which may have one or more underlying values. An example “treatment” item can include the following properties: conditions for which it is treated, drug treatment associated with it, side-effect conditions of treatment, and so on. Further, an example “medical condition” item can include the following properties: preliminary, intermediate and advanced symptoms; preliminary, intermediate, and advanced medical treatments and drugs; and ethnic, sexual orientation, age range, and other attributes associated with the condition; and so on. 
     The templates can include terms that explain the causal or observed relations between treatments, drugs and conditions. Example additional language templates can include:
         [symptom] in [population group] typical of [medical condition]   [drug] treatment for [medical condition] in [population group] showing [symptoms]   [side effect 1], [side effect 2], [ . . . ] after taking [drug]   [medical condition] treated using [drug]
 
where the terms in brackets are properties into which values can be populated and the words outside the brackets are the additional language of the template.
       

     Example descriptions having values integrated with the additional language can include:
         “Delayed motoric skills in young infants among Jewish due to Tay Sachs disease”   “Ibuprofen 400 treatment for Migraine without Headache in middle-aged female”   “Abdomen rash following use of Amoxicillin”   “Rosacea treated using Doxycycline”       

     In this example embodiment, at the time of diagnosis (e.g., in office patient visit), the healthcare expert (e.g., General Practitioner doctor) can input the keywords they believe are relevant to the patient&#39;s case, such as the symptoms, drug names, past or current diseases, and the system matches the keywords to the most relevant suggested descriptions. By reviewing the suggested descriptions and variations of cases, they can rapidly narrow down to the most relevant description (e.g., diagnosis), while interacting with the patient in-office to further explore the condition (such as asking about past diseases, ethnic correlation, based on the generated descriptions). In some example embodiments, the one or more of the descriptions are pre-linked to a certain symptom (e.g., article page describing the symptoms), while in other embodiments a selected description can then be used as a query against a database, as discussed above with reference to  FIG. 2 . 
     The potential benefit of pre-linking descriptions with results is that the administrator of the description search system  150  may seek to have the browse experience provided by the descriptions but not risk the chance of irrelevant results being retrieved in the query process. In the medical context, administrative users having medical expertise may know beforehand that certain descriptions should link to certain pages and may create such linkages manually, thereby avoiding false diagnosis from generated descriptions. 
     The use of description variation, as opposed to simply submitting the keywords into a search engine and hoping for some articles to match, is that the sentence structure is in a concise and meaningful form, allowing no room for misinterpretation. For example, plain text search for “rash doxycycline” will retrieve a mix of articles, where Doxycycline is used to either cure a rash or happens to cause a rash. The results may be very large and include many repeats. When observed from a description generation approach, the descriptions inherently address the case where a medical treatment using Doxycycline caused certain side-effect, versus the case where Doxycycline was used to treat Rosacea-type facial rash. By choosing a result from a list, the expert can access a User Interface (e.g., search user interface with a text field bar for term entry), that shows additional options relating to the drug, treatment or condition, allowing further research after a primary filter is done. It is therefore a quick and meaningful method for honing into a medical facts database, which can be immense and difficult for even well-trained professional to navigate efficiently. 
       FIG. 8  shows an example user interface  800  for description based searches, according to some example embodiments. The user interface  800  is displayed on a screen of a user device (e.g., client device displaying a mobile application screen, a web-browser displaying a website with different products or encyclopedia articles, a movie website ratings website, a medical search system, etc.). The user interface  800  comprises a search element  805  into which a search user inputs one or more words of a search request. The search request is used to generate matching descriptions displayed in the descriptions element  810 . For example, the descriptions element  810  can be a drop-down element that extends from the search element  805  to display descriptions-based auto-complete results (non-past user data based auto-complete results). In some example embodiments, the descriptions element  810  is not displayed and upon pressing the “Enter” or search button of the device, the input terms are matched to descriptions and the top ranking description or a set of the top ranking descriptions (e.g., top n descriptions) are used as search engine queries. 
     The user interface  800  further comprises a results area  815  in which a plurality of results 1-4 are displayed. In some example embodiments, the results are identified by using a description as a search term in a search engine, while in other example embodiments the results are identified via pre-linking to one or more of the descriptions, as discussed above. 
       FIG. 9  is a block diagram  900  illustrating an architecture of software  902 , which can be installed on any one or more of the devices described above.  FIG. 9  is merely a non-limiting example of a software architecture, and it will be appreciated that many other architectures can be implemented to facilitate the functionality described herein. In various embodiments, the software  902  is implemented by hardware such as a machine  1000  of  FIG. 10  that includes processors  1010 , memory  1030 , and I/O components  1050 . In this example architecture, the software  902  can be conceptualized as a stack of layers where each layer may provide a particular functionality. For example, the software  902  includes layers such as an operating system  904 , libraries  906 , frameworks  908 , and applications  910 . Operationally, the applications  910  invoke application programming interface (API) calls  912  through the software stack and receive messages  914  in response to the API calls  912 , consistent with some embodiments. 
     In various implementations, the operating system  904  manages hardware resources and provides common services. The operating system  904  includes, for example, a kernel  920 , services  922 , and drivers  924 . The kernel  920  acts as an abstraction layer between the hardware and the other software layers, consistent with some embodiments. For example, the kernel  920  provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionality. The services  922  can provide other common services for the other software layers. The drivers  924  are responsible for controlling or interfacing with the underlying hardware, according to some embodiments. For instance, the drivers  924  can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), WI-FI® drivers, audio drivers, power management drivers, and so forth. 
     In some embodiments, the libraries  906  provide a low-level common infrastructure utilized by the applications  910 . The libraries  906  can include system libraries  930  (e.g., C standard library) that can provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries  906  can include API libraries  932  such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic content on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries  906  can also include a wide variety of other libraries  934  to provide many other APIs to the applications  910 . 
     The frameworks  908  provide a high-level common infrastructure that can be utilized by the applications  910 , according to some embodiments. For example, the frameworks  908  provide various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks  908  can provide a broad spectrum of other APIs that can be utilized by the applications  910 , some of which may be specific to a particular operating system or platform. 
     In an example embodiment, the applications  910  include a home application  950 , a contacts application  952 , a browser application  954 , a book reader application  956 , a location application  958 , a media application  960 , a messaging application  962 , a game application  964 , and a broad assortment of other applications such as a third-party application  966 . According to some embodiments, the applications  910  are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications  910 , structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application  966  (e.g., an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party application  966  can invoke the API calls  912  provided by the operating system  904  to facilitate functionality described herein. 
       FIG. 10  illustrates a diagrammatic representation of a machine  1000  in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example embodiment. Specifically,  FIG. 10  shows a diagrammatic representation of the machine  1000  in the example form of a computer system, within which instructions  1016  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  1000  to perform any one or more of the methodologies discussed herein may be executed. The instructions  1016  transform the general, non-programmed machine  1000  into a particular machine  1000  programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine  1000  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  1000  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine  1000  may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a PDA, an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  1016 , sequentially or otherwise, that specify actions to be taken by the machine  1000 . Further, while only a single machine  1000  is illustrated, the term “machine” shall also be taken to include a collection of machines  1000  that individually or jointly execute the instructions  1016  to perform any one or more of the methodologies discussed herein. 
     The machine  1000  may include processors  1010 , memory  1030 , and I/O components  1050 , which may be configured to communicate with each other such as via a bus  1002 . In an example embodiment, the processors  1010  (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor  1012  and a processor  1014  that may execute the instructions  1016 . The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although  FIG. 10  shows multiple processors  1010 , the machine  1000  may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof. 
     The memory  1030  may include a main memory  1032 , a static memory  1034 , and a storage unit  1036 , both accessible to the processors  1010  such as via the bus  1002 . The main memory  1030 , the static memory  1034 , and storage unit  1036  store the instructions  1016  embodying any one or more of the methodologies or functions described herein. The instructions  1016  may also reside, completely or partially, within the main memory  1032 , within the static memory  1034 , within the storage unit  1036 , within at least one of the processors  1010  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  1000 . 
     The I/O components  1050  may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components  1050  that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components  1050  may include many other components that are not shown in  FIG. 10 . The I/O components  1050  are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. In various example embodiments, the I/O components  1050  may include output components  1052  and input components  1054 . The output components  1052  may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components  1054  may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. 
     In further example embodiments, the I/O components  1050  may include biometric components  1056 , motion components  1058 , environmental components  1060 , or position components  1062 , among a wide array of other components. For example, the biometric components  1056  may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components  1058  may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components  1060  may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components  1062  may include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like. 
     Communication may be implemented using a wide variety of technologies. The I/O components  1050  may include communication components  1064  operable to couple the machine  1000  to a network  1080  or devices  1070  via a coupling  1082  and a coupling  1072 , respectively. For example, the communication components  1064  may include a network interface component or another suitable device to interface with the network  1080 . In further examples, the communication components  1064  may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fit components, and other communication components to provide communication via other modalities. The devices  1070  may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB). 
     Moreover, the communication components  1064  may detect identifiers or include components operable to detect identifiers. For example, the communication components  1064  may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components  1064 , such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth. 
     The various memories (i.e.,  1030 ,  1032 ,  1034 , and/or memory of the processor(s)  1010 ) and/or storage unit  1036  may store one or more sets of instructions and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions  1016 ), when executed by processor(s)  1010 , cause various operations to implement the disclosed embodiments. 
     As used herein, the terms “machine-storage medium,” “device-storage medium,” “computer-storage medium” mean the same thing and may be used interchangeably in this disclosure. The terms refer to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions and/or data. The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), FPGA, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below. 
     In various example embodiments, one or more portions of the network  1080  may be an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, the Internet, a portion of the Internet, a portion of the PSTN, a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network  1080  or a portion of the network  1080  may include a wireless or cellular network, and the coupling  1082  may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, the coupling  1082  may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long range protocols, or other data transfer technology. 
     The instructions  1016  may be transmitted or received over the network  1080  using a transmission medium via a network interface device (e.g., a network interface component included in the communication components  1064 ) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions  1016  may be transmitted or received using a transmission medium via the coupling  1072  (e.g., a peer-to-peer coupling) to the devices  1070 . The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure. The terms “transmission medium” and “signal medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions  1016  for execution by the machine  1000 , and includes digital or analog communications signals or other intangible media to facilitate communication of such software. Hence, the terms “transmission medium” and “signal medium” shall be taken to include any form of modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal. 
     The terms “machine-readable medium,” “computer-readable medium” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals.