the following detailed description of the invention refers to the accompanying drawings . the same reference numbers in different drawings may identify the same or similar elements . also , the following detailed description does not limit the invention . a “ document ,” as the term is used herein , is to be broadly interpreted to include any machine - readable and machine - storable work product . a document may include an e - mail , a web site , a file , one or more digital images , a combination of files , one or more files with embedded links to other files , a news group posting , a blog , a web advertisement , etc . in the context of the internet , a common document is a web page . web pages often include textual information and may include embedded information ( such as meta information , images , hyperlinks , etc .) and / or embedded instructions ( such as javascript , etc .). a “ link ” as the term is used here , is to be broadly interpreted to include any reference to or from a document . fig1 is an exemplary diagram of a network 100 in which systems and methods consistent with the principles of the invention may be implemented . network 100 may include multiple clients 110 connected to multiple servers 120 and 115 via a network 130 . network 130 may include a local area network ( lan ), a wide area network ( wan ), a telephone network , such as the public switched telephone network ( pstn ), an intranet , the internet , a memory device , or a combination of networks . two clients 110 and two servers 120 and 115 have been illustrated as connected to network 130 for simplicity . in practice , there may be more or fewer clients and servers . also , in some instances , a client may perform the functions of a server and a server may perform the functions of a client . clients 110 may include client entities . an entity may be defined as a device , such as a wireless telephone , a personal computer , a personal digital assistant ( pda ), a laptop , or another type of computation or communication device , a thread or process running on one of these devices , and / or an object executable by one of these devices . servers 120 and 115 may include server entities that access , fetch , aggregate , process , search , and / or maintain documents in a manner consistent with the principles of the invention . clients 110 and servers 120 and 115 may connect to network 130 via wired , wireless , and / or optical connections . in an implementation consistent with the principles of the invention , server 120 may include a search engine 125 usable by users at clients 110 . server 120 may implement a data aggregation service by crawling a corpus of documents ( e . g ., web pages ) hosted on data server ( s ) 115 and store information associated with these documents in a repository of crawled documents . the data aggregation service may be implemented in other ways , such as by agreement with the operator ( s ) of data server ( s ) 115 to distribute their hosted documents via the data aggregation service . search engine 125 may execute a query , received from a user , on the corpus of documents hosted on data server ( s ) 115 . server 120 may also host various games , as described below with respect to fig3 - 10 , to gather data for various purposes , such as , for example , search - related functions . server ( s ) 115 may store or maintain documents that may be crawled by server 120 . such documents may include data related to published news stories , products , images , user groups , geographic areas , or any other type of data . for example , server ( s ) 115 may store or maintain news stories from any type of news source , such as , for example , the washington post , the new york times , time magazine , or newsweek . as another example , server ( s ) 115 may store or maintain data related to specific product data , such as product data provided by one or more product manufacturers . as yet another example , server ( s ) 115 may store or maintain data related to other types of web documents , such as pages of web sites . while servers 120 and 115 are shown as separate entities , it may be possible for one or more of servers 120 and 115 to perform one or more of the functions of another one or more of servers 120 and 115 . for example , it may be possible that two or more of servers 120 and 115 are implemented as a single server . it may also be possible for a single one of servers 120 or 115 to be implemented as two or more separate ( and possibly distributed ) devices . fig2 is an exemplary diagram of a client or server entity ( hereinafter called “ client / server entity ”), which may correspond to one or more of clients 110 and servers 120 and 115 , according to an implementation consistent with the principles of the invention . the client / server entity may include a bus 210 , a processing unit 220 , an optional main memory 230 , a read only memory ( rom ) 240 , a storage device 250 , an input device 260 , an output device 270 , and a communication interface 280 . bus 210 may include a path that permits communication among the components of the client / server entity . processing unit 220 may include any type of software , firmware or hardware implemented processing device , such as a microprocessor , a field programmable gate array ( fpga ), combinational logic , etc . main memory 230 may include a random access memory ( ram ) or another type of dynamic storage device that stores information and instructions for execution by processing unit 220 , if processing unit 220 includes a microprocessor . rom 240 may include a conventional rom device or another type of static storage device that stores static information and / or instructions for use by processing unit 220 . storage device 250 may include a magnetic and / or optical recording medium and its corresponding drive . input device 260 may include a conventional mechanism that permits an operator to input information to the client / server entity , such as a keyboard , a mouse , a pen , voice recognition and / or other biometric mechanisms , etc . output device 270 may include a conventional mechanism that outputs information to the operator , including a display , a printer , a speaker , etc . communication interface 280 may include any transceiver - like mechanism that enables the client / server entity to communicate with other devices and / or systems . for example , communication interface 280 may include mechanisms for communicating with another device or system via a network , such as network 130 . as will be described in detail below , the client / server entity , consistent with the principles of the invention , may perform certain data processing operations . the client / server entity may , in some implementations , perform these operations in response to processing unit 220 executing software instructions contained in a computer - readable medium , such as memory 230 . a computer - readable medium may be defined as one or more physical or logical memory devices and / or carrier waves . the software instructions may be read into memory 230 from another computer - readable medium , such as data storage device 250 , or from another device via communication interface 280 . the software instructions contained in memory 230 may cause processing unit 220 to perform processes that will be described later . alternatively , hardwired circuitry may be used in place of , or in combination with , software instructions to implement processes consistent with the principles of the invention . thus , implementations consistent with principles of the invention are not limited to any specific combination of hardware circuitry and software . fig3 illustrates an overview of a game that may be used to gather data to support question - type search queries . a log 305 of question - type search queries may be accumulated from multiple search queries of the form “ what is xxx ?,” “ who is xxx ?,” “ where is xxx ?,” or other types of question formats , received at search engine 125 . the question - type queries may be accumulated from one or more users over a period of time . from the question - type query log 305 , queries having known answers 310 may be identified , and queries having unknown answers 315 may be identified . for example , the question - type query “ where is akron ?” may have the known answer “ ohio .” however , the question - type query “ who is joe smith ?” may have an unknown answer . in some implementations , queries having known answers 310 may be retrieved from any query source 370 , and need not be retrieved only from query log 305 . the queries having known answers 310 and queries with unknown answers 315 may be issued to a participant 320 and a participant 325 from server 120 . participant 320 may provide , to server 120 , his answers 330 for the queries with known answers 310 , and his answers 335 for the queries with unknown answers 315 . a score may be assigned 350 , by server 120 , to participant 320 based on the participant &# 39 ; s own answers 330 to the queries having known answers . for example , a score may be assigned based on a number of the queries that participant 320 answered correctly . participant 325 may also provide , to server 120 , his answers 340 for the queries with known answers 310 , and his answers 345 for the queries with unknown answers 315 . a score may also be assigned 355 , by server 120 , to participant 325 based on the participant &# 39 ; s answers 340 to the queries having known answers . a score , for example , may be assigned based on a number of the queries that participant 325 answered correctly . participant 1 320 &# 39 ; s answers 335 to the queries with unknown answers 315 , and participant 2 325 &# 39 ; s answers 345 to the queries with unknown answers 315 , may then be aggregated 360 . the answers 335 and 345 , for example , may be aggregated in a database associated with server 120 . a correct answer for each question - type search query may then be determined from the participants &# 39 ; aggregated answers 360 . for example , an analysis of the answers 335 and 345 may determine that both participants ( or a majority of participants , if more than two participants are involved ) chose the same answer for a given question - type query and that , therefore , this answer is the correct answer for the query . fig4 a , 4 b and 4 c are flowcharts of an exemplary process , consistent with principles of the invention , for analyzing participants &# 39 ; answers to quiz questions to determine correct answers for at least some of the quiz questions . as one skilled in the art will appreciate , the process exemplified by fig4 a , 4 b and 4 c can be implemented in software and stored on a computer - readable memory , such as main memory 230 , rom 240 or storage device 250 of server 120 . in other implementations , the processing exemplified by fig4 a , 4 b and 4 c can be implemented in hardwired circuitry , such as combinational logic , within processing unit 220 of server 120 . the exemplary process may begin with the retrieval of question - type search queries from a query log ( block 405 ) ( fig4 a ). the query log may be accumulated from multiple search queries of the form “ what is xxx ?,” “ who is xxx ?,” “ where is xxx ?,” or other types of question formats , received at search engine 125 and stored in a database associated with server 120 . queries from the retrieved question - type search queries that have known answers may be identified ( block 410 ). for example , a portion of the logged question - type search queries may have previously known , correct answers . queries from the retrieved question - type search queries that do not have known answers may be identified ( block 415 ). for example , a portion of the logged question - type search queries may not have any known , correct answers associated with them . the determination of questions with known or unknown answers may be made based on an analysis of query logs and users &# 39 ; selections of search results . in some implementations , queries having known answers may be retrieved from any source , and need not be retrieved only from the query log . a number of question - type search queries , from the retrieved queries with known answers , may be issued to one or more participants ( block 420 ). for example , a set of search queries of the form “ who is x 1 ,” “ what is x 2 ,” and “ where is x 3 ” may be issued to the one or more participants , where the correct answers to these questions are all known . a number of question - type search queries , from the retrieved queries having unknown answers , may be issued to the one or more participants ( block 425 ) ( fig4 b ). for example , a set of search queries of the form “ where is x 4 ,” “ who is x 5 ,” and “ what is x 6 ” may be issued to the one or more participants , where the correct answers to these questions are not known . fig5 illustrates the issuance of question - type queries 505 to a participant via a game show document 510 . answers , for each issued question - type search query , may be received from the participant ( s ) ( block 430 ). as shown in fig5 , a participant may enter his answers in answer fields 515 in game show document 510 and submit them via , for example , an answer submission 520 “ button .” in another implementation , a document may present several candidate answers in a multiple choice format to a participant , and the participant may choose his answer from the several candidate answers . a determination may then be made , for each participant , how many of the question - type search queries having known answers that the participant answered correctly ( block 435 ). a score may be assigned to each participant based on the number of the question - type search queries , having known answers , that the participant answered correctly ( block 440 ). for example , if a given participant answered 90 of 100 question - type search queries having known answers correctly , then the participant may be assigned a score of 90 %. as shown in fig5 , a participant score document 525 may be presented to the participant indicating the participant &# 39 ; s assigned score . the answers received from the participant ( s ) may be aggregated ( block 445 ) ( fig4 c ) and stored in a memory , such as memory 230 of server 120 , or in a database associated with server 120 . a correct answer for each of the question - type search queries having unknown answers may be determined using the aggregated answers ( block 450 ). the answers from the participants , for the question - type search queries having unknown answers , may be analyzed to ascertain which answer has been given the most for a specific question - type query . the answer that has been given the most by participants for a specific query may be determined to be the correct answer . for example , if there have been 100 participants , and 10 of the participants selected the same answer , but everyone else selected different answers , then the answer picked by the 10 participants may not actually be the “ correct ” answer . a given answer may have to be selected a minimum number of times to be considered the “ correct ” answer . if each participant selected an answer from a multiple choice list of candidate answers , then the answer that was chosen by a majority of participants may be determined to be the correct answer for the question - type query . in one implementation , the answers given by participants that have been assigned a high score ( see block 440 above ) may be weighted more heavily when determining a correct answer for each of the question - type search queries . weighting high scoring participants more heavily when determining a correct answer for a search query may remove the activity of “ bots ” ( i . e ., computer programs that run automatically ), since “ bots ” would likely not answer the question - type queries correctly that have previously known answers . the determined “ correct ” answers to queries with previously unknown answers may be used by a search engine for answering subsequently received question - type search queries . fig6 illustrates an exemplary overview of the use of image labels for identifying the labels associated with an image as related terms to one another . multiple images ( e . g ., images 615 - 1 through 615 - 4 ) may be presented to multiple participants ( e . g ., participant 605 and participant 610 ). the images may include , for example , digital images in any digital image format ( e . g ., mpeg , jpeg , etc .). for each image 615 , each participant ( e . g ., participants 605 and 610 ) may supply a guess word ( e . g ., guess words 620 - 1 through 620 - 4 for participant 605 and guess words 625 - 1 through 625 - 4 for participant 610 ) that the participant believes describes the presented image . each guess word supplied by a participant may then be identified as an image label ( i . e ., guess word 1 _ 1 620 - 1 identified as label 1 _ 1 630 - 1 for image 1 615 - 1 ; guess word 2 _ 2 625 - 2 identified as label 2 _ 2 635 - 2 , etc .). the identified image labels may then also be identified as related terms . for example , as shown in fig6 , label 1 _ 1 and label 2 _ 1 , previously supplied as guess words from participant 605 and participant 610 for image 1 615 - 1 , may be identified as “ related terms .” the labels identified as “ related terms ” may , for example , represent synonyms of one another , or be otherwise related . if the participants to the image labeling game are playing the game in different countries , the identification of image labels as “ related terms ” may be used for language translation also . for example , if participants playing from france label the image of a car “ voiture ,” it can be inferred that “ voiture ” is french for “ car .” this technique may be particularly useful in translating languages that are more obscure than french . fig7 a and 7b are flowcharts of an exemplary process , according to an implementation consistent with the principles of the invention , for providing labels for images , and for identifying the image labels as related terms . as one skilled in the art will appreciate , the process exemplified by fig7 a and 7b can be implemented in software and stored on a computer - readable memory , such as main memory 230 , rom 240 or storage device 250 of server 120 . in other implementations , the processing exemplified by fig7 a and 7b can be implemented in hardwired circuitry , such as combinational logic , within processing unit 220 of server 120 . the exemplary process may begin with sending an image ( image_i ) to game participants ( block 705 ) ( fig7 a ). multiple participants may participate in each image labeling game . the image may be retrieved from memory and sent from server 120 to each participant at a client 110 . fig8 illustrates an exemplary image label guessing document 805 , that is sent to each game participant , and that includes an image 810 . a determination may be made whether a “ pass ” has been received from the participants ( block 710 ), meaning that the participant does not have a label for image 810 . a participant may indicate a “ pass ” via , for example , a “ pass ” button 815 provided in document 805 provided to the participant , as shown in fig8 . if a “ pass ” has been received from the participants , a determination may be made whether the image ( image_i ) is the last image ( block 715 ). each game may only include a given sequence of images , spanning a first image to a last image . if the image is not the last image , then a counter i , used to designate a particular image in an image sequence , may be incremented ( i = i + 1 ) ( block 720 ). if the image is the last image , then the exemplary process may continue at block 765 below . returning to block 710 , if a “ pass ” has not been received from the participants , then a guess may be received from each participant ( block 725 ). for example , each participant may type their next guess word in field 820 of document 805 , as shown in fig8 . a determination may then be made whether any of the guesses is a “ taboo ” word ( block 730 ). a “ taboo ” word may include a word that the game has excluded as an acceptable label for a given image . as shown in fig8 , document 805 may , for each image 810 , present a list 825 of “ taboo ” words to each participant . if any of the guesses from the participants of the game is a “ taboo ” word , then an error message may be sent to the participant who submitted the “ taboo ” word ( block 735 ), and the exemplary process may return to block 725 above . if none of the guesses are “ taboo ” words , then a determination may be made whether the guesses of the participants match ( block 740 ) ( fig7 b ). for example , if there are two participants to the game , then both participants may submit guess words that match . if the participants have not submitted matching guess words , then a determination may be made whether a time period has expired ( block 745 ). the time period may be configurable , and may represent an allotted amount of time that the participants to the game have to provide matching labels for a given image . if the time period has not expired , then the exemplary process may return to block 725 above . if the time period has expired , then the exemplary process may return to block 720 above with the sending of a new image ( image_i + 1 ) to the game participants . returning to block 740 , if the guesses of the participants match , then the matching guesses may be stored as a label for the image ( image_i ) sent to the participants ( block 750 ). a determination may be made whether the image is the last image ( block 755 ). if not , then the exemplary process may return to block 720 above . if the image is the last image , then , in one implementation , labels associated with each image , from multiple games , or possibly multiple , different participants , may be identified as related terms ( block 760 ). labels , associated with each image , may be aggregated from multiple different games and the labels may be identified as “ related .” the image labels may be identified , for example , as synonyms . the labels ( and their synonyms ) may be used by a search engine for , for example , supplying related search terms for a given input search term . in another implementation , if the image is the last image , then a given label from a participant in a first country in a first language may be identified as a language translation of a corresponding label from a participant in a second country in a second language ( block 765 ). for example , if one of the participants to the image labeling is playing the game in the united states in english , and labels an image of an automobile a “ car ,” and another participant is playing the game in france in french , and labels the image of the automobile “ voiture ,” it can be inferred that “ voiture ” is french for “ car .” “ voiture ,” thus , may be identified as the french translation of the english label “ car .” fig9 illustrates an exemplary overview of a game challenge , according to an exemplary aspect of the invention , in which a game participant is challenged with multiple tasks , and a portion of the participant &# 39 ; s responses are used to verify that the participant is a human participant , and another portion of the participant &# 39 ; s responses are used for purposes other than human verification . as shown in fig9 , a participant may be challenged with multiple tasks , with a portion 905 of the tasks being tasks that have known responses , and another portion 910 of the tasks being tasks that have unknown responses 910 . the tasks may include any type of task , such as labeling an image , as described above with respect to fig6 - 8 , or answering questions , as described above with respect to fig3 - 5 . the participant may provide a response 925 to each task 915 - 1 through 915 - x having known responses , and to each task 920 - 1 through 920 - z having unknown responses . the participant may be verified 930 as being human based on the responses 925 to each task having known responses . for example , if the task involves the participant providing answers to question - type queries , then the participant &# 39 ; s responses 925 to tasks 915 - 1 through 915 - x can be compared to the known response to those questions to verify whether the participant is human , or whether the participant may possibly be a “ bot ”. a “ bot ” may not respond correctly to the tasks to which the correct responses are known . the participant responses 925 to the tasks 920 - 1 through 920 - z having unknown responses may be used 935 for purposes other than human verification . for example , as described above with respect to fig3 - 5 , participant answers to question - type queries that have unknown answers may be used to determine a correct answer to the question - type queries . as another example , as described above with respect to fig6 - 8 , image guess words provided by game participants may used as related terms . fig1 is a flowchart of an exemplary process , according to an implementation consistent with the principles of the invention , for using participant responses to multiple tasks to verify whether the participant is a human . as one skilled in the art will appreciate , the process exemplified by fig1 can be implemented in software and stored on a computer - readable memory , such as main memory 230 , rom 240 or storage device 250 of server 120 . in other implementations , the processing exemplified by fig1 can be implemented in hardwired circuitry , such as combinational logic , within processing unit 220 of server 120 . the exemplary process may begin with challenging a game participant with multiple tasks ( block 1005 ). server 120 may send the multiple tasks to the game participant at a client 110 via network 130 . the multiple tasks may include any type of task , such as issuing question - type queries to a participant , or presenting an image for the participant to label . prior to the start of the game , correct responses to a first portion of the multiple tasks are identified , and correct responses to a second portion of the tasks are determined to be unknown . the game participant may then be verified as being human based on the participant &# 39 ; s responses to the first portion of tasks ( block 1010 ). the participant &# 39 ; s responses to the tasks having known responses may be compared with the correct responses to determine how many of the participant &# 39 ; s responses are incorrect . a high error rate may indicate that the participant is not human , and may , for example , be a “ bot .” responses identified as being potentially from a non - human source , such as a “ bot ” may be discarded . responses from the game participant to the second portion of tasks may then be used for other than human verification ( block 1015 ). for example , as described above with respect to fig3 - 5 , participant answers to question - type queries that have unknown answers may be used to determine a correct answer to the question - type queries . as another example , as described above with respect to fig6 - 8 , image guess words provided by game participants may used as related terms . the foregoing description of preferred embodiments of the present invention provides illustration and description , but is not intended to be exhaustive or to limit the invention to the precise form disclosed . modifications and variations are possible in light of the above teachings or may be acquired from practice of the invention . for example , while series of acts have been described with regard to fig4 a , 4 b , 4 c , 7 a , 7 b and 10 , the order of the acts may be modified in other implementations consistent with the principles of the invention . for the processes described with respect to fig4 a , 4 b , 4 c , 7 a , 7 b and 10 , answers or responses from game participants may be solicited from participants in many different ways than those described . in some implementations , answers or responses may be solicited using freeform input ( e . g ., if the correct answer / response is not known ), or they may be solicited using a multiple choice format ( i . e ., if a set of candidate answers / responses is known that is believed to include a correct answer / response ). additionally , participants may be incentivized to play the games described above with respect to fig4 a , 4 b , 4 c , 7 a , 7 b and 10 . game participants may be rewarded ( i . e ., monetarily or otherwise ) for participating in the games , and rewards may be used to leverage a significant number of participants to participate in the games . in some implementations , only a small subset of participants may be rewarded ( e . g ., a reward lottery ), but the participants may not now who will receive the reward until some time after participating in a given game . it will also be apparent to one of ordinary skill in the art that aspects of the invention , as described above , may be implemented in many different forms of software , firmware , and hardware in the implementations illustrated in the figures . the actual software code or specialized control hardware used to implement aspects consistent with the principles of the invention is not limiting of the present invention . thus , the operation and behavior of the aspects of the invention were described without reference to the specific software code — it being understood that one of ordinary skill in the art would be able to design software and control hardware to implement the aspects based on the description herein . no element , act , or instruction used in the present application should be construed as critical or essential to the invention unless explicitly described as such . also , as used herein , the article “ a ” is intended to include one or more items . where only one item is intended , the term “ one ” or similar language is used . further , the phrase “ based on ” is intended to mean “ based , at least in part , on ” unless explicitly stated otherwise .